Title: | Nonlinear Mixed Effects Models in Population PK/PD |
---|---|
Description: | Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the 'rxode2' package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>). |
Authors: | Matthew Fidler [aut, cre] , Yuan Xiong [ctb], Rik Schoemaker [ctb] , Justin Wilkins [ctb] , Wenping Wang [ctb], Mirjam Trame [ctb], Huijuan Xu [ctb], John Harrold [ctb], Bill Denney [ctb] , Theodoros Papathanasiou [ctb], Teun Post [ctb], Richard Hooijmaijers [ctb] |
Maintainer: | Matthew Fidler <[email protected]> |
License: | GPL (>= 3) |
Version: | 3.0.1.9000 |
Built: | 2024-12-21 05:57:50 UTC |
Source: | https://github.com/nlmixr2/nlmixr2 |
This returns a new fit object with CWRES attached
addCwres(fit, focei = TRUE, updateObject = TRUE, envir = parent.frame(1))
addCwres(fit, focei = TRUE, updateObject = TRUE, envir = parent.frame(1))
fit |
nlmixr2 fit without WRES/CWRES |
focei |
Boolean indicating if the focei objective function is added. If not the foce objective function is added. |
updateObject |
Boolean indicating if the original fit object should be updated. By default this is true. |
envir |
Environment that should be checked for object to update. By default this is the global environment. |
fit with CWRES
Matthew L. Fidler
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } f <- try(nlmixr2(one.cmt, theo_sd, "saem")) print(f) # even though you may have forgotten to add the cwres, you can add it to the data.frame: if (!inherits(f, "try-error")) { f <- try(addCwres(f)) print(f) } # Note this also adds the FOCEi objective function
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } f <- try(nlmixr2(one.cmt, theo_sd, "saem")) print(f) # even though you may have forgotten to add the cwres, you can add it to the data.frame: if (!inherits(f, "try-error")) { f <- try(addCwres(f)) print(f) } # Note this also adds the FOCEi objective function
NPDE calculation for nlmixr2
addNpde( object, updateObject = TRUE, table = tableControl(), ..., envir = parent.frame(1) )
addNpde( object, updateObject = TRUE, table = tableControl(), ..., envir = parent.frame(1) )
object |
nlmixr2 fit object |
updateObject |
Boolean indicating if original object should be updated. By default this is TRUE. |
table |
'tableControl()' list of options |
... |
Additional arguments passed to |
envir |
Environment that should be checked for object to update. By default this is the global environment. |
New nlmixr2 fit object
Matthew L. Fidler
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } f <- nlmixr2(one.cmt, theo_sd, "saem") # even though you may have forgotten to add the NPDE, you can add it to the data.frame: f <- addNpde(f)
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } f <- nlmixr2(one.cmt, theo_sd, "saem") # even though you may have forgotten to add the NPDE, you can add it to the data.frame: f <- addNpde(f)
Add table information to nlmixr2 fit object without tables
addTable( object, updateObject = FALSE, data = object$dataSav, thetaEtaParameters = object$foceiThetaEtaParameters, table = tableControl(), keep = NULL, drop = NULL, envir = parent.frame(1) )
addTable( object, updateObject = FALSE, data = object$dataSav, thetaEtaParameters = object$foceiThetaEtaParameters, table = tableControl(), keep = NULL, drop = NULL, envir = parent.frame(1) )
object |
nlmixr2 family of objects |
updateObject |
Update the object (default FALSE) |
data |
Saved data from |
thetaEtaParameters |
Internal theta/eta parameters |
table |
a 'tableControl()' list of options |
keep |
Character Vector of items to keep |
drop |
Character Vector of items to drop or NULL |
envir |
Environment to search for updating |
Fit with table information attached
Matthew Fidler
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } # run without tables step f <- nlmixr2(one.cmt, theo_sd, "saem", control=list(calcTables=FALSE)) print(f) # Now add the tables f <- addTable(f) print(f)
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } # run without tables step f <- nlmixr2(one.cmt, theo_sd, "saem", control=list(calcTables=FALSE)) print(f) # Now add the tables f <- addTable(f) print(f)
Control for bobyqa estimation method in nlmixr2
bobyqaControl( npt = NULL, rhobeg = NULL, rhoend = NULL, iprint = 0L, maxfun = 100000L, returnBobyqa = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
bobyqaControl( npt = NULL, rhobeg = NULL, rhoend = NULL, iprint = 0L, maxfun = 100000L, returnBobyqa = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
npt |
The number of points used to approximate the objective function via a quadratic approximation. The value of npt must be in the interval [n+2,(n+1)(n+2)/2] where n is the number of parameters in 'par'. Choices that exceed 2*n+1 are not recommended. If not defined, it will be set to min(n * 2, n+2). |
rhobeg |
'rhobeg' and 'rhoend' must be set to the initial and final values of a trust region radius, so both must be positive with '0 < rhoend < rhobeg'. Typically 'rhobeg' should be about one tenth of the greatest expected change to a variable. If the user does not provide a value, this will be set to 'min(0.95, 0.2 * max(abs(par)))'. Note also that smallest difference 'abs(upper-lower)' should be greater than or equal to 'rhobeg*2'. If this is not the case then 'rhobeg' will be adjusted. |
rhoend |
The smallest value of the trust region radius that is allowed. If not defined, then 1e-6 times the value set for 'rhobeg' will be used. |
iprint |
The value of 'iprint' should be set to an integer value in '0, 1, 2, 3, ...', which controls the amount of printing. Specifically, there is no output if 'iprint=0' and there is output only at the start and the return if 'iprint=1'. Otherwise, each new value of 'rho' is printed, with the best vector of variables so far and the corresponding value of the objective function. Further, each new value of the objective function with its variables are output if 'iprint=3'. If 'iprint > 3', the objective function value and corresponding variables are output every 'iprint' evaluations. Default value is '0'. |
maxfun |
The maximum allowed number of function evaluations. If this is exceeded, the method will terminate. |
returnBobyqa |
return the bobyqa output instead of the nlmixr2 fit |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
bobqya control structure
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="bobyqa") print(fit2) # you can also get the nlm output with fit2$bobyqa # The nlm control has been modified slightly to include # extra components and name the parameters
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="bobyqa") print(fit2) # you can also get the nlm output with fit2$bobyqa # The nlm control has been modified slightly to include # extra components and name the parameters
Produce delta objective function for boostrap
bootplot(x, ...)
bootplot(x, ...)
x |
fit object |
... |
Additional arguments passed to |
Fit traceplot or nothing.
Vipul Mann, Matthew L. Fidler
R Niebecker, MO Karlsson. (2013) Are datasets for NLME models large enough for a bootstrap to provide reliable parameter uncertainty distributions? PAGE 2013. https://www.page-meeting.org/?abstract=2899
Bootstrap input dataset and rerun the model to get confidence bounds and aggregated parameters
bootstrapFit( fit, nboot = 200, nSampIndiv, stratVar, stdErrType = c("perc", "sd", "se"), ci = 0.95, pvalues = NULL, restart = FALSE, plotHist = FALSE, fitName = as.character(substitute(fit)) )
bootstrapFit( fit, nboot = 200, nSampIndiv, stratVar, stdErrType = c("perc", "sd", "se"), ci = 0.95, pvalues = NULL, restart = FALSE, plotHist = FALSE, fitName = as.character(substitute(fit)) )
fit |
the nlmixr2 fit object |
nboot |
an integer giving the number of bootstrapped models to be fit; default value is 200 |
nSampIndiv |
an integer specifying the number of samples in each bootstrapped sample; default is the number of unique subjects in the original dataset |
stratVar |
Variable in the original dataset to stratify on; This is useful to distinguish between sparse and full sampling and other features you may wish to keep distinct in your bootstrap |
stdErrType |
This gives the standard error type for the
updated standard errors; The current possibilities are: |
ci |
Confidence interval level to calculate. Default is 0.95 for a 95 percent confidence interval |
pvalues |
a vector of pvalues indicating the probability of
each subject to get selected; default value is |
restart |
A boolean to try to restart an interrupted or
incomplete boostrap. By default this is |
plotHist |
A boolean indicating if a histogram plot to assess
how well the bootstrap is doing. By default this is turned off
( |
fitName |
is the fit name that is used for the name of the boostrap files. By default it is the fit provided though it could be something else. |
Nothing, called for the side effects; The original fit is updated with the bootstrap confidence bands
Vipul Mann, Matthew Fidler
## Not run: one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- 1; label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2(one.cmt, nlmixr2data::theo_sd, est = "saem", control = list(print = 0)) withr::with_tempdir({ # Run example in temp dir bootstrapFit(fit, nboot = 5, restart = TRUE) # overwrites any of the existing data or model files bootstrapFit(fit, nboot = 7) # resumes fitting using the stored data and model files # Note this resumes because the total number of bootstrap samples is not 10 bootstrapFit(fit, nboot=10) # Note the boostrap standard error and variance/covariance matrix is retained. # If you wish to switch back you can change the covariance matrix by nlmixr2est::setCov(fit, "linFim") # And change it back again nlmixr2est::setCov(fit, "boot10") # This change will affect any simulations with uncertainty in their parameters # You may also do a chi-square diagnostic plot check for the bootstrap with bootplot(fit) }) ## End(Not run)
## Not run: one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- 1; label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2(one.cmt, nlmixr2data::theo_sd, est = "saem", control = list(print = 0)) withr::with_tempdir({ # Run example in temp dir bootstrapFit(fit, nboot = 5, restart = TRUE) # overwrites any of the existing data or model files bootstrapFit(fit, nboot = 7) # resumes fitting using the stored data and model files # Note this resumes because the total number of bootstrap samples is not 10 bootstrapFit(fit, nboot=10) # Note the boostrap standard error and variance/covariance matrix is retained. # If you wish to switch back you can change the covariance matrix by nlmixr2est::setCov(fit, "linFim") # And change it back again nlmixr2est::setCov(fit, "boot10") # This change will affect any simulations with uncertainty in their parameters # You may also do a chi-square diagnostic plot check for the bootstrap with bootplot(fit) }) ## End(Not run)
Control Options for FOCEi
foceiControl( sigdig = 3, ..., epsilon = NULL, maxInnerIterations = 1000, maxOuterIterations = 5000, n1qn1nsim = NULL, print = 1L, printNcol = floor((getOption("width") - 23)/12), scaleTo = 1, scaleObjective = 0, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleC0 = 1e+05, derivEps = rep(20 * sqrt(.Machine$double.eps), 2), derivMethod = c("switch", "forward", "central"), derivSwitchTol = NULL, covDerivMethod = c("central", "forward"), covMethod = c("r,s", "r", "s", ""), hessEps = (.Machine$double.eps)^(1/3), hessEpsLlik = (.Machine$double.eps)^(1/3), optimHessType = c("central", "forward"), optimHessCovType = c("central", "forward"), eventType = c("central", "forward"), centralDerivEps = rep(20 * sqrt(.Machine$double.eps), 2), lbfgsLmm = 7L, lbfgsPgtol = 0, lbfgsFactr = NULL, eigen = TRUE, addPosthoc = TRUE, diagXform = c("sqrt", "log", "identity"), sumProd = FALSE, optExpression = TRUE, literalFix = TRUE, ci = 0.95, useColor = crayon::has_color(), boundTol = NULL, calcTables = TRUE, noAbort = TRUE, interaction = TRUE, cholSEtol = (.Machine$double.eps)^(1/3), cholAccept = 0.001, resetEtaP = 0.15, resetThetaP = 0.05, resetThetaFinalP = 0.15, diagOmegaBoundUpper = 5, diagOmegaBoundLower = 100, cholSEOpt = FALSE, cholSECov = FALSE, fo = FALSE, covTryHarder = FALSE, outerOpt = c("nlminb", "bobyqa", "lbfgsb3c", "L-BFGS-B", "mma", "lbfgsbLG", "slsqp", "Rvmmin"), innerOpt = c("n1qn1", "BFGS"), rhobeg = 0.2, rhoend = NULL, npt = NULL, rel.tol = NULL, x.tol = NULL, eval.max = 4000, iter.max = 2000, abstol = NULL, reltol = NULL, resetHessianAndEta = FALSE, stateTrim = Inf, shi21maxOuter = 0L, shi21maxInner = 20L, shi21maxInnerCov = 20L, shi21maxFD = 20L, gillK = 10L, gillStep = 4, gillFtol = 0, gillRtol = sqrt(.Machine$double.eps), gillKcov = 10L, gillKcovLlik = 10L, gillStepCovLlik = 4.5, gillStepCov = 2, gillFtolCov = 0, gillFtolCovLlik = 0, rmatNorm = TRUE, rmatNormLlik = TRUE, smatNorm = TRUE, smatNormLlik = TRUE, covGillF = TRUE, optGillF = TRUE, covSmall = 1e-05, adjLik = TRUE, gradTrim = Inf, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), gradCalcCentralSmall = 1e-04, gradCalcCentralLarge = 10000, etaNudge = qnorm(1 - 0.05/2)/sqrt(3), etaNudge2 = qnorm(1 - 0.05/2) * sqrt(3/5), nRetries = 3, seed = 42, resetThetaCheckPer = 0.1, etaMat = NULL, repeatGillMax = 1, stickyRecalcN = 4, gradProgressOfvTime = 10, addProp = c("combined2", "combined1"), badSolveObjfAdj = 100, compress = TRUE, rxControl = NULL, sigdigTable = NULL, fallbackFD = FALSE, smatPer = 0.6, sdLowerFact = 0.001, zeroGradFirstReset = TRUE, zeroGradRunReset = TRUE, zeroGradBobyqa = TRUE )
foceiControl( sigdig = 3, ..., epsilon = NULL, maxInnerIterations = 1000, maxOuterIterations = 5000, n1qn1nsim = NULL, print = 1L, printNcol = floor((getOption("width") - 23)/12), scaleTo = 1, scaleObjective = 0, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleC0 = 1e+05, derivEps = rep(20 * sqrt(.Machine$double.eps), 2), derivMethod = c("switch", "forward", "central"), derivSwitchTol = NULL, covDerivMethod = c("central", "forward"), covMethod = c("r,s", "r", "s", ""), hessEps = (.Machine$double.eps)^(1/3), hessEpsLlik = (.Machine$double.eps)^(1/3), optimHessType = c("central", "forward"), optimHessCovType = c("central", "forward"), eventType = c("central", "forward"), centralDerivEps = rep(20 * sqrt(.Machine$double.eps), 2), lbfgsLmm = 7L, lbfgsPgtol = 0, lbfgsFactr = NULL, eigen = TRUE, addPosthoc = TRUE, diagXform = c("sqrt", "log", "identity"), sumProd = FALSE, optExpression = TRUE, literalFix = TRUE, ci = 0.95, useColor = crayon::has_color(), boundTol = NULL, calcTables = TRUE, noAbort = TRUE, interaction = TRUE, cholSEtol = (.Machine$double.eps)^(1/3), cholAccept = 0.001, resetEtaP = 0.15, resetThetaP = 0.05, resetThetaFinalP = 0.15, diagOmegaBoundUpper = 5, diagOmegaBoundLower = 100, cholSEOpt = FALSE, cholSECov = FALSE, fo = FALSE, covTryHarder = FALSE, outerOpt = c("nlminb", "bobyqa", "lbfgsb3c", "L-BFGS-B", "mma", "lbfgsbLG", "slsqp", "Rvmmin"), innerOpt = c("n1qn1", "BFGS"), rhobeg = 0.2, rhoend = NULL, npt = NULL, rel.tol = NULL, x.tol = NULL, eval.max = 4000, iter.max = 2000, abstol = NULL, reltol = NULL, resetHessianAndEta = FALSE, stateTrim = Inf, shi21maxOuter = 0L, shi21maxInner = 20L, shi21maxInnerCov = 20L, shi21maxFD = 20L, gillK = 10L, gillStep = 4, gillFtol = 0, gillRtol = sqrt(.Machine$double.eps), gillKcov = 10L, gillKcovLlik = 10L, gillStepCovLlik = 4.5, gillStepCov = 2, gillFtolCov = 0, gillFtolCovLlik = 0, rmatNorm = TRUE, rmatNormLlik = TRUE, smatNorm = TRUE, smatNormLlik = TRUE, covGillF = TRUE, optGillF = TRUE, covSmall = 1e-05, adjLik = TRUE, gradTrim = Inf, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), gradCalcCentralSmall = 1e-04, gradCalcCentralLarge = 10000, etaNudge = qnorm(1 - 0.05/2)/sqrt(3), etaNudge2 = qnorm(1 - 0.05/2) * sqrt(3/5), nRetries = 3, seed = 42, resetThetaCheckPer = 0.1, etaMat = NULL, repeatGillMax = 1, stickyRecalcN = 4, gradProgressOfvTime = 10, addProp = c("combined2", "combined1"), badSolveObjfAdj = 100, compress = TRUE, rxControl = NULL, sigdigTable = NULL, fallbackFD = FALSE, smatPer = 0.6, sdLowerFact = 0.001, zeroGradFirstReset = TRUE, zeroGradRunReset = TRUE, zeroGradBobyqa = TRUE )
sigdig |
Optimization significant digits. This controls:
|
... |
Additional arguments passed to |
epsilon |
Precision of estimate for n1qn1 optimization. |
maxInnerIterations |
Number of iterations for n1qn1 optimization. |
maxOuterIterations |
Maximum number of L-BFGS-B optimization for outer problem. |
n1qn1nsim |
Number of function evaluations for n1qn1 optimization. |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
scaleObjective |
Scale the initial objective function to this value. By default this is 0 (meaning do not scale) |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleC0 |
Number to adjust the scaling factor by if the initial gradient is zero. |
derivEps |
Forward difference tolerances, which is a vector of relative difference and absolute difference. The central/forward difference step size h is calculated as:
|
derivMethod |
indicates the method for calculating derivatives of the outer problem. Currently supports "switch", "central" and "forward" difference methods. Switch starts with forward differences. This will switch to central differences when abs(delta(OFV)) <= derivSwitchTol and switch back to forward differences when abs(delta(OFV)) > derivSwitchTol. |
derivSwitchTol |
The tolerance to switch forward to central differences. |
covDerivMethod |
indicates the method for calculating the derivatives while calculating the covariance components (Hessian and S). |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
hessEps |
is a double value representing the epsilon for the Hessian calculation. This is used for the R matrix calculation. |
hessEpsLlik |
is a double value representing the epsilon for the Hessian calculation when doing focei generalized log-likelihood estimation. This is used for the R matrix calculation. |
optimHessType |
The hessian type for when calculating the individual hessian by numeric differences (in generalized log-likelihood estimation). The options are "central", and "forward". The central differences is what R's 'optimHess()' uses and is the default for this method. (Though the "forward" is faster and still reasonable for most cases). The Shi21 cannot be changed for the Gill83 algorithm with the optimHess in a generalized likelihood problem. |
optimHessCovType |
The hessian type for when calculating the individual hessian by numeric differences (in generalized log-likelihood estimation). The options are "central", and "forward". The central differences is what R's 'optimHess()' uses. While this takes longer in optimization, it is more accurate, so for calculating the covariance and final likelihood, the central differences are used. This also uses the modified Shi21 method |
eventType |
Event gradient type for dosing events; Can be "central" or "forward" |
centralDerivEps |
Central difference tolerances. This is a numeric vector of relative difference and absolute difference. The central/forward difference step size h is calculated as:
|
lbfgsLmm |
An integer giving the number of BFGS updates retained in the "L-BFGS-B" method, It defaults to 7. |
lbfgsPgtol |
is a double precision variable. On entry pgtol >= 0 is specified by the user. The iteration will stop when:
where pg_i is the ith component of the projected gradient. On exit pgtol is unchanged. This defaults to zero, when the check is suppressed. |
lbfgsFactr |
Controls the convergence of the "L-BFGS-B"
method. Convergence occurs when the reduction in the
objective is within this factor of the machine
tolerance. Default is 1e10, which gives a tolerance of about
|
eigen |
A boolean indicating if eigenvectors are calculated to include a condition number calculation. |
addPosthoc |
Boolean indicating if posthoc parameters are added to the table output. |
diagXform |
This is the transformation used on the diagonal
of the
|
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
useColor |
Boolean indicating if focei can use ASCII color codes |
boundTol |
Tolerance for boundary issues. |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
noAbort |
Boolean to indicate if you should abort the FOCEi evaluation if it runs into troubles. (default TRUE) |
interaction |
Boolean indicate FOCEi should be used (TRUE) instead of FOCE (FALSE) |
cholSEtol |
tolerance for Generalized Cholesky Decomposition. Defaults to suggested (.Machine$double.eps)^(1/3) |
cholAccept |
Tolerance to accept a Generalized Cholesky Decomposition for a R or S matrix. |
resetEtaP |
represents the p-value for reseting the individual ETA to 0 during optimization (instead of the saved value). The two test statistics used in the z-test are either chol(omega^-1) %*% eta or eta/sd(allEtas). A p-value of 0 indicates the ETAs never reset. A p-value of 1 indicates the ETAs always reset. |
resetThetaP |
represents the p-value for reseting the
population mu-referenced THETA parameters based on ETA drift
during optimization, and resetting the optimization. A
p-value of 0 indicates the THETAs never reset. A p-value of 1
indicates the THETAs always reset and is not allowed. The
theta reset is checked at the beginning and when nearing a
local minima. The percent change in objective function where
a theta reset check is initiated is controlled in
|
resetThetaFinalP |
represents the p-value for reseting the population mu-referenced THETA parameters based on ETA drift during optimization, and resetting the optimization one final time. |
diagOmegaBoundUpper |
This represents the upper bound of the
diagonal omega matrix. The upper bound is given by
diag(omega)*diagOmegaBoundUpper. If
|
diagOmegaBoundLower |
This represents the lower bound of the
diagonal omega matrix. The lower bound is given by
diag(omega)/diagOmegaBoundUpper. If
|
cholSEOpt |
Boolean indicating if the generalized Cholesky should be used while optimizing. |
cholSECov |
Boolean indicating if the generalized Cholesky should be used while calculating the Covariance Matrix. |
fo |
is a boolean indicating if this is a FO approximation routine. |
covTryHarder |
If the R matrix is non-positive definite and cannot be corrected to be non-positive definite try estimating the Hessian on the unscaled parameter space. |
outerOpt |
optimization method for the outer problem |
innerOpt |
optimization method for the inner problem (not implemented yet.) |
rhobeg |
Beginning change in parameters for bobyqa algorithm (trust region). By default this is 0.2 or 20 parameters when the parameters are scaled to 1. rhobeg and rhoend must be set to the initial and final values of a trust region radius, so both must be positive with 0 < rhoend < rhobeg. Typically rhobeg should be about one tenth of the greatest expected change to a variable. Note also that smallest difference abs(upper-lower) should be greater than or equal to rhobeg*2. If this is not the case then rhobeg will be adjusted. (bobyqa) |
rhoend |
The smallest value of the trust region radius that is allowed. If not defined, then 10^(-sigdig-1) will be used. (bobyqa) |
npt |
The number of points used to approximate the objective function via a quadratic approximation for bobyqa. The value of npt must be in the interval [n+2,(n+1)(n+2)/2] where n is the number of parameters in par. Choices that exceed 2*n+1 are not recommended. If not defined, it will be set to 2*n + 1. (bobyqa) |
rel.tol |
Relative tolerance before nlminb stops (nlmimb). |
x.tol |
X tolerance for nlmixr2 optimizer |
eval.max |
Number of maximum evaluations of the objective function (nlmimb) |
iter.max |
Maximum number of iterations allowed (nlmimb) |
abstol |
Absolute tolerance for nlmixr2 optimizer (BFGS) |
reltol |
tolerance for nlmixr2 (BFGS) |
resetHessianAndEta |
is a boolean representing if the
individual Hessian is reset when ETAs are reset using the
option |
stateTrim |
Trim state amounts/concentrations to this value. |
shi21maxOuter |
The maximum number of steps for the optimization of the forward-difference step size. When not zero, use this instead of Gill differences. |
shi21maxInner |
The maximum number of steps for the optimization of the individual Hessian matrices in the generalized likelihood problem. When 0, un-optimized finite differences are used. |
shi21maxInnerCov |
The maximum number of steps for the optimization of the individual Hessian matrices in the generalized likelihood problem for the covariance step. When 0, un-optimized finite differences are used. |
shi21maxFD |
The maximum number of steps for the optimization of the forward difference step size when using dosing events (lag time, modeled duration/rate and bioavailability) |
gillK |
The total number of possible steps to determine the optimal forward/central difference step size per parameter (by the Gill 1983 method). If 0, no optimal step size is determined. Otherwise this is the optimal step size determined. |
gillStep |
When looking for the optimal forward difference step size, this is This is the step size to increase the initial estimate by. So each iteration the new step size = (prior step size)*gillStep |
gillFtol |
The gillFtol is the gradient error tolerance that is acceptable before issuing a warning/error about the gradient estimates. |
gillRtol |
The relative tolerance used for Gill 1983 determination of optimal step size. |
gillKcov |
The total number of possible steps to determine the optimal forward/central difference step size per parameter (by the Gill 1983 method) during the covariance step. If 0, no optimal step size is determined. Otherwise this is the optimal step size determined. |
gillKcovLlik |
The total number of possible steps to determine the optimal forward/central difference step per parameter when using the generalized focei log-likelihood method (by the Gill 1986 method). If 0, no optimal step size is determined. Otherwise this is the optimal step size is determined |
gillStepCovLlik |
Same as above but during generalized focei log-likelihood |
gillStepCov |
When looking for the optimal forward difference step size, this is This is the step size to increase the initial estimate by. So each iteration during the covariance step is equal to the new step size = (prior step size)*gillStepCov |
gillFtolCov |
The gillFtol is the gradient error tolerance that is acceptable before issuing a warning/error about the gradient estimates during the covariance step. |
gillFtolCovLlik |
Same as above but applied during generalized log-likelihood estimation. |
rmatNorm |
A parameter to normalize gradient step size by the parameter value during the calculation of the R matrix |
rmatNormLlik |
A parameter to normalize gradient step size by the parameter value during the calculation of the R matrix if you are using generalized log-likelihood Hessian matrix. |
smatNorm |
A parameter to normalize gradient step size by the parameter value during the calculation of the S matrix |
smatNormLlik |
A parameter to normalize gradient step size by the parameter value during the calculation of the S matrix if you are using the generalized log-likelihood. |
covGillF |
Use the Gill calculated optimal Forward difference step size for the instead of the central difference step size during the central difference gradient calculation. |
optGillF |
Use the Gill calculated optimal Forward difference step size for the instead of the central difference step size during the central differences for optimization. |
covSmall |
The covSmall is the small number to compare covariance numbers before rejecting an estimate of the covariance as the final estimate (when comparing sandwich vs R/S matrix estimates of the covariance). This number controls how small the variance is before the covariance matrix is rejected. |
adjLik |
In nlmixr2, the objective function matches NONMEM's objective function, which removes a 2*pi constant from the likelihood calculation. If this is TRUE, the likelihood function is adjusted by this 2*pi factor. When adjusted this number more closely matches the likelihood approximations of nlme, and SAS approximations. Regardless of if this is turned on or off the objective function matches NONMEM's objective function. |
gradTrim |
The parameter to adjust the gradient to if the |gradient| is very large. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
gradCalcCentralSmall |
A small number that represents the value where |grad| < gradCalcCentralSmall where forward differences switch to central differences. |
gradCalcCentralLarge |
A large number that represents the value where |grad| > gradCalcCentralLarge where forward differences switch to central differences. |
etaNudge |
By default initial ETA estimates start at zero; Sometimes this doesn't optimize appropriately. If this value is non-zero, when the n1qn1 optimization didn't perform appropriately, reset the Hessian, and nudge the ETA up by this value; If the ETA still doesn't move, nudge the ETA down by this value. By default this value is qnorm(1-0.05/2)*1/sqrt(3), the first of the Gauss Quadrature numbers times by the 0.95% normal region. If this is not successful try the second eta nudge number (below). If +-etaNudge2 is not successful, then assign to zero and do not optimize any longer |
etaNudge2 |
This is the second eta nudge. By default it is qnorm(1-0.05/2)*sqrt(3/5), which is the n=3 quadrature point (excluding zero) times by the 0.95% normal region |
nRetries |
If FOCEi doesn't fit with the current parameter estimates, randomly sample new parameter estimates and restart the problem. This is similar to 'PsN' resampling. |
seed |
an object specifying if and how the random number generator should be initialized |
resetThetaCheckPer |
represents objective function % percentage below which resetThetaP is checked. |
etaMat |
Eta matrix for initial estimates or final estimates of the ETAs. |
repeatGillMax |
If the tolerances were reduced when calculating the initial Gill differences, the Gill difference is repeated up to a maximum number of times defined by this parameter. |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
gradProgressOfvTime |
This is the time for a single objective function evaluation (in seconds) to start progress bars on gradient evaluations |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
badSolveObjfAdj |
The objective function adjustment when the ODE system cannot be solved. It is based on each individual bad solve. |
compress |
Should the object have compressed items |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
fallbackFD |
Fallback to the finite differences if the sensitivity equations do not solve. |
smatPer |
A percentage representing the number of failed parameter gradients for each individual (which are replaced with the overall gradient for the parameter) out of the total number of gradients parameters (ie 'ntheta*nsub') before the S matrix is considered to be a bad matrix. |
sdLowerFact |
A factor for multiplying the estimate by when the lower estimate is zero and the error is known to represent a standard deviation of a parameter (like add.sd, prop.sd, pow.sd, lnorm.sd, etc). When zero, no factor is applied. If your initial estimate is 0.15 and your lower bound is zero, then the lower bound would be assumed to be 0.00015. |
zeroGradFirstReset |
boolean, when 'TRUE' if the first gradient is zero, reset the zero gradient to 'sqrt(.Machine$double.eps)' to get past the bad initial estimate, otherwise error (and possibly reset), when 'FALSE' error when the first gradient is zero. When 'NA' on the last reset, have the zero gradient ignored, otherwise error and look for another value. Default is 'TRUE' |
zeroGradRunReset |
boolean, when 'TRUE' if a gradient is zero, reset the zero gradient to 'sqrt(.Machine$double.eps)' to get past the bad estimate while running. Otherwise error (and possibly reset). Default is 'TRUE' |
zeroGradBobyqa |
boolean, when 'TRUE' if a gradient is zero, the reset will change the method to the gradient free bobyqa method. When 'NA', the zero gradient will change to bobyqa only when the first gradient is zero. Default is 'TRUE' |
Note this uses the R's L-BFGS-B in optim
for the
outer problem and the BFGS n1qn1
with that
allows restoring the prior individual Hessian (for faster
optimization speed).
However the inner problem is not scaled. Since most eta estimates start near zero, scaling for these parameters do not make sense.
This process of scaling can fix some ill conditioning for the unscaled problem. The covariance step is performed on the unscaled problem, so the condition number of that matrix may not be reflective of the scaled problem's condition-number.
The control object that changes the options for the FOCEi family of estimation methods
Matthew L. Fidler
Gill, P.E., Murray, W., Saunders, M.A., & Wright, M.H. (1983). Computing Forward-Difference Intervals for Numerical Optimization. Siam Journal on Scientific and Statistical Computing, 4, 310-321.
Shi, H.M., Xie, Y., Xuan, M.Q., & Nocedal, J. (2021). Adaptive Finite-Difference Interval Estimation for Noisy Derivative-Free Optimization.
Other Estimation control:
nlmixr2NlmeControl()
,
saemControl()
Control for lbfgsb3c estimation method in nlmixr2
lbfgsb3cControl( trace = 0, factr = 1e+07, pgtol = 0, abstol = 0, reltol = 0, lmm = 5L, maxit = 10000L, returnLbfgsb3c = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
lbfgsb3cControl( trace = 0, factr = 1e+07, pgtol = 0, abstol = 0, reltol = 0, lmm = 5L, maxit = 10000L, returnLbfgsb3c = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
trace |
If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method "L-BFGS-B" there are six levels of tracing. (To understand exactly what these do see the source code: higher levels give more detail.) |
factr |
controls the convergence of the "L-BFGS-B" method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is 1e7, that is a tolerance of about 1e-8. |
pgtol |
helps control the convergence of the "L-BFGS-B" method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed. |
abstol |
helps control the convergence of the "L-BFGS-B" method. It is an absolute tolerance difference in x values. This defaults to zero, when the check is suppressed. |
reltol |
helps control the convergence of the "L-BFGS-B" method. It is an relative tolerance difference in x values. This defaults to zero, when the check is suppressed. |
lmm |
is an integer giving the number of BFGS updates retained in the "L-BFGS-B" method, It defaults to 5. |
maxit |
maximum number of iterations. |
returnLbfgsb3c |
return the lbfgsb3c output instead of the nlmixr2 fit |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
gradTo |
this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2". |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
bobqya control structure
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="lbfgsb3c") print(fit2) # you can also get the nlm output with fit2$lbfgsb3c fit2$lbfgsb3c # The nlm control has been modified slightly to include # extra components and name the parameters
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="lbfgsb3c") print(fit2) # you can also get the nlm output with fit2$lbfgsb3c fit2$lbfgsb3c # The nlm control has been modified slightly to include # extra components and name the parameters
Control for n1qn1 estimation method in nlmixr2
n1qn1Control( epsilon = (.Machine$double.eps)^0.25, max_iterations = 10000, nsim = 10000, imp = 0, print.functions = FALSE, returnN1qn1 = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "n1qn1", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
n1qn1Control( epsilon = (.Machine$double.eps)^0.25, max_iterations = 10000, nsim = 10000, imp = 0, print.functions = FALSE, returnN1qn1 = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "n1qn1", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
epsilon |
Precision of estimate for n1qn1 optimization. |
max_iterations |
Number of iterations |
nsim |
Number of function evaluations |
imp |
Verbosity of messages. |
print.functions |
Boolean to control if the function value and parameter estimates are echoed every time a function is called. |
returnN1qn1 |
return the n1qn1 output instead of the nlmixr2 fit |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
gradTo |
this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2". |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
bobqya control structure
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="n1qn1") print(fit2) # you can also get the nlm output with fit2$n1qn1 fit2$n1qn1 # The nlm control has been modified slightly to include # extra components and name the parameters
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="n1qn1") print(fit2) # you can also get the nlm output with fit2$n1qn1 fit2$n1qn1 # The nlm control has been modified slightly to include # extra components and name the parameters
Control for newuoa estimation method in nlmixr2
newuoaControl( npt = NULL, rhobeg = NULL, rhoend = NULL, iprint = 0L, maxfun = 100000L, returnNewuoa = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
newuoaControl( npt = NULL, rhobeg = NULL, rhoend = NULL, iprint = 0L, maxfun = 100000L, returnNewuoa = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
npt |
The number of points used to approximate the objective function via a quadratic approximation for bobyqa. The value of npt must be in the interval [n+2,(n+1)(n+2)/2] where n is the number of parameters in par. Choices that exceed 2*n+1 are not recommended. If not defined, it will be set to 2*n + 1. (bobyqa) |
rhobeg |
Beginning change in parameters for bobyqa algorithm (trust region). By default this is 0.2 or 20 parameters when the parameters are scaled to 1. rhobeg and rhoend must be set to the initial and final values of a trust region radius, so both must be positive with 0 < rhoend < rhobeg. Typically rhobeg should be about one tenth of the greatest expected change to a variable. Note also that smallest difference abs(upper-lower) should be greater than or equal to rhobeg*2. If this is not the case then rhobeg will be adjusted. (bobyqa) |
rhoend |
The smallest value of the trust region radius that is allowed. If not defined, then 10^(-sigdig-1) will be used. (bobyqa) |
iprint |
The value of 'iprint' should be set to an integer value in '0, 1, 2, 3, ...', which controls the amount of printing. Specifically, there is no output if 'iprint=0' and there is output only at the start and the return if 'iprint=1'. Otherwise, each new value of 'rho' is printed, with the best vector of variables so far and the corresponding value of the objective function. Further, each new value of the objective function with its variables are output if 'iprint=3'. If 'iprint > 3', the objective function value and corresponding variables are output every 'iprint' evaluations. Default value is '0'. |
maxfun |
The maximum allowed number of function evaluations. If this is exceeded, the method will terminate. |
returnNewuoa |
return the newuoa output instead of the nlmixr2 fit |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
newuoa control structure
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="newuoa") print(fit2) # you can also get the nlm output with fit2$newuoa # The nlm control has been modified slightly to include # extra components and name the parameters
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="newuoa") print(fit2) # you can also get the nlm output with fit2$newuoa # The nlm control has been modified slightly to include # extra components and name the parameters
nlmixr2 defaults controls for nlm
nlmControl( typsize = NULL, fscale = 1, print.level = 0, ndigit = NULL, gradtol = 1e-06, stepmax = NULL, steptol = 1e-06, iterlim = 10000, check.analyticals = FALSE, returnNlm = FALSE, solveType = c("hessian", "grad", "fun"), stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, optimHessType = c("central", "forward"), hessErr = (.Machine$double.eps)^(1/3), shi21maxHess = 20L, useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "nlm", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
nlmControl( typsize = NULL, fscale = 1, print.level = 0, ndigit = NULL, gradtol = 1e-06, stepmax = NULL, steptol = 1e-06, iterlim = 10000, check.analyticals = FALSE, returnNlm = FALSE, solveType = c("hessian", "grad", "fun"), stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, optimHessType = c("central", "forward"), hessErr = (.Machine$double.eps)^(1/3), shi21maxHess = 20L, useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "nlm", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
typsize |
an estimate of the size of each parameter at the minimum. |
fscale |
an estimate of the size of |
print.level |
this argument determines the level of printing
which is done during the minimization process. The default
value of |
ndigit |
the number of significant digits in the function |
gradtol |
a positive scalar giving the tolerance at which the
scaled gradient is considered close enough to zero to
terminate the algorithm. The scaled gradient is a
measure of the relative change in |
stepmax |
a positive scalar which gives the maximum allowable
scaled step length. |
steptol |
A positive scalar providing the minimum allowable relative step length. |
iterlim |
a positive integer specifying the maximum number of iterations to be performed before the program is terminated. |
check.analyticals |
a logical scalar specifying whether the analytic gradients and Hessians, if they are supplied, should be checked against numerical derivatives at the initial parameter values. This can help detect incorrectly formulated gradients or Hessians. |
returnNlm |
is a logical that allows a return of the 'nlm' object |
solveType |
tells if ‘nlm' will use nlmixr2’s analytical gradients when available (finite differences will be used for event-related parameters like parameters controlling lag time, duration/rate of infusion, and modeled bioavailability). This can be: - '"hessian"' which will use the analytical gradients to create a Hessian with finite differences. - '"gradient"' which will use the gradient and let 'nlm' calculate the finite difference hessian - '"fun"' where nlm will calculate both the finite difference gradient and the finite difference Hessian When using nlmixr2's finite differences, the "ideal" step size for either central or forward differences are optimized for with the Shi2021 method which may give more accurate derivatives |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
eventType |
Event gradient type for dosing events; Can be "central" or "forward" |
shiErr |
This represents the epsilon when optimizing the ideal step size for numeric differentiation using the Shi2021 method |
shi21maxFD |
The maximum number of steps for the optimization of the forward difference step size when using dosing events (lag time, modeled duration/rate and bioavailability) |
optimHessType |
The hessian type for when calculating the individual hessian by numeric differences (in generalized log-likelihood estimation). The options are "central", and "forward". The central differences is what R's 'optimHess()' uses and is the default for this method. (Though the "forward" is faster and still reasonable for most cases). The Shi21 cannot be changed for the Gill83 algorithm with the optimHess in a generalized likelihood problem. |
hessErr |
This represents the epsilon when optimizing the Hessian step size using the Shi2021 method. |
shi21maxHess |
Maximum number of times to optimize the best step size for the hessian calculation |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
gradTo |
this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2". |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
allows selection of "r", which uses nlmixr2's 'nlmixr2Hess()' for the hessian calculation or "nlm" which uses the hessian from 'stats::nlm(.., hessian=TRUE)'. When using ‘nlmixr2’s‘ hessian for optimization or 'nlmixr2’s' gradient for solving this defaults to "nlm" since 'stats::optimHess()' assumes an accurate gradient and is faster than 'nlmixr2Hess' |
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
nlm control object
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="nlm") print(fit2) # you can also get the nlm output with fit2$nlm fit2$nlm # The nlm control has been modified slightly to include # extra components and name the parameters
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="nlm") print(fit2) # you can also get the nlm output with fit2$nlm fit2$nlm # The nlm control has been modified slightly to include # extra components and name the parameters
The values supplied in the function call replace the defaults and a list with all possible arguments is returned. The returned list is used as the ‘control’ argument to the ‘nlme’ function.
nlmeControl( maxIter = 100, pnlsMaxIter = 100, msMaxIter = 100, minScale = 0.001, tolerance = 1e-05, niterEM = 25, pnlsTol = 0.001, msTol = 1e-06, returnObject = FALSE, msVerbose = FALSE, msWarnNoConv = TRUE, gradHess = TRUE, apVar = TRUE, .relStep = .Machine$double.eps^(1/3), minAbsParApVar = 0.05, opt = c("nlminb", "nlm"), natural = TRUE, sigma = NULL, optExpression = TRUE, literalFix = TRUE, sumProd = FALSE, rxControl = NULL, method = c("ML", "REML"), random = NULL, fixed = NULL, weights = NULL, verbose = TRUE, returnNlme = FALSE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, muRefCovAlg = TRUE, ... )
nlmeControl( maxIter = 100, pnlsMaxIter = 100, msMaxIter = 100, minScale = 0.001, tolerance = 1e-05, niterEM = 25, pnlsTol = 0.001, msTol = 1e-06, returnObject = FALSE, msVerbose = FALSE, msWarnNoConv = TRUE, gradHess = TRUE, apVar = TRUE, .relStep = .Machine$double.eps^(1/3), minAbsParApVar = 0.05, opt = c("nlminb", "nlm"), natural = TRUE, sigma = NULL, optExpression = TRUE, literalFix = TRUE, sumProd = FALSE, rxControl = NULL, method = c("ML", "REML"), random = NULL, fixed = NULL, weights = NULL, verbose = TRUE, returnNlme = FALSE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, muRefCovAlg = TRUE, ... )
maxIter |
maximum number of iterations for the |
pnlsMaxIter |
maximum number of iterations
for the |
msMaxIter |
maximum number of iterations for |
minScale |
minimum factor by which to shrink the default step size
in an attempt to decrease the sum of squares in the |
tolerance |
tolerance for the convergence criterion in the
|
niterEM |
number of iterations for the EM algorithm used to refine the initial estimates of the random effects variance-covariance coefficients. Default is 25. |
pnlsTol |
tolerance for the convergence criterion in |
msTol |
tolerance for the convergence criterion in |
returnObject |
a logical value indicating whether the fitted
object should be returned when the maximum number of iterations is
reached without convergence of the algorithm. Default is
|
msVerbose |
a logical value passed as the |
msWarnNoConv |
logical indicating if a |
gradHess |
a logical value indicating whether numerical gradient
vectors and Hessian matrices of the log-likelihood function should
be used in the |
apVar |
a logical value indicating whether the approximate
covariance matrix of the variance-covariance parameters should be
calculated. Default is |
.relStep |
relative step for numerical derivatives
calculations. Default is |
minAbsParApVar |
numeric value - minimum absolute parameter value
in the approximate variance calculation. The default is |
opt |
the optimizer to be used, either |
natural |
a logical value indicating whether the |
sigma |
optionally a positive number to fix the residual error at.
If |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
method |
a character string. If |
random |
optionally, any of the following: (i) a two-sided formula
of the form |
fixed |
a two-sided linear formula of the form
|
weights |
an optional |
verbose |
an optional logical value. If |
returnNlme |
Returns the nlme object instead of the nlmixr object (by default FALSE). If any of the nlme specific options of 'random', 'fixed', 'sens', the nlme object is returned |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
muRefCovAlg |
This controls if algebraic expressions that can be mu-referenced are treated as mu-referenced covariates by: 1. Creating a internal data-variable 'nlmixrMuDerCov#' for each algebraic mu-referenced expression 2. Change the algebraic expression to 'nlmixrMuDerCov# * mu_cov_theta' 3. Use the internal mu-referenced covariate for saem 4. After optimization is completed, replace 'model()' with old 'model()' expression 5. Remove 'nlmixrMuDerCov#' from nlmix2 output In general, these covariates should be more accurate since it changes the system to a linear compartment model. Therefore, by default this is 'TRUE'. |
... |
Additional arguments passed to |
a nlmixr-nlme list
Other Estimation control:
foceiControl()
,
saemControl()
nlmeControl() nlmixr2NlmeControl()
nlmeControl() nlmixr2NlmeControl()
nlmixr2 nlminb defaults
nlminbControl( eval.max = 200, iter.max = 150, trace = 0, abs.tol = 0, rel.tol = 1e-10, x.tol = 1.5e-08, xf.tol = 2.2e-14, step.min = 1, step.max = 1, sing.tol = rel.tol, scale = 1, scale.init = NULL, diff.g = NULL, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, returnNlminb = FALSE, solveType = c("hessian", "grad", "fun"), stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, optimHessType = c("central", "forward"), hessErr = (.Machine$double.eps)^(1/3), shi21maxHess = 20L, useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "nlminb", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
nlminbControl( eval.max = 200, iter.max = 150, trace = 0, abs.tol = 0, rel.tol = 1e-10, x.tol = 1.5e-08, xf.tol = 2.2e-14, step.min = 1, step.max = 1, sing.tol = rel.tol, scale = 1, scale.init = NULL, diff.g = NULL, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, returnNlminb = FALSE, solveType = c("hessian", "grad", "fun"), stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, optimHessType = c("central", "forward"), hessErr = (.Machine$double.eps)^(1/3), shi21maxHess = 20L, useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "nlminb", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
eval.max |
Maximum number of evaluations of the objective function allowed. Defaults to 200. |
iter.max |
Maximum number of iterations allowed. Defaults to 150. |
trace |
The value of the objective function and the parameters is printed every trace'th iteration. When 0 no trace information is to be printed |
abs.tol |
Absolute tolerance. Defaults to 0 so the absolute convergence test is not used. If the objective function is known to be non-negative, the previous default of '1e-20' would be more appropriate |
rel.tol |
Relative tolerance. Defaults to '1e-10'. |
x.tol |
X tolerance. Defaults to '1.5e-8'. |
xf.tol |
false convergence tolerance. Defaults to '2.2e-14'. |
step.min |
Minimum step size. Default to ‘1.’. |
step.max |
Maximum step size. Default to ‘1.’. |
sing.tol |
singular convergence tolerance; defaults to 'rel.tol;. |
scale |
See PORT documentation (or leave alone). |
scale.init |
... probably need to check PORT documentation |
diff.g |
an estimated bound on the relative error in the objective function value |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
returnNlminb |
logical; when TRUE this will return the nlminb result instead of the nlmixr2 fit object |
solveType |
tells if ‘nlm' will use nlmixr2’s analytical gradients when available (finite differences will be used for event-related parameters like parameters controlling lag time, duration/rate of infusion, and modeled bioavailability). This can be: - '"hessian"' which will use the analytical gradients to create a Hessian with finite differences. - '"gradient"' which will use the gradient and let 'nlm' calculate the finite difference hessian - '"fun"' where nlm will calculate both the finite difference gradient and the finite difference Hessian When using nlmixr2's finite differences, the "ideal" step size for either central or forward differences are optimized for with the Shi2021 method which may give more accurate derivatives |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
eventType |
Event gradient type for dosing events; Can be "central" or "forward" |
shiErr |
This represents the epsilon when optimizing the ideal step size for numeric differentiation using the Shi2021 method |
shi21maxFD |
The maximum number of steps for the optimization of the forward difference step size when using dosing events (lag time, modeled duration/rate and bioavailability) |
optimHessType |
The hessian type for when calculating the individual hessian by numeric differences (in generalized log-likelihood estimation). The options are "central", and "forward". The central differences is what R's 'optimHess()' uses and is the default for this method. (Though the "forward" is faster and still reasonable for most cases). The Shi21 cannot be changed for the Gill83 algorithm with the optimHess in a generalized likelihood problem. |
hessErr |
This represents the epsilon when optimizing the Hessian step size using the Shi2021 method. |
shi21maxHess |
Maximum number of times to optimize the best step size for the hessian calculation |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
gradTo |
this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2". |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="nlminb") print(fit2) # you can also get the nlm output with fit2$nlminb fit2$nlminb
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="nlminb") print(fit2) # you can also get the nlm output with fit2$nlminb fit2$nlminb
nlmixr2 is an R package for fitting population pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PKPD) models.
nlmixr2( object, data, est = NULL, control = list(), table = tableControl(), ..., save = NULL, envir = parent.frame() )
nlmixr2( object, data, est = NULL, control = list(), table = tableControl(), ..., save = NULL, envir = parent.frame() )
object |
Fitted object or function specifying the model. |
data |
nlmixr data |
est |
estimation method (all methods are shown by 'nlmixr2AllEst()'). Methods can be added for other tools |
control |
The estimation control object. These are expected to be different for each type of estimation method |
table |
The output table control object (like 'tableControl()') |
... |
Additional arguments passed to |
save |
Boolean to save a nlmixr2 object in a rds file in the
working directory. If |
envir |
Environment where the nlmixr object/function is evaluated before running the estimation routine. |
The nlmixr2 generalized function allows common access to the nlmixr2 estimation routines.
The nlmixr object has the following fields:
Field | Description |
conditionNumber | Condition number, that is the highest divided by the lowest eigenvalue in the population covariance matrix |
cor | Correlation matrix |
phiR | correlation matrix of each individual’s eta (if present) |
objDF | Data frame containing objective function information (AIC, BIC, etc.) |
time | Duration of different parts of the analysis (e.g. setup, optimization, calculation of covariance, etc.) |
theta | Estimates for eta for each individual |
etaObf | Estimates for eta for each individual, This also includes the objective function for each individual |
fixef | Estimates of fixed effects |
foceiControl | Estimation options if focei was used |
ui | Final estimates for the model |
dataMergeFull | Full data merge with the fit output and the original dataset; Also includes nlmixrLlikObs which includes the individual observation contribution to the likelihood |
censInfo | Gives the censorng information abot the fit (the type of censoring that was seend and handled in the dataset) |
dataLloq | Gives the lloq from the dataset (average) when cesoring has occured; Requires the fit to have a table step |
dataUloq | Gives the uloq from the dataset (average) when censoring has occured; requires the fit to have a table step |
eta | IIV values for each indiviudal |
dataMergeInner | Inner data merge with the fit output and the original dataset; Also includes nlmixrLlikObs which includes the individual observation contribution to the likelihood |
rxControl | Integration options used to control rxode2 |
dataMergeLeft | Left data merge with the fit output and the original dataset; Also includes nlmixrLlikObs which includes the individual observation contribution to the likelihood |
omega | Matrix containing the estimates of the multivarte normal covariance matrix for between subject varaibilities (omega) |
covMethod | Method used to calculate covariance of the fixed effects |
modelName | Name of the R object containing the model |
origData | Original dataset |
phiRSE | Relative standard error of each individuals eta |
dataMergeRight | Right data merge with the fit output and the original dataset; Also includes nlmixrLlikObs which includes the individual observation contribution to the likelihood |
ipredModel | rxode2 estimation model for fit (internal will likely be removed from visibility |
phiSE | Standard error of each individuals eta |
parFixed | Table of parameter estimates (rounded and pretty looking) |
parFixedDF | Table of parameter estimates as a data frame |
omegaR | The correlation matirx of omega with standard deviations for the diagonal pieces |
iniUi | The initial model used to start the estimation |
finalUi | The model with the estimates replaced as values |
scaleInfo | The scaling factors used for nlmixr2 estimation in focei; The can be changed by foceiControl(scaleC=…) if you think these are unreasonable. It also tells the Gill83 outcome of trying to find the best step size (High gradient error, bad gradient etc) |
table | These are the table options that were used when generating the table output (were CWRES included, etc |
shrink | This is a table of shrinkages for all the individual ETAs as well as the variance shrinkage as well as summary statistics for the ETAs and Residual Error |
env | This is the environment where all the information for the fit is stored outside of the data-frame. It is an R environment hence $env |
seed | This is the initial seed used for saem |
simInfo | This returns a list of all the fit information used for a traditional rxode2 simulation, which you can tweak yourself if you wish |
runInfo | This returns a list of all the warnings or fit information |
parHistStacked | Value of objective function and parameters at each iteration (tall format) |
parHist | Value of objective function and parameters at each iteration (wide format) |
cov | Variance-covariance matrix |
Either a nlmixr2 model or a nlmixr2 fit object
Rationale
nlmixr estimation routines each have their own way of specifying
models. Often the models are specified in ways that are most
intuitive for one estimation routine, but do not make sense for
another estimation routine. Sometimes, legacy estimation
routines like nlme
have their own syntax that
is outside of the control of the nlmixr package.
The unique syntax of each routine makes the routines themselves
easier to maintain and expand, and allows interfacing with
existing packages that are outside of nlmixr (like
nlme
). However, a model definition language
that is common between estimation methods, and an output object
that is uniform, will make it easier to switch between estimation
routines and will facilitate interfacing output with external
packages like Xpose.
The nlmixr mini-modeling language, attempts to address this issue by incorporating a common language. This language is inspired by both R and NONMEM, since these languages are familiar to many pharmacometricians.
Initial Estimates and boundaries for population parameters
nlmixr models are contained in a R function with two blocks:
ini
and model
. This R function can be named
anything, but is not meant to be called directly from R. In fact
if you try you will likely get an error such as Error: could
not find function "ini"
.
The ini
model block is meant to hold the initial estimates
for the model, and the boundaries of the parameters for estimation
routines that support boundaries (note nlmixr's saem
and nlme
do not currently support parameter boundaries).
To explain how these initial estimates are specified we will start with an annotated example:
f <- function(){ ## Note the arguments to the function are currently ## ignored by nlmixr ini({ ## Initial conditions for population parameters (sometimes ## called theta parameters) are defined by either `<-` or '=' lCl <- 1.6 #log Cl (L/hr) ## Note that simple expressions that evaluate to a number are ## OK for defining initial conditions (like in R) lVc = log(90) #log V (L) ## Also a comment on a parameter is captured as a parameter label lKa <- 1 #log Ka (1/hr) ## Bounds may be specified by c(lower, est, upper), like NONMEM: ## Residuals errors are assumed to be population parameters prop.err <- c(0, 0.2, 1) }) ## The model block will be discussed later model({}) }
As shown in the above examples:
Simple parameter values are specified as a R-compatible assignment
Boundaries my be specified by c(lower, est, upper)
.
Like NONMEM, c(lower,est)
is equivalent to c(lower,est,Inf)
Also like NONMEM, c(est)
does not specify a lower bound, and is equivalent
to specifying the parameter without R's 'c' function.
The initial estimates are specified on the variance scale, and in analogy with NONMEM, the square roots of the diagonal elements correspond to coefficients of variation when used in the exponential IIV implementation
These parameters can be named almost any R compatible name. Please note that:
Residual error estimates should be coded as population estimates (i.e. using an '=' or '<-' statement, not a '~').
Naming variables that start with "_
" are not supported. Note that R does not
allow variable starting with "_
" to be assigned without quoting them.
Naming variables that start with "rx_
" or "nlmixr_
" is
not supported since rxode2 and nlmixr2 use these prefixes
internally for certain estimation routines and calculating residuals.
Variable names are case sensitive, just like they are in R. "CL
" is not the
same as "Cl
".
Initial Estimates for between subject error distribution (NONMEM's $OMEGA)
In mixture models, multivariate normal individual deviations from
the population parameters are estimated (in NONMEM these are
called eta
parameters). Additionally the
variance/covariance matrix of these deviations is also estimated
(in NONMEM this is the OMEGA matrix). These also have initial
estimates. In nlmixr these are specified by the '~' operator that
is typically used in R for "modeled by", and was chosen to
distinguish these estimates from the population and residual error
parameters.
Continuing the prior example, we can annotate the estimates for the between subject error distribution
f <- function(){ ini({ lCl <- 1.6 #log Cl (L/hr) lVc = log(90) #log V (L) lKa <- 1 #log Ka (1/hr) prop.err <- c(0, 0.2, 1) ## Initial estimate for ka IIV variance ## Labels work for single parameters eta.ka ~ 0.1 # BSV Ka ## For correlated parameters, you specify the names of each ## correlated parameter separated by a addition operator `+` ## and the left handed side specifies the lower triangular ## matrix initial of the covariance matrix. eta.cl + eta.vc ~ c(0.1, 0.005, 0.1) ## Note that labels do not currently work for correlated ## parameters. Also do not put comments inside the lower ## triangular matrix as this will currently break the model. }) ## The model block will be discussed later model({}) }
As shown in the above examples:
Simple variances are specified by the variable name and the estimate separated by '~'.
Correlated parameters are specified by the sum of the variable labels and then the lower triangular matrix of the covariance is specified on the left handed side of the equation. This is also separated by '~'.
Currently the model syntax does not allow comments inside the lower triangular matrix.
Model Syntax for ODE based models (NONMEM's $PK, $PRED, $DES and $ERROR)
Once the initialization block has been defined, you can define a
model in terms of the defined variables in the ini
block. You can
also mix in RxODE blocks into the model.
The current method of defining a nlmixr model is to specify the parameters, and then possibly the RxODE lines:
Continuing describing the syntax with an annotated example:
f <- function(){ ini({ lCl <- 1.6 #log Cl (L/hr) lVc <- log(90) #log Vc (L) lKA <- 0.1 #log Ka (1/hr) prop.err <- c(0, 0.2, 1) eta.Cl ~ 0.1 ## BSV Cl eta.Vc ~ 0.1 ## BSV Vc eta.KA ~ 0.1 ## BSV Ka }) model({ ## First parameters are defined in terms of the initial estimates ## parameter names. Cl <- exp(lCl + eta.Cl) Vc = exp(lVc + eta.Vc) KA <- exp(lKA + eta.KA) ## After the differential equations are defined kel <- Cl / Vc; d/dt(depot) = -KA*depot; d/dt(centr) = KA*depot-kel*centr; ## And the concentration is then calculated cp = centr / Vc; ## Last, nlmixr is told that the plasma concentration follows ## a proportional error (estimated by the parameter prop.err) cp ~ prop(prop.err) }) }
A few points to note:
Parameters are often defined before the differential equations.
The differential equations, parameters and error terms are in a single block, instead of multiple sections.
State names, calculated variables cannot start with either "rx_
"
or "nlmixr_
" since these are used internally in some estimation routines.
Errors are specified using the '~'. Currently you can use either add(parameter)
for additive error, prop(parameter) for proportional error or add(parameter1) + prop(parameter2)
for additive plus proportional error. You can also specify norm(parameter)
for the additive error,
since it follows a normal distribution.
Some routines, like saem
require parameters in terms of Pop.Parameter + Individual.Deviation.Parameter + Covariate*Covariate.Parameter
.
The order of these parameters do not matter. This is similar to NONMEM's mu-referencing, though
not quite so restrictive.
The type of parameter in the model is determined by the initial block; Covariates used in the
model are missing in the ini
block. These variables need to be present in the modeling
dataset for the model to run.
Model Syntax for solved PK systems
Solved PK systems are also currently supported by nlmixr with the 'linCmt()' pseudo-function. An annotated example of a solved system is below:
##'
f <- function(){ ini({ lCl <- 1.6 #log Cl (L/hr) lVc <- log(90) #log Vc (L) lKA <- 0.1 #log Ka (1/hr) prop.err <- c(0, 0.2, 1) eta.Cl ~ 0.1 ## BSV Cl eta.Vc ~ 0.1 ## BSV Vc eta.KA ~ 0.1 ## BSV Ka }) model({ Cl <- exp(lCl + eta.Cl) Vc = exp(lVc + eta.Vc) KA <- exp(lKA + eta.KA) ## Instead of specifying the ODEs, you can use ## the linCmt() function to use the solved system. ## ## This function determines the type of PK solved system ## to use by the parameters that are defined. In this case ## it knows that this is a one-compartment model with first-order ## absorption. linCmt() ~ prop(prop.err) }) }
A few things to keep in mind:
While RxODE allows mixing of solved systems and ODEs, this has not been implemented in nlmixr yet.
The solved systems implemented are the one, two and three compartment models with or without first-order absorption. Each of the models support a lag time with a tlag parameter.
In general the linear compartment model figures out the model by the parameter names. nlmixr currently knows about numbered volumes, Vc/Vp, Clearances in terms of both Cl and Q/CLD. Additionally nlmixr knows about elimination micro-constants (ie K12). Mixing of these parameters for these models is currently not supported.
Checking model syntax
After specifying the model syntax you can check that nlmixr is
interpreting it correctly by using the nlmixr
function on
it.
Using the above function we can get:
> nlmixr(f) ## 1-compartment model with first-order absorption in terms of Cl ## Initialization: ################################################################################ Fixed Effects ($theta): lCl lVc lKA 1.60000 4.49981 0.10000 Omega ($omega): [,1] [,2] [,3] [1,] 0.1 0.0 0.0 [2,] 0.0 0.1 0.0 [3,] 0.0 0.0 0.1 ## Model: ################################################################################ Cl <- exp(lCl + eta.Cl) Vc = exp(lVc + eta.Vc) KA <- exp(lKA + eta.KA) ## Instead of specifying the ODEs, you can use ## the linCmt() function to use the solved system. ## ## This function determines the type of PK solved system ## to use by the parameters that are defined. In this case ## it knows that this is a one-compartment model with first-order ## absorption. linCmt() ~ prop(prop.err)
In general this gives you information about the model (what type of solved system/RxODE), initial estimates as well as the code for the model block.
Using the model syntax for estimating a model
Once the model function has been created, you can use it and a dataset to estimate the parameters for a model given a dataset.
This dataset has to have RxODE compatible events IDs. Both Monolix and NONMEM use a a very similar standard to what nlmixr can support.
Once the data has been converted to the appropriate format, you
can use the nlmixr
function to run the appropriate code.
The method to estimate the model is:
fit <- nlmixr(model.function, dataset, est="est", control=estControl(options))
Currently nlme
and saem
are implemented. For example, to run the
above model with saem
, we could have the following:
> f <- function(){ ini({ lCl <- 1.6 #log Cl (L/hr) lVc <- log(90) #log Vc (L) lKA <- 0.1 #log Ka (1/hr) prop.err <- c(0, 0.2, 1) eta.Cl ~ 0.1 ## BSV Cl eta.Vc ~ 0.1 ## BSV Vc eta.KA ~ 0.1 ## BSV Ka }) model({ ## First parameters are defined in terms of the initial estimates ## parameter names. Cl <- exp(lCl + eta.Cl) Vc = exp(lVc + eta.Vc) KA <- exp(lKA + eta.KA) ## After the differential equations are defined kel <- Cl / Vc; d/dt(depot) = -KA*depot; d/dt(centr) = KA*depot-kel*centr; ## And the concentration is then calculated cp = centr / Vc; ## Last, nlmixr is told that the plasma concentration follows ## a proportional error (estimated by the parameter prop.err) cp ~ prop(prop.err) }) } > fit.s <- nlmixr(f,d,est="saem",control=saemControl(n.burn=50,n.em=100,print=50)); Compiling RxODE differential equations...done. c:/Rtools/mingw_64/bin/g++ -I"c:/R/R-34~1.1/include" -DNDEBUG -I"d:/Compiler/gcc-4.9.3/local330/include" -Ic:/nlmixr/inst/include -Ic:/R/R-34~1.1/library/STANHE~1/include -Ic:/R/R-34~1.1/library/Rcpp/include -Ic:/R/R-34~1.1/library/RCPPAR~1/include -Ic:/R/R-34~1.1/library/RCPPEI~1/include -Ic:/R/R-34~1.1/library/BH/include -O2 -Wall -mtune=core2 -c saem3090757b4bd1x64.cpp -o saem3090757b4bd1x64.o In file included from c:/R/R-34~1.1/library/RCPPAR~1/include/armadillo:52:0, from c:/R/R-34~1.1/library/RCPPAR~1/include/RcppArmadilloForward.h:46, from c:/R/R-34~1.1/library/RCPPAR~1/include/RcppArmadillo.h:31, from saem3090757b4bd1x64.cpp:1: c:/R/R-34~1.1/library/RCPPAR~1/include/armadillo_bits/compiler_setup.hpp:474:96: note: #pragma message: WARNING: use of OpenMP disabled; this compiler doesn't support OpenMP 3.0+ #pragma message ("WARNING: use of OpenMP disabled; this compiler doesn't support OpenMP 3.0+") ^ c:/Rtools/mingw_64/bin/g++ -shared -s -static-libgcc -o saem3090757b4bd1x64.dll tmp.def saem3090757b4bd1x64.o c:/nlmixr/R/rx_855815def56a50f0e7a80e48811d947c_x64.dll -Lc:/R/R-34~1.1/bin/x64 -lRblas -Lc:/R/R-34~1.1/bin/x64 -lRlapack -lgfortran -lm -lquadmath -Ld:/Compiler/gcc-4.9.3/local330/lib/x64 -Ld:/Compiler/gcc-4.9.3/local330/lib -Lc:/R/R-34~1.1/bin/x64 -lR done. 1: 1.8174 4.6328 0.0553 0.0950 0.0950 0.0950 0.6357 50: 1.3900 4.2039 0.0001 0.0679 0.0784 0.1082 0.1992 100: 1.3894 4.2054 0.0107 0.0686 0.0777 0.1111 0.1981 150: 1.3885 4.2041 0.0089 0.0683 0.0778 0.1117 0.1980 Using sympy via SnakeCharmR ## Calculate ETA-based prediction and error derivatives: Calculate Jacobian...................done. Calculate sensitivities....... done. ## Calculate d(f)/d(eta) ## ... ## done ## ... ## done The model-based sensitivities have been calculated Calculating Table Variables... done
The options for saem
are controlled by saemControl
.
You may wish to make sure the minimization is complete in the case
of saem
. You can do that with traceplot
which shows the
iteration history with the divided by burn-in and EM phases. In
this case, the burn in seems reasonable; you may wish to increase
the number of iterations in the EM phase of the estimation.
Overall it is probably a semi-reasonable solution.
nlmixr output objects
In addition to unifying the modeling language sent to each of the estimation routines, the outputs currently have a unified structure.
You can see the fit object by typing the object name:
> fit.s -- nlmixr SAEM fit (ODE); OBJF calculated from FOCEi approximation ------------- OBJF AIC BIC Log-likelihood Condition Number 62337.09 62351.09 62399.01 -31168.55 82.6086 -- Time (sec; fit.s$time): ----------------------------------------------------- saem setup Likelihood Calculation covariance table elapsed 430.25 31.64 1.19 0 3.44 -- Parameters (fit.s$par.fixed): ----------------------------------------------- Parameter Estimate SE lCl log Cl (L/hr) 1.39 0.0240 1.73 4.01 (3.83, 4.20) 26.6 lVc log Vc (L) 4.20 0.0256 0.608 67.0 (63.7, 70.4) 28.5 lKA log Ka (1/hr) 0.00924 0.0323 349. 1.01 (0.947, 1.08) 34.3 prop.err prop.err 0.198 19.8 Shrink(SD) lCl 0.248 lVc 1.09 lKA 4.19 prop.err 1.81 No correlations in between subject variability (BSV) matrix Full BSV covariance (fit.s$omega) or correlation (fit.s$omega.R; diagonals=SDs) Distribution stats (mean/skewness/kurtosis/p-value) available in fit.s$shrink -- Fit Data (object fit.s is a modified data.frame): --------------------------- # A tibble: 6,947 x 22 ID TIME DV PRED RES WRES IPRED IRES IWRES CPRED CRES * <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 1 0.25 205. 198. 6.60 0.0741 189. 16.2 0.434 198. 6.78 2 1 0.5 311. 349. -38.7 -0.261 330. -19.0 -0.291 349. -38.3 3 1 0.75 389. 464. -74.5 -0.398 434. -45.2 -0.526 463. -73.9 # ... with 6,944 more rows, and 11 more variables: CWRES <dbl>, eta.Cl <dbl>, # eta.Vc <dbl>, eta.KA <dbl>, depot <dbl>, centr <dbl>, Cl <dbl>, Vc <dbl>, # KA <dbl>, kel <dbl>, cp <dbl>
This example shows what is typical printout of a nlmixr fit object. The elements of the fit are:
The type of fit (nlme
, saem
, etc)
Metrics of goodness of fit (AIC
, BIC
,
and logLik
).
To align the comparison between methods, the FOCEi likelihood objective is calculated regardless of the method used and used for goodness of fit metrics.
This FOCEi likelihood has been compared to NONMEM's objective function and gives the same values (based on the data in Wang 2007)
Also note that saem
does not calculate an objective function,
and the FOCEi is used as the only objective function for the fit.
Even though the objective functions are calculated in the same manner, caution should be used when comparing fits from various estimation routines.
The next item is the timing of each of the steps of the fit.
These can be also accessed by (fit.s$time
).
As a mnemonic, the access for this item is shown in the printout. This is true for almost all of the other items in the printout.
After the timing of the fit, the parameter estimates are displayed (can be accessed by
fit.s$par.fixed
)
While the items are rounded for R printing, each estimate without rounding is still accessible by the '$' syntax. For example, the '$Untransformed' gives the untransformed parameter values.
The Untransformed parameter takes log-space parameters and back-transforms them to normal parameters. Not the CIs are listed on the back-transformed parameter space.
Proportional Errors are converted to
Omega block (accessed by fit.s$omega
)
The table of fit data. Please note:
A nlmixr fit object is actually a data frame. Saving it as a Rdata object and then loading it without nlmixr will just show the data by itself. Don't worry; the fit information has not vanished, you can bring it back by simply loading nlmixr, and then accessing the data.
Special access to fit information (like the $omega
) needs nlmixr to extract the information.
If you use the $
to access information, the order of precedence is:
Fit data from the overall data.frame
Information about the parsed nlmixr model (via $uif
)
Parameter history if available (via $par.hist
and $par.hist.stacked
)
Fixed effects table (via $par.fixed
)
Individual differences from the typical population parameters (via $eta
)
Fit information from the list of information generated during the post-hoc residual calculation.
Fit information from the environment where the post-hoc residual were calculated
Fit information about how the data and options interacted with the specified model (such as estimation options or if the solved system is for an infusion or an IV bolus).
While the printout may displays the data as a data.table
object or tbl
object, the data is NOT any of these objects, but rather a derived data frame.
Since the object is a data.frame, you can treat it like one.
In addition to the above properties of the fit object, there are a few additional that may be helpful for the modeler:
$theta
gives the fixed effects parameter estimates (in NONMEM the
theta
s). This can also be accessed in fixed.effects
function. Note that the residual variability is treated as a fixed effect parameter
and is included in this list.
$eta
gives the random effects parameter estimates, or in NONMEM the
eta
s. This can also be accessed in using the random.effects
function.
Matthew L. Fidler
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 prop.sd <- 0.01 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) + prop(prop.sd) }) } # fitF <- nlmixr(one.cmt, theo_sd, "focei") fitS <- nlmixr(one.cmt, theo_sd, "saem")
one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 prop.sd <- 0.01 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) + prop(prop.sd) }) } # fitF <- nlmixr(one.cmt, theo_sd, "focei") fitS <- nlmixr(one.cmt, theo_sd, "saem")
Check your nlmixr2 installation for potential issues
nlmixr2CheckInstall()
nlmixr2CheckInstall()
nlmixr2CheckInstall()
nlmixr2CheckInstall()
nlmixr2 defaults controls for nls
nlsControl( maxiter = 10000, tol = 1e-05, minFactor = 1/1024, printEval = FALSE, warnOnly = FALSE, scaleOffset = 0, nDcentral = FALSE, algorithm = c("LM", "default", "plinear", "port"), ftol = sqrt(.Machine$double.eps), ptol = sqrt(.Machine$double.eps), gtol = 0, diag = list(), epsfcn = 0, factor = 100, maxfev = integer(), nprint = 0, solveType = c("grad", "fun"), stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, trace = FALSE, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, returnNls = FALSE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
nlsControl( maxiter = 10000, tol = 1e-05, minFactor = 1/1024, printEval = FALSE, warnOnly = FALSE, scaleOffset = 0, nDcentral = FALSE, algorithm = c("LM", "default", "plinear", "port"), ftol = sqrt(.Machine$double.eps), ptol = sqrt(.Machine$double.eps), gtol = 0, diag = list(), epsfcn = 0, factor = 100, maxfev = integer(), nprint = 0, solveType = c("grad", "fun"), stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, trace = FALSE, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, returnNls = FALSE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
maxiter |
A positive integer specifying the maximum number of iterations allowed. |
tol |
A positive numeric value specifying the tolerance level for the relative offset convergence criterion. |
minFactor |
A positive numeric value specifying the minimum step-size factor allowed on any step in the iteration. The increment is calculated with a Gauss-Newton algorithm and successively halved until the residual sum of squares has been decreased or until the step-size factor has been reduced below this limit. |
printEval |
a logical specifying whether the number of evaluations (steps in the gradient direction taken each iteration) is printed. |
warnOnly |
a logical specifying whether |
scaleOffset |
a constant to be added to the denominator of the relative
offset convergence criterion calculation to avoid a zero divide in the case
where the fit of a model to data is very close. The default value of
|
nDcentral |
only when numerical derivatives are used:
|
algorithm |
character string specifying the algorithm to use.
The default algorithm is a Gauss-Newton algorithm. Other possible
values are |
ftol |
non-negative numeric. Termination occurs when
both the actual and predicted relative reductions in the sum of
squares are at most |
ptol |
non-negative numeric. Termination occurs when
the relative error between two consecutive iterates is at most
|
gtol |
non-negative numeric. Termination occurs when
the cosine of the angle between result of |
diag |
a list or numeric vector containing positive
entries that serve as multiplicative scale factors for the
parameters. Length of |
epsfcn |
(used if |
factor |
positive numeric, used in determining the
initial step bound. This bound is set to the product of
|
maxfev |
integer; termination occurs
when the number of calls to |
nprint |
is an integer; set |
solveType |
tells if ‘nlm' will use nlmixr2’s analytical gradients when available (finite differences will be used for event-related parameters like parameters controlling lag time, duration/rate of infusion, and modeled bioavailability). This can be: - '"hessian"' which will use the analytical gradients to create a Hessian with finite differences. - '"gradient"' which will use the gradient and let 'nlm' calculate the finite difference hessian - '"fun"' where nlm will calculate both the finite difference gradient and the finite difference Hessian When using nlmixr2's finite differences, the "ideal" step size for either central or forward differences are optimized for with the Shi2021 method which may give more accurate derivatives |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
eventType |
Event gradient type for dosing events; Can be "central" or "forward" |
shiErr |
This represents the epsilon when optimizing the ideal step size for numeric differentiation using the Shi2021 method |
shi21maxFD |
The maximum number of steps for the optimization of the forward difference step size when using dosing events (lag time, modeled duration/rate and bioavailability) |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
gradTo |
this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2". |
trace |
logical value indicating if a trace of the iteration
progress should be printed. Default is |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
returnNls |
logical; when TRUE, will return the nls object instead of the nlmixr object |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
nls control object
Matthew L. Fidler
if (rxode2::.linCmtSensB()) { one.cmt <- function() { ini({ tka <- 0.45 tcl <- log(c(0, 2.7, 100)) tv <- 3.45 add.sd <- 0.7 }) model({ ka <- exp(tka) cl <- exp(tcl) v <- exp(tv) linCmt() ~ add(add.sd) }) } # Uses nlsLM from minpack.lm if available fit1 <- nlmixr(one.cmt, nlmixr2data::theo_sd, est="nls", nlsControl(algorithm="LM")) # Uses port and respect parameter boundaries fit2 <- nlmixr(one.cmt, nlmixr2data::theo_sd, est="nls", nlsControl(algorithm="port")) # You can access the underlying nls object with `$nls` fit2$nls }
if (rxode2::.linCmtSensB()) { one.cmt <- function() { ini({ tka <- 0.45 tcl <- log(c(0, 2.7, 100)) tv <- 3.45 add.sd <- 0.7 }) model({ ka <- exp(tka) cl <- exp(tcl) v <- exp(tv) linCmt() ~ add(add.sd) }) } # Uses nlsLM from minpack.lm if available fit1 <- nlmixr(one.cmt, nlmixr2data::theo_sd, est="nls", nlsControl(algorithm="LM")) # Uses port and respect parameter boundaries fit2 <- nlmixr(one.cmt, nlmixr2data::theo_sd, est="nls", nlsControl(algorithm="port")) # You can access the underlying nls object with `$nls` fit2$nls }
nlmixr2 optim defaults
optimControl( method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent"), trace = 0, fnscale = 1, parscale = 1, ndeps = 0.001, maxit = 10000, abstol = 1e-08, reltol = 1e-08, alpha = 1, beta = 0.5, gamma = 2, REPORT = NULL, warn.1d.NelderMead = TRUE, type = NULL, lmm = 5, factr = 1e+07, pgtol = 0, temp = 10, tmax = 10, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, solveType = c("grad", "fun"), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, returnOptim = FALSE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "optim", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
optimControl( method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent"), trace = 0, fnscale = 1, parscale = 1, ndeps = 0.001, maxit = 10000, abstol = 1e-08, reltol = 1e-08, alpha = 1, beta = 0.5, gamma = 2, REPORT = NULL, warn.1d.NelderMead = TRUE, type = NULL, lmm = 5, factr = 1e+07, pgtol = 0, temp = 10, tmax = 10, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), eventType = c("central", "forward"), shiErr = (.Machine$double.eps)^(1/3), shi21maxFD = 20L, solveType = c("grad", "fun"), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, gradTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, returnOptim = FALSE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", "optim", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
method |
The method to be used. See ‘Details’. Can be abbreviated. |
trace |
Non-negative integer. If positive, tracing information on the progress of the optimization is produced. Higher values may produce more tracing information: for method '"L-BFGS-B"', there are six levels of tracing. See 'optim()' for more information |
fnscale |
An overall scaling to be applied to the value of 'fn' and 'gr' during optimization. If negative, turns the problem into a maximization problem. Optimization is performed on 'fn(par)/fnscale' |
parscale |
A vector of scaling values for the parameters. Optimization is performed on 'par/parscale' and these should be comparable in the sense that a unit change in any element produces about a unit change in the scaled value. Not used (nor needed) for 'method = "Brent"' |
ndeps |
A vector of step sizes for the finite-difference approximation to the gradient, on 'par/parscale' scale. Defaults to '1e-3' |
maxit |
The maximum number of iterations. Defaults to '100' for the derivative-based methods, and '500' for '"Nelder-Mead"'. |
abstol |
The absolute convergence tolerance. Only useful for non-negative functions, as a tolerance for reaching zero. |
reltol |
Relative convergence tolerance. The algorithm stops if it is unable to reduce the value by a factor of 'reltol * (abs(val) + reltol)' at a step |
alpha |
Reflection factor for the '"Nelder-Mead"' method. |
beta |
Contraction factor for the '"Nelder-Mead"' method |
gamma |
Expansion factor for the '"Nelder-Mead"' method |
REPORT |
The frequency of reports for the '"BFGS"', '"L-BFGS-B"' and '"SANN"' methods if 'control$trace' is positive. Defaults to every 10 iterations for '"BFGS"' and '"L-BFGS-B"', or every 100 temperatures for '"SANN"' |
warn.1d.NelderMead |
a logical indicating if the (default) '"Nelder-Mead"' method should signal a warning when used for one-dimensional minimization. As the warning is sometimes inappropriate, you can suppress it by setting this option to 'FALSE' |
type |
for the conjugate-gradients method. Takes value '1' for the Fletcher-Reeves update, '2' for Polak-Ribiere and '3' for Beale-Sorenson. |
lmm |
is an integer giving the number of BFGS updates retained in the '"L-BFGS-B"' method, It defaults to '5' |
factr |
controls the convergence of the '"L-BFGS-B"' method. Convergence occurs when the reduction in the objective is within this factor of the machine tolerance. Default is '1e7', that is a tolerance of about '1e-8'. |
pgtol |
helps control the convergence of the ‘"L-BFGS-B"’ method. It is a tolerance on the projected gradient in the current search direction. This defaults to zero, when the check is suppressed |
temp |
controls the '"SANN"' method. It is the starting temperature for the cooling schedule. Defaults to '10'. |
tmax |
is the number of function evaluations at each temperature for the '"SANN"' method. Defaults to '10'. |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
eventType |
Event gradient type for dosing events; Can be "central" or "forward" |
shiErr |
This represents the epsilon when optimizing the ideal step size for numeric differentiation using the Shi2021 method |
shi21maxFD |
The maximum number of steps for the optimization of the forward difference step size when using dosing events (lag time, modeled duration/rate and bioavailability) |
solveType |
tells if ‘optim' will use nlmixr2’s analytical gradients when available (finite differences will be used for event-related parameters like parameters controlling lag time, duration/rate of infusion, and modeled bioavailability). This can be: - '"gradient"' which will use the gradient and let 'optim' calculate the finite difference hessian - '"fun"' where optim will calculate both the finite difference gradient and the finite difference Hessian When using nlmixr2's finite differences, the "ideal" step size for either central or forward differences are optimized for with the Shi2021 method which may give more accurate derivatives These are only applied in the gradient based methods: "BFGS", "CG", "L-BFGS-B" |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
gradTo |
this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2". |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
returnOptim |
logical; when TRUE this will return the optim list instead of the nlmixr2 fit object |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
allows selection of "r", which uses nlmixr2's 'nlmixr2Hess()' for the hessian calculation or "optim" which uses the hessian from 'stats::optim(.., hessian=TRUE)' |
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
optimControl object for nlmixr2
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="optim", optimControl(method="BFGS")) fit2
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="optim", optimControl(method="BFGS")) fit2
Linearly re-parameterize the model to be less sensitive to rounding errors
preconditionFit(fit, estType = c("full", "posthoc", "none"), ntry = 10L)
preconditionFit(fit, estType = c("full", "posthoc", "none"), ntry = 10L)
fit |
A nlmixr2 fit to be preconditioned |
estType |
Once the fit has been linearly reparameterized, should a "full" estimation, "posthoc" estimation or simply a estimation of the covariance matrix "none" before the fit is updated |
ntry |
number of tries before giving up on a pre-conditioned covariance estimate |
A nlmixr2 fit object that was preconditioned to stabilize the variance/covariance calculation
Aoki Y, Nordgren R, Hooker AC. Preconditioning of Nonlinear Mixed Effects Models for Stabilisation of Variance-Covariance Matrix Computations. AAPS J. 2016;18(2):505-518. doi:10.1208/s12248-016-9866-5
Estimate the objective function values for a model while fixing defined parameter values
profileFixed(fitted, which, control = list())
profileFixed(fitted, which, control = list())
fitted |
The fit model |
which |
A data.frame with column names of parameters to fix and values of the fitted value to fix (one row only). |
control |
A list passed to |
which
with a column named OFV
added with the objective function
value of the fitted estimate fixing the parameters in the other columns
profileFixedSingle()
: Estimate the objective function value for a model
while fixing a single set of defined parameter values (for use in parameter
profiling)
Other Profiling:
fixedControl()
,
llpControl()
,
profile.nlmixr2FitCore()
,
profileLlp()
,
profileNlmixr2FitCoreRet()
Other Profiling:
fixedControl()
,
llpControl()
,
profile.nlmixr2FitCore()
,
profileLlp()
,
profileNlmixr2FitCoreRet()
Estimate the objective function values for a model while fixing defined parameter values
profileFixedSingle(fitted, which)
profileFixedSingle(fitted, which)
fitted |
The fit model |
which |
A data.frame with column names of parameters to fix and values of the fitted value to fix (one row only). |
which
with a column named OFV
added with the objective function
value of the fitted estimate fixing the parameters in the other columns
profileFixedSingle()
: Estimate the objective function value for a model
while fixing a single set of defined parameter values (for use in parameter
profiling)
Other Profiling:
fixedControl()
,
llpControl()
,
profile.nlmixr2FitCore()
,
profileLlp()
,
profileNlmixr2FitCoreRet()
Other Profiling:
fixedControl()
,
llpControl()
,
profile.nlmixr2FitCore()
,
profileLlp()
,
profileNlmixr2FitCoreRet()
Profile confidence intervals with log-likelihood profiling
profileLlp(fitted, which, control)
profileLlp(fitted, which, control)
fitted |
The fit model |
which |
Either |
control |
A list passed to |
A data.frame with columns named "Parameter" (the parameter name(s) that were fixed), OFV (the objective function value), and the current estimate for each of the parameters. In addition, if any boundary is found, the OFV increase will be indicated by the absolute value of the "profileBound" column and if that boundary is the upper or lower boundary will be indicated by the "profileBound" column being positive or negative, respectively.
Other Profiling:
fixedControl()
,
llpControl()
,
profile.nlmixr2FitCore()
,
profileFixed()
,
profileNlmixr2FitCoreRet()
Control Options for SAEM
saemControl( seed = 99, nBurn = 200, nEm = 300, nmc = 3, nu = c(2, 2, 2), print = 1, trace = 0, covMethod = c("linFim", "fim", "r,s", "r", "s", ""), calcTables = TRUE, logLik = FALSE, nnodesGq = 3, nsdGq = 1.6, optExpression = TRUE, literalFix = TRUE, adjObf = TRUE, sumProd = FALSE, addProp = c("combined2", "combined1"), tol = 1e-06, itmax = 30, type = c("nelder-mead", "newuoa"), powRange = 10, lambdaRange = 3, odeRecalcFactor = 10^(0.5), maxOdeRecalc = 5L, perSa = 0.75, perNoCor = 0.75, perFixOmega = 0.1, perFixResid = 0.1, compress = TRUE, rxControl = NULL, sigdig = NULL, sigdigTable = NULL, ci = 0.95, muRefCov = TRUE, muRefCovAlg = TRUE, handleUninformativeEtas = TRUE, ... )
saemControl( seed = 99, nBurn = 200, nEm = 300, nmc = 3, nu = c(2, 2, 2), print = 1, trace = 0, covMethod = c("linFim", "fim", "r,s", "r", "s", ""), calcTables = TRUE, logLik = FALSE, nnodesGq = 3, nsdGq = 1.6, optExpression = TRUE, literalFix = TRUE, adjObf = TRUE, sumProd = FALSE, addProp = c("combined2", "combined1"), tol = 1e-06, itmax = 30, type = c("nelder-mead", "newuoa"), powRange = 10, lambdaRange = 3, odeRecalcFactor = 10^(0.5), maxOdeRecalc = 5L, perSa = 0.75, perNoCor = 0.75, perFixOmega = 0.1, perFixResid = 0.1, compress = TRUE, rxControl = NULL, sigdig = NULL, sigdigTable = NULL, ci = 0.95, muRefCov = TRUE, muRefCovAlg = TRUE, handleUninformativeEtas = TRUE, ... )
seed |
Random Seed for SAEM step. (Needs to be set for reproducibility.) By default this is 99. |
nBurn |
Number of iterations in the first phase, ie the MCMC/Stochastic Approximation
steps. This is equivalent to Monolix's |
nEm |
Number of iterations in the Expectation-Maximization
(EM) Step. This is equivalent to Monolix's |
nmc |
Number of Markov Chains. By default this is 3. When
you increase the number of chains the numerical integration by
MC method will be more accurate at the cost of more
computation. In Monolix this is equivalent to |
nu |
This is a vector of 3 integers. They represent the
numbers of transitions of the three different kernels used in
the Hasting-Metropolis algorithm. The default value is The first value represents the initial number of multi-variate Gibbs samples are taken from a normal distribution. The second value represents the number of uni-variate, or multi- dimensional random walk Gibbs samples are taken. The third value represents the number of bootstrap/reshuffling or uni-dimensional random samples are taken. |
print |
The number it iterations that are completed before anything is printed to the console. By default, this is 1. |
trace |
An integer indicating if you want to trace(1) the SAEM algorithm process. Useful for debugging, but not for typical fitting. |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of each individual's gradient cross-product (evaluated at the individual empirical Bayes estimates). " " " " " "" Does not calculate the covariance step. |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
logLik |
boolean indicating that log-likelihood should be calculate by Gaussian quadrature. |
nnodesGq |
number of nodes to use for the Gaussian quadrature when computing the likelihood with this method (defaults to 1, equivalent to the Laplacian likelihood) |
nsdGq |
span (in SD) over which to integrate when computing the likelihood by Gaussian quadrature. Defaults to 3 (eg 3 times the SD) |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
tol |
This is the tolerance for the regression models used for complex residual errors (ie add+prop etc) |
itmax |
This is the maximum number of iterations for the regression models used for complex residual errors. The number of iterations is itmax*number of parameters |
type |
indicates the type of optimization for the residuals; Can be one of c("nelder-mead", "newuoa") |
powRange |
This indicates the range that powers can take for residual errors; By default this is 10 indicating the range is c(-10, 10) |
lambdaRange |
This indicates the range that Box-Cox and Yeo-Johnson parameters are constrained to be; The default is 3 indicating the range c(-3,3) |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
perSa |
This is the percent of the time the 'nBurn' iterations in phase runs runs a simulated annealing. |
perNoCor |
This is the percentage of the MCMC phase of the SAEM algorithm where the variance/covariance matrix has no correlations. By default this is 0.75 or 75 Monte-carlo iteration. |
perFixOmega |
This is the percentage of the 'nBurn' phase where the omega values are unfixed to allow better exploration of the likelihood surface. After this time, the omegas are fixed during optimization. |
perFixResid |
This is the percentage of the 'nBurn' phase where the residual components are unfixed to allow better exploration of the likelihood surface. |
compress |
Should the object have compressed items |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
sigdig |
Specifies the "significant digits" that the ode
solving requests. When specified this controls the relative and
absolute tolerances of the ODE solvers. By default the tolerance
is |
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
muRefCov |
This controls if mu-referenced covariates in 'saem' are handled differently than non mu-referenced covariates. When 'TRUE', mu-referenced covariates have special handling. When 'FALSE' mu-referenced covariates are treated the same as any other input parameter. |
muRefCovAlg |
This controls if algebraic expressions that can be mu-referenced are treated as mu-referenced covariates by: 1. Creating a internal data-variable 'nlmixrMuDerCov#' for each algebraic mu-referenced expression 2. Change the algebraic expression to 'nlmixrMuDerCov# * mu_cov_theta' 3. Use the internal mu-referenced covariate for saem 4. After optimization is completed, replace 'model()' with old 'model()' expression 5. Remove 'nlmixrMuDerCov#' from nlmix2 output In general, these covariates should be more accurate since it changes the system to a linear compartment model. Therefore, by default this is 'TRUE'. |
handleUninformativeEtas |
boolean that tells nlmixr2's saem to calculate uninformative etas and handle them specially (default is 'TRUE'). |
... |
Additional arguments passed to |
List of options to be used in nlmixr2
fit for
SAEM.
Wenping Wang & Matthew L. Fidler
Other Estimation control:
foceiControl()
,
nlmixr2NlmeControl()
Set/get Objective function type for a nlmixr2 object
setOfv(x, type)
setOfv(x, type)
x |
nlmixr2 fit object |
type |
Type of objective function to use for AIC, BIC, and $objective |
Nothing
Matthew L. Fidler
Output table/data.frame options
tableControl( npde = NULL, cwres = NULL, nsim = 300, ties = TRUE, censMethod = c("truncated-normal", "cdf", "ipred", "pred", "epred", "omit"), seed = 1009, cholSEtol = (.Machine$double.eps)^(1/3), state = TRUE, lhs = TRUE, eta = TRUE, covariates = TRUE, addDosing = FALSE, subsetNonmem = TRUE, cores = NULL, keep = NULL, drop = NULL )
tableControl( npde = NULL, cwres = NULL, nsim = 300, ties = TRUE, censMethod = c("truncated-normal", "cdf", "ipred", "pred", "epred", "omit"), seed = 1009, cholSEtol = (.Machine$double.eps)^(1/3), state = TRUE, lhs = TRUE, eta = TRUE, covariates = TRUE, addDosing = FALSE, subsetNonmem = TRUE, cores = NULL, keep = NULL, drop = NULL )
npde |
When TRUE, request npde regardless of the algorithm used. |
cwres |
When TRUE, request CWRES and FOCEi likelihood regardless of the algorithm used. |
nsim |
represents the number of simulations. For rxode2, if
you supply single subject event tables (created with
|
ties |
When 'TRUE' jitter prediction-discrepancy points to discourage ties in cdf. |
censMethod |
Handle censoring method: - '"truncated-normal"' Simulates from a truncated normal distribution under the assumption of the model and censoring. - '"cdf"' Use the cdf-method for censoring with npde and use this for any other residuals ('cwres' etc) - '"omit"' omit the residuals for censoring |
seed |
an object specifying if and how the random number generator should be initialized |
cholSEtol |
The tolerance for the 'rxode2::choleSE' function |
state |
is a Boolean indicating if 'state' values will be included (default 'TRUE') |
lhs |
is a Boolean indicating if remaining 'lhs' values will be included (default 'TRUE') |
eta |
is a Boolean indicating if 'eta' values will be included (default 'TRUE') |
covariates |
is a Boolean indicating if covariates will be included (default 'TRUE') |
addDosing |
Boolean indicating if the solve should add rxode2
EVID and related columns. This will also include dosing
information and estimates at the doses. Be default, rxode2
only includes estimates at the observations. (default
|
subsetNonmem |
subset to NONMEM compatible EVIDs only. By
default |
cores |
Number of cores used in parallel ODE solving. This
is equivalent to calling |
keep |
is the keep sent to the table |
drop |
is the dropped variables sent to the table |
If you ever want to add CWRES/FOCEi objective function you can use the addCwres
If you ever want to add NPDE/EPRED columns you can use the addNpde
A list of table options for nlmixr2
Matthew L. Fidler
Produce trace-plot for fit if applicable
traceplot(x, ...)
traceplot(x, ...)
x |
fit object |
... |
Additional arguments passed to |
Fit traceplot or nothing.
Rik Schoemaker, Wenping Wang & Matthew L. Fidler
library(nlmixr2est) ## The basic model consiss of an ini block that has initial estimates one.compartment <- function() { ini({ tka <- 0.45 # Log Ka tcl <- 1 # Log Cl tv <- 3.45 # Log V eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) # and a model block with the error sppecification and model specification model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) d/dt(depot) = -ka * depot d/dt(center) = ka * depot - cl / v * center cp = center / v cp ~ add(add.sd) }) } ## The fit is performed by the function nlmixr/nlmix2 specifying the model, data and estimate fit <- nlmixr2(one.compartment, theo_sd, est="saem", saemControl(print=0)) # This shows the traceplot of the fit (useful for saem) traceplot(fit)
library(nlmixr2est) ## The basic model consiss of an ini block that has initial estimates one.compartment <- function() { ini({ tka <- 0.45 # Log Ka tcl <- 1 # Log Cl tv <- 3.45 # Log V eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) # and a model block with the error sppecification and model specification model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) d/dt(depot) = -ka * depot d/dt(center) = ka * depot - cl / v * center cp = center / v cp ~ add(add.sd) }) } ## The fit is performed by the function nlmixr/nlmix2 specifying the model, data and estimate fit <- nlmixr2(one.compartment, theo_sd, est="saem", saemControl(print=0)) # This shows the traceplot of the fit (useful for saem) traceplot(fit)
Control for uobyqa estimation method in nlmixr2
uobyqaControl( npt = NULL, rhobeg = NULL, rhoend = NULL, iprint = 0L, maxfun = 100000L, returnUobyqa = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
uobyqaControl( npt = NULL, rhobeg = NULL, rhoend = NULL, iprint = 0L, maxfun = 100000L, returnUobyqa = FALSE, stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), useColor = crayon::has_color(), printNcol = floor((getOption("width") - 23)/12), print = 1L, normType = c("rescale2", "mean", "rescale", "std", "len", "constant"), scaleType = c("nlmixr2", "norm", "mult", "multAdd"), scaleCmax = 1e+05, scaleCmin = 1e-05, scaleC = NULL, scaleTo = 1, rxControl = NULL, optExpression = TRUE, sumProd = FALSE, literalFix = TRUE, addProp = c("combined2", "combined1"), calcTables = TRUE, compress = TRUE, covMethod = c("r", ""), adjObf = TRUE, ci = 0.95, sigdig = 4, sigdigTable = NULL, ... )
npt |
The number of points used to approximate the objective function via a quadratic approximation for bobyqa. The value of npt must be in the interval [n+2,(n+1)(n+2)/2] where n is the number of parameters in par. Choices that exceed 2*n+1 are not recommended. If not defined, it will be set to 2*n + 1. (bobyqa) |
rhobeg |
Beginning change in parameters for bobyqa algorithm (trust region). By default this is 0.2 or 20 parameters when the parameters are scaled to 1. rhobeg and rhoend must be set to the initial and final values of a trust region radius, so both must be positive with 0 < rhoend < rhobeg. Typically rhobeg should be about one tenth of the greatest expected change to a variable. Note also that smallest difference abs(upper-lower) should be greater than or equal to rhobeg*2. If this is not the case then rhobeg will be adjusted. (bobyqa) |
rhoend |
The smallest value of the trust region radius that is allowed. If not defined, then 10^(-sigdig-1) will be used. (bobyqa) |
iprint |
The value of 'iprint' should be set to an integer value in '0, 1, 2, 3, ...', which controls the amount of printing. Specifically, there is no output if 'iprint=0' and there is output only at the start and the return if 'iprint=1'. Otherwise, each new value of 'rho' is printed, with the best vector of variables so far and the corresponding value of the objective function. Further, each new value of the objective function with its variables are output if 'iprint=3'. If 'iprint > 3', the objective function value and corresponding variables are output every 'iprint' evaluations. Default value is '0'. |
maxfun |
The maximum allowed number of function evaluations. If this is exceeded, the method will terminate. |
returnUobyqa |
return the uobyqa output instead of the nlmixr2 fit |
stickyRecalcN |
The number of bad ODE solves before reducing the atol/rtol for the rest of the problem. |
maxOdeRecalc |
Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve. |
odeRecalcFactor |
The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced |
useColor |
Boolean indicating if focei can use ASCII color codes |
printNcol |
Number of columns to printout before wrapping parameter estimates/gradient |
print |
Integer representing when the outer step is printed. When this is 0 or do not print the iterations. 1 is print every function evaluation (default), 5 is print every 5 evaluations. |
normType |
This is the type of parameter
normalization/scaling used to get the scaled initial values
for nlmixr2. These are used with With the exception of In general, all all scaling formula can be described by:
= (
)/
Where The other data normalization approaches follow the following formula
= (
)/
|
scaleType |
The scaling scheme for nlmixr2. The supported types are:
|
scaleCmax |
Maximum value of the scaleC to prevent overflow. |
scaleCmin |
Minimum value of the scaleC to prevent underflow. |
scaleC |
The scaling constant used with
These parameter scaling coefficients are chose to try to keep similar slopes among parameters. That is they all follow the slopes approximately on a log-scale. While these are chosen in a logical manner, they may not always apply. You can specify each parameters scaling factor by this parameter if you wish. |
scaleTo |
Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed. |
rxControl |
'rxode2' ODE solving options during fitting, created with 'rxControl()' |
optExpression |
Optimize the rxode2 expression to speed up calculation. By default this is turned on. |
sumProd |
Is a boolean indicating if the model should change
multiplication to high precision multiplication and sums to
high precision sums using the PreciseSums package. By default
this is |
literalFix |
boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'. |
addProp |
specifies the type of additive plus proportional errors, the one where standard deviations add (combined1) or the type where the variances add (combined2). The combined1 error type can be described by the following equation:
The combined2 error model can be described by the following equation:
Where: - y represents the observed value - f represents the predicted value - a is the additive standard deviation - b is the proportional/power standard deviation - c is the power exponent (in the proportional case c=1) |
calcTables |
This boolean is to determine if the foceiFit
will calculate tables. By default this is |
compress |
Should the object have compressed items |
covMethod |
Method for calculating covariance. In this discussion, R is the Hessian matrix of the objective function. The S matrix is the sum of individual gradient cross-product (evaluated at the individual empirical Bayes estimates).
|
adjObf |
is a boolean to indicate if the objective function
should be adjusted to be closer to NONMEM's default objective
function. By default this is |
ci |
Confidence level for some tables. By default this is 0.95 or 95% confidence. |
sigdig |
Optimization significant digits. This controls:
|
sigdigTable |
Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3. |
... |
Additional arguments passed to |
uobyqa control structure
Matthew L. Fidler
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="uobyqa") print(fit2) # you can also get the nlm output with fit2$nlm fit2$uobyqa # The nlm control has been modified slightly to include # extra components and name the parameters
# A logit regression example with emax model dsn <- data.frame(i=1:1000) dsn$time <- exp(rnorm(1000)) dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time))) mod <- function() { ini({ E0 <- 0.5 Em <- 0.5 E50 <- 2 g <- fix(2) }) model({ v <- E0+Em*time^g/(E50^g+time^g) ll(bin) ~ DV * v - log(1 + exp(v)) }) } fit2 <- nlmixr(mod, dsn, est="uobyqa") print(fit2) # you can also get the nlm output with fit2$nlm fit2$uobyqa # The nlm control has been modified slightly to include # extra components and name the parameters
VPC based on ui model
vpcCens(..., cens = TRUE, idv = "time")
vpcCens(..., cens = TRUE, idv = "time")
... |
Additional arguments passed to |
cens |
is a boolean to show if this is a censoring plot or
not. When |
idv |
Name of independent variable. For |
Simulated dataset (invisibly)
Matthew L. Fidler
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
VPC based on ui model
vpcCensTad(..., cens = TRUE, idv = "tad")
vpcCensTad(..., cens = TRUE, idv = "tad")
... |
Additional arguments passed to |
cens |
is a boolean to show if this is a censoring plot or
not. When |
idv |
Name of independent variable. For |
Simulated dataset (invisibly)
Matthew L. Fidler
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
VPC based on ui model
vpcPlot( fit, data = NULL, n = 300, bins = "jenks", n_bins = "auto", bin_mid = "mean", show = NULL, stratify = NULL, pred_corr = FALSE, pred_corr_lower_bnd = 0, pi = c(0.05, 0.95), ci = c(0.05, 0.95), uloq = fit$dataUloq, lloq = fit$dataLloq, log_y = FALSE, log_y_min = 0.001, xlab = NULL, ylab = NULL, title = NULL, smooth = TRUE, vpc_theme = NULL, facet = "wrap", scales = "fixed", labeller = NULL, vpcdb = FALSE, verbose = FALSE, ..., seed = 1009, idv = "time", cens = FALSE )
vpcPlot( fit, data = NULL, n = 300, bins = "jenks", n_bins = "auto", bin_mid = "mean", show = NULL, stratify = NULL, pred_corr = FALSE, pred_corr_lower_bnd = 0, pi = c(0.05, 0.95), ci = c(0.05, 0.95), uloq = fit$dataUloq, lloq = fit$dataLloq, log_y = FALSE, log_y_min = 0.001, xlab = NULL, ylab = NULL, title = NULL, smooth = TRUE, vpc_theme = NULL, facet = "wrap", scales = "fixed", labeller = NULL, vpcdb = FALSE, verbose = FALSE, ..., seed = 1009, idv = "time", cens = FALSE )
fit |
nlmixr2 fit object |
data |
this is the data to use to augment the VPC fit. By
default is the fitted data, (can be retrieved by
|
n |
Number of VPC simulations |
bins |
either "density", "time", or "data", "none", or one of the approaches available in classInterval() such as "jenks" (default) or "pretty", or a numeric vector specifying the bin separators. |
n_bins |
when using the "auto" binning method, what number of bins to aim for |
bin_mid |
either "mean" for the mean of all timepoints (default) or "middle" to use the average of the bin boundaries. |
show |
what to show in VPC (obs_dv, obs_ci, pi, pi_as_area, pi_ci, obs_median, sim_median, sim_median_ci) |
stratify |
character vector of stratification variables. Only 1 or 2 stratification variables can be supplied. |
pred_corr |
perform prediction-correction? |
pred_corr_lower_bnd |
lower bound for the prediction-correction |
pi |
simulated prediction interval to plot. Default is c(0.05, 0.95), |
ci |
confidence interval to plot. Default is (0.05, 0.95) |
uloq |
Number or NULL indicating upper limit of quantification. Default is NULL. |
lloq |
Number or NULL indicating lower limit of quantification. Default is NULL. |
log_y |
Boolean indicting whether y-axis should be shown as logarithmic. Default is FALSE. |
log_y_min |
minimal value when using log_y argument. Default is 1e-3. |
xlab |
label for x axis |
ylab |
label for y axis |
title |
title |
smooth |
"smooth" the VPC (connect bin midpoints) or show bins as rectangular boxes. Default is TRUE. |
vpc_theme |
theme to be used in VPC. Expects list of class vpc_theme created with function vpc_theme() |
facet |
either "wrap", "columns", or "rows" |
scales |
either "fixed" (default), "free_y", "free_x" or "free" |
labeller |
ggplot2 labeller function to be passed to underlying ggplot object |
vpcdb |
Boolean whether to return the underlying vpcdb rather than the plot |
verbose |
show debugging information (TRUE or FALSE) |
... |
Additional arguments passed to |
seed |
an object specifying if and how the random number generator should be initialized |
idv |
Name of independent variable. For |
cens |
is a boolean to show if this is a censoring plot or
not. When |
Simulated dataset (invisibly)
Matthew L. Fidler
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
VPC based on ui model
vpcPlotTad(..., idv = "tad")
vpcPlotTad(..., idv = "tad")
... |
Additional arguments passed to |
idv |
Name of independent variable. For |
Simulated dataset (invisibly)
Matthew L. Fidler
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
one.cmt <- function() { ini({ tka <- 0.45; label("Ka") tcl <- log(c(0, 2.7, 100)); label("Cl") tv <- 3.45; label("V") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label("Additive residual error") }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr2est::nlmixr( one.cmt, data = nlmixr2data::theo_sd, est = "saem", control = list(print = 0) ) vpcPlot(fit)
VPC simulation
vpcSim( object, ..., keep = NULL, n = 300, pred = FALSE, seed = 1009, nretry = 50, minN = 10, normRelated = TRUE )
vpcSim( object, ..., keep = NULL, n = 300, pred = FALSE, seed = 1009, nretry = 50, minN = 10, normRelated = TRUE )
object |
This is the nlmixr2 fit object |
... |
Additional arguments passed to |
keep |
Column names to keep in the output simulated dataset |
n |
Number of simulations |
pred |
Should predictions be added to the simulation |
seed |
Seed to set for the VPC simulation |
nretry |
Number of times to retry the simulation if there is NA values in the simulation |
minN |
With retries, the minimum number of studies to restimulate (by default 10) |
normRelated |
should the VPC style simulation be for normal related variables only |
data frame of the VPC simulation
Matthew L. Fidler
if (rxode2::.linCmtSensB()) { one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr(one.cmt, theo_sd, est="focei") head(vpcSim(fit, pred=TRUE)) }
if (rxode2::.linCmtSensB()) { one.cmt <- function() { ini({ ## You may label each parameter with a comment tka <- 0.45 # Log Ka tcl <- log(c(0, 2.7, 100)) # Log Cl ## This works with interactive models ## You may also label the preceding line with label("label text") tv <- 3.45; label("log V") ## the label("Label name") works with all models eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7 }) model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) linCmt() ~ add(add.sd) }) } fit <- nlmixr(one.cmt, theo_sd, est="focei") head(vpcSim(fit, pred=TRUE)) }