Allows $etaH and related family to be integrated
into a saem fit if cwres is
calculated.
Fixed a bug where nlmixrLlikObs in the merged
dataset is sometimes named llikObs, now it is always named
nlmixrLlikObs
Fixed a bug where nlmixrLlikObs shows up in merged
dataset when cwres is not calculated (it was always
0), also allow cwres calculation to pick up
nlmixrLlikObs in merged dataset.
Dropped dparser dependency
Fixes $etaH memory corruption so the standard errors
of etas are now correct
Removed the memory requirements for focei by
neta*neta*nsub
Fixed character based covariates so the work correctly (again) with focei. Added a test for this as well.
Fixes $dataMergeInner so that observation-based
log-likelihoods work with infusions. Should fix tests with
ggPMX
Fixes $etaSE and $etaRSE to work
correctly when there is only 1 eta.
Fixes npde valgrind observed on CRAN machines
Gill forward differences will not repeat now (by default), You
can change back to prior behavior with
foceiControl(repeatGillMax=3)
Number of sticky recalculation is reduced to 4; to have the old
behavior use foceiControl(stickyRecalcN=5)
n2ll has been changed to ll to specify
individual log-likelihoods. This was only used in simulation and was not
well documented.
Generalized log-likelihood is only supported with
rxode2 2.0.8 or later.
The S matrix calculation was made a bit more robust
to errors in individual gradients. When there are errors in the
individual gradient calculation, assume the gradient is the same as the
overall gradient. In the tests cases, were reasonable using this
adjusted S matrix. This means if some individuals do not have very much
data to support a specific parameter, a S matrix
calculation for the population will still be generated. When there is
some patients/subject combinations that do not have sufficient data, we
will add the following to the run information:
S matrix had problems solving for some subject and parameters.
The S matrix calculation will still fail if the percentage
of parameters that are being reset is lower than
foceiControl(smatPer=0.6) or whatever you specify.
The r,s covariance matrix will now also check for
unreasonably small values (controlled by
foceiControl(covSmall=...)) and select a different
covariance estimate method even when the “r” and “s” matrices are
calculated “correctly”.
What type(s) censoring (if any) is now stored in
fit$censInformation
Standard errors of $etas can now be obtained with
fit$phiSE, also available are fit$phiRSE
(relative standard error), fit$phiH, (individual hessian),
fit$phiC (individual covariances), fit$phiR
(individual correlation matrices)
Can also use Shi 2021 differences in addition to Gill differences. In our tests (using the same datasets as CPT) these produced worse estimates than the Gill 1983, though it is unclear why since it should be a faster more accurate method. A modified version is used in calculating the individual Hessians of numerically for the generalized likelihood approach.
Generalized likelihood estimation is now present in
nlmixr2est for focei, foce and
posthoc
nmNearPD() is a function you may use for nearest
positive definite matrix. This is derived from
Matrix::nearPD() but is implemented in C/C++ to be used in
(possibly threaded) optimization.
Individual Hessians can be accessed by $phiH,
covariance by $phiC, eta standard errors by
$phiSE and eta RSEs can be accessed by
$phiRSE. There are eta aliases for these as
well ($etaH, $etaC, $etaSE, and
$etaRSE).
Can now access the individual point’s contribution to the overall
likelihood when merging to the original dataset. These merges can be
accessed with $dataMergeFull, $dataMergeLeft,
$dataMergeRight, and $dataMergeInner. The
columns with the individual data column is
nlmixrLlikObs.
To calculate the total focei/foce objective
function, the sum of the likelihoods still need to be adjusted by the
omega/eta contribution, and the individual Hessians, and possibly the
NONMEM objective function offset constant.
cens and limit do not produce the correct
table output (#180)bobyqa by default. With this, it is more important to
examine the model parameters and fits for plausibility.pd/npd as an output as well as
npd/npdenlmixr2 “saem” fit from another R
session, nlmixr2 will no longer crash with
fit$objfNPDE was identical to NPD even with
correlated models, this was fixed (prior output was actually
NPDE).Added ui$getSplitMuModel which is used in
babelmixr2 and will be used in the refined stepwise
covariate selection of nlmixr2extra
Added work-around to remove
_nlmixr2est_RcppExport_registerCCallable since the
registering of C callable are handled manually at the moment.
Use .zeros() for the matrices in armadillo in
addition to relying on calloc to give zero
matrices.
Fixed one uninitialized object
Fix for augPred so it works on population only
models
nlme no longer sets options to treat all covariates
as non mu-referenced covariates, but directly calls a function that can
turn on or off the mu-reference covariate selection.
vpcSim now tries to simulate IDs that didn’t
simulate correctly (with a warning)
Export nmObjHandleControlObject
nlmixr2est contains the estimation functions within
nlmixr2.
Remove lower level foceiFit function. Focei, foce,
fo, foi, and posthoc now directly takes rxode2 ui objects
New error types are supported in focei including mixing theta and etas in residual errors and different types of proportional errors
Different types of additive and proportional errors can be used
for each endpoint using + combined1() or
+ combined2() otherwise it takes the supplied
addProp option to figure out which type of combined model
is run (by default combined2())
Focei model cache is now named focei-md5Digest.qs
and uses qs compression/saving/loading.
foceiControl() aligned between other
methods.
foceiControl(adjLik=TRUE) uses the NONMEM-style
objective function throughout. foceiControl(adjLik=FALSE)
uses the adjusted objective function throughout, and adjusts it back to
the NONMEM objective function.
Lag time and other between subject variability differences no longer calculate an ideal relative step size, but an absolute step size when using Gill differences (default)
Objective function checks for infinite/NaN/NA values for the entire solving space and ensures no overflow occurs when calculating the inner hessian
mu referencing is no longer required for saem;
Internally non mu-referenced values are converted to mu referenced
values and the converted back when calculating the nlmixr2
object.
nlmixr2 forced the parameter ordering to (1)
population effects,
nlmixr2
sees the parameters. Since this is based on a random number generator,
the optimization trajectory will be different and have different results
than nlmixrComponents of omega can now be fixed.
Residual error components can also be fixed.
When optimizing only one residual value, nlmixr2’s saem uses
nlm from R, which is more efficient than the nealder-meade
method.
Lower level saem functions (like
configsaem()) are not exported because they are
increasingly difficult to use and convert to something standard; a few
methods (like print, summary etc) are
maintained to view the lower level object and for debugging it.
Parameter history and print-out no longer includes fixed parameters.
The model to calculate the residuals more closely matches the model used for estimation to remove small rounding differences that may occur in the models.
Different types of additive and proportional errors can be used
for each endpoint using + combined1() or
+ combined2() otherwise it takes the supplied
addProp option to figure out which type of combined model
is run (by default combined2())
Parameter history and printout now uses standard deviation for additive only components, matching the estimation of the components.
rxode2 solving options are now saved in the
rxControl part of the saemControl(). That is
saemControl(rxControl=rxControl(...)); This fixes any
conflicting option names as well as allowing alignment between the
control structures in focei, nlme and
saem
saemControl() aligned between other
methods.
nlme has been completely rewritten to directly run
from the rxode2 UI
nlme always tries to use mu-referencing (when
available)
Internally nlme now uses parallel processing for
solving so it should be faster.
nlmixr2NlmeControl() (which will overwrite
nlmeControl()) documents and adds more options to
nlme. Also aligned with other methods.
weights, fixed, random can
be specified in nlmixr2NlmeControl(). If so, then the
nlme object will be returned.
returnNlme is a new option that will return the
nlme object instead of the traditional nlme
object.
nlme_ode and lme_lin_cmpt are both
removed.
rxode2 solving options are now saved in the
rxControl part of the saemControl(). That is
nlmeControl(rxControl=rxControl(...)); This fixes any
conflicting option names as well as allowing alignment between the
control structures in focei, nlme and
saem
With saem, the nlmixr2 function now saves/compresses
the phiM information. This means the gaussian and
Laplacians likelihoods can be calculated when you save the nlmixr object
and then restore it later.
The nlmixr2 object compresses infrequently used and removes many
unneeded objects. Even with compression, the saem objects
are often a bit bigger since they include the large phiM
object.
nlmixr2 now supports non-mu referenced ETAs in the
fit$parFixed and fit$parFixedDf
nlmixr2 interface changed to use rxode2
UI
keep and drop are added to
tableControl to influence the end data-frame
$simInfo uses a quoted expression for
$rx instead of a string
$simInfo$sigma is a diagonal matrix since now the
normal simulation is controlled by the variability modeled as a
population value.
nlmixr2 now allows etas that have initial omega
estimates of zero to be dropped from the model (instead of issuing an
error about a non-positive definite $omega matrix)
addNpde(fit, table=tableControl(nsim=500))vpc function rewritten and split out to
vpcSim() and vpcPlot() (which is a replacement
for vpc()).
There were too many mismatches between vpc::vpc and
nlmixr::vpc which caused inconsistencies in code based on
load order of vpc and nlmixr. This way both
coexist, and you can use the vpc simulation for other
packages more easily (like ggPMX) without creating or
summarizing data since ggPMX has its own methods for
summarizing and creating plots.
VPC now directly uses rxode2::rxSolve
augPred() has been written to use the new fit
object.
nlmixr2AugPred was changed to
nlmixr2AugPredSolve()
augPred uses the new interface and supports multiple
endpoints. The endpoint name is now always on the
plot(augPred(fit)).
fit$est,
and now getFitMethod(fit) simply returns
fit$estMany methods lower level utility functions have been deleted.
nmDocx, nmLst and nmSave
have been removed.
function 'rx_0ba247452048de33b1ffb8af516714fc__calc_lhs' not provided by package 'rx_0ba247452048de33b1ffb8af516714fc_'
would cause the estimation to stop. Now rxode2::rxClean()
is run when this occurs.