Saem non-mu reference input parameters/covariates were fixed so they work correctly with fixed parameters (Issue #445)
Focei changed back to having a lower bound for standard deviations when not specified. This means that best model fits may change. You can revert to the old settings by using foceiControl(sdLowerFact=0.0)
. You can also change the factors to other values than the default value, that is foceiControl(sdLowerFact=0.000001)
for instance which would multiply the initial value by 0.000001
when either the lower bound isn’t specified or the lower bound is specified as zero for the error estimates related to error-based standard deviations.
In nlmixr2
, expressions are optimized. Because of that optimization, numerical rounding differences can cause different directions in optimization when fixing parameters in the model vs. fixing the parameters manually.
This means that the fixed parameters in a model vs hard-coded fixed parameters could give different values in the final model.
A new option literalFix
was introduced which change the fixed population parameters to constants in the model while running the optimization. This makes the output of fixing within the model and fixing manually the same (which is what is likely expected). The default is for this to be turned on (ie. literalFix=TRUE
). You can get back the old behavior by using the option literalFix=FALSE
.
In saem
, the monte-carlo sampling occurs for all parameters including non-informative ETAs. A fix ensure that non-informative etas in saem
are fixed to zero while sampling the phi
values. This may change results for models with uninformative etas. To ignore the uninformative etas with saem
you ca use use the prior saem
handling with saemControl(handleUninformativeEtas=FALSE)
.
Gracefully degrade when $cov is not in the right form (see #423)
Add support for PopED in place solving (used in babelmixr2)
If est=foceiControl()
or other nlmixr2 control with the class foceiControl
infer the estimation method is focei
focei
cache needs to be based on the parameter order as well as the model information (#415)Algebraic mu referencing has been implemented in nlme
and saem
.
New estimation method “nlm” has been added to estimate population only likelihoods using stats::nlm
and possibly return a standardized nlmixr2
fit.
New estimation method “nls” has been added to estimate population only problems. This uses minpack.lm::nlsNM
by default if present, or the stats::nls
New estimation method “optim” has been added to estimate population only likelihoods. This uses stats::optim
and returns a standardized nlmixr2
fit.
New estimation method “nlminb” has been added to estimate population only likelihoods. This uses stats::nlminb
and returns a standardized nlmixr2
fit.
New estimation methods from the minqa
package: “bobyqa”, “uobyqa” and “newuoa” have been added to estimate population only likelihoods. These methods returns a standardized nlmixr2
fit.
New estimation method “lbfgsb3c” to estimate population only likelihoods. This returns a standardized nlmixr2
fit.
New estimation method “n1qn1” to estimate population only likelihoods. This returns a standardized nlmixr2
fit.
Added new feature for vpcSim()
where a minimum number of subjects are simulated from the model when trying to fill in ODEs that were not solved successfully. By default this is 10
. This also works-around a bug when there is only one subject simulated and the data.frame
has a slightly different output.
Removed fit$saemTransformedData
since it isn’t actually used in saem
anymore (but will break anyone’s code who is using it)
Now the internal function .foceiPreProcessData()
requires the rxode2 control rxControl()
because some of the new steady state lag features need to translate the data differently based on rxControl()
options.
Printing models with correlated omega values and omega values fixed to zero no longer fails (#359)
Add back values for $parHistData (#368)
This requires a new rxode2
which will fix multiple endpoint issues observed (#394)
Manual back-transformed values in $parFixed
are now displaying correctly and are calculated based on the confidence interval in the control instead of 95% confidence no matter what (#397)
as.rxUi()
method was added for fit models (#377)nlmixr2
models will crash R.As requested by CRAN, remove Rvmmin
Values in $parFixed
for BSV without exponential transformation are now correctly shown (#366)
rxode2
now allows simulation with omega
having diagonal zero elements, $omega
and $omegaR
now reflects this information including the zero omega elements in the output. On the other hand, the other eta-information and standard error information for zero etas are still excluded in $phiR
, $phiSE
, $eta
etc.vpcSim()
works when an eta value is fixed to 0 (#341)
augPred()
now consistently uses the simulation model (instead of the inner model used for CWRES
calculation).
ucminf
Add $fitMergeFull
, $fitMergInner
, $fitMergeLeft
, $fitMergeRight
as a complement to $dataMergeFull
, $dataMergInner
, $dataMergeLeft
, $dataMergeRight
. The fit variants prefer columns in the fit dataset instead of the original dataset. This is useful for goodness of fit plots with censoring since the DV
in the fit simulates values under the ipred/residual assumption and will give more appropriate goodness of fits, otherwise these values are the limit of whatever censoring is applied
Moved the mu reference fix for the split mu referenced model here (from babelmixr2)
Breaking change, now calculate condition number based on covariance and correlation, the names have changed to be more explicit. conditionNumber
changed to conditionNumberCov
and a new metric conditionNumberCor
has been added.
A bug in boundary value detection prevented automatic covariance calculation with FOCEi estimation (#318)
Fix vpcSim
so that it will be a bit more robust when it is difficult to simulate.
A bug in model piping which did not allow models to be appended to was fixed (rxode2#364)
An internal change was made in nlmixr2.rxUi()
to better support the babelmixr2 PKNCA estimation method (babelmixr2#75)
Fixed bug where $iniUi
did not return the initial ui when running non focei
related methods. Also added alias of $uiIni
to the same function.
Dropped Stan headers for this package, also updated to C++17
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 $dataMergeInner
. The columns with the individual data column is nlmixrLlikObs
.
To calculate the total focei
/foce
objective function, the sum of the likelihoods still need to be adjusted by the omega/eta contribution, and the individual Hessians, and possibly the NONMEM objective function offset constant.
cens
and limit
do not produce the correct table output (#180)bobyqa
by default. With this, it is more important to examine the model parameters and fits for plausibility.pd
/npd
as an output as well as npd
/npde
nlmixr2
“saem” fit from another R session, nlmixr2
will no longer crash with fit$objf
NPDE
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 nlmixr
Components 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$est
Many 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.