Executes an NLME simple estimation
fitmodel.Rd
Executes an NLME simple estimation
Usage
fitmodel(
model,
hostPlatform = NULL,
params,
simpleTables,
runInBackground = FALSE,
filesToReturn = "*",
...
)
Arguments
- model
PK/PD model class object.
- hostPlatform
Host definition for model execution. See
hostParams
. Ifmissing
, PhoenixMPIDir64 is given and MPI is installed, MPI local host with 4 threads is used. If MPI is not found, local host without parallelization is used.- params
Engine parameters. See
engineParams
. Ifmissing
, default parameters generated by engineParams(model) are used.- simpleTables
Optional list of simple tables. See
tableParams
. By default a table named 'posthoc.csv' is returned with structural parameters values for all source data rows.- runInBackground
Set to
TRUE
to run in background and return prompt.- filesToReturn
Used to specify which files to be outputted to the model directory and loaded as returned value. By default, all the applicable files listed in the
Value
section will be outputted to the model directory and loaded as returned value. Only those files listed in theValue
section can be specified. Simple regex patterns are supported for the specification.- ...
Additional arguments for
hostParams
or arguments available insideengineParams
functions. IfengineParams
arguments are supplied through bothparams
argument and additional argument (i.e., ellipsis), then the arguments inparams
will be ignored and only the additional arguments will be used with warning. IfhostParams
arguments are supplied through both thehostPlatform
argument and the ellipses, values supplied tohostPlatform
will be overridden by additional arguments supplied via the ellipses e.g.,...
.
Value
if runInBackground
is FALSE
, a list with main
resulted dataframes is returned:
Overall
ConvergenceData
residuals
Secondary
StrCovariate - if continuous covariates presented
StrCovariateCat - if categorical covariates presented
theta
posthoc table
posthocStacked table
Requested tables
nlme7engine.log
textual output is returned and loaded with the main information related to
fitting. dmp.txt
structure with the results of fitting (including LL by subject information)
is returned and loaded. These 2 files are returned and loaded irrespective of
filesToReturn
argument value.
For individual models, additional dataframe with partial derivatives is returned:
ParDer
For population models and the method specified is NOT Naive-Pooled
,
additional dataframes are returned:
omega
Eta
EtaStacked
EtaEta
EtaCov
EtaCovariate - if continuous covariates presented
EtaCovariateCat - if categorical covariates presented
bluptable.dat
If standard error computation was requested and it was successful, additional dataframes are returned:
thetaCorrelation
thetaCovariance
Covariance
omega_stderr
If nonparametric method was requested (numIterNonParametric
> 0) and
the method
specified in engineParams
is NOT Naive-Pooled
,
additional dataframes are returned:
nonParSupportResult
nonParStackedResult
nonParEtaResult
nonParOverallResult
if runInBackground
is TRUE
, only current status of job is returned.
filesToReturn
with Certara.Xpose.NLME
If filesToReturn
is used and "ConvergenceData.csv" and "residuals.csv"
are not in the patterns, these files won't be returned and loaded. These files
are essential for Certara.Xpose.NLME::xposeNlmeModel
and
Certara.Xpose.NLME::xposeNlme
functions. This makes impossible to
use the resulted object in Certara.Xpose.NLME
functions.
Non-loaded but returned files
The non-loaded but returned files in the model working directory are:
err1.txt - concatenated for all runs detailed logs for all steps of optimization,
out.txt - general pivoted information about results,
doses.csv - information about doses given for all subjects,
iniest.csv - information about initial estimates
Examples
if (FALSE) { # \dontrun{
# Define the host
host <- hostParams(parallelMethod = "None",
hostName = "local",
numCores = 1)
# Define the model
model <- pkmodel(numComp = 2,
absorption = "FirstOrder",
ID = "Subject",
Time = "Act_Time",
CObs = "Conc",
Aa = "Amount",
data = pkData,
modelName = "PkModel")
Table01 <- tableParams(name = "SimTableObs.csv",
timesList = "0,1,2,4,4.9,55.1,56,57,59,60",
variablesList = "C, CObs",
timeAfterDose = FALSE,
forSimulation = FALSE)
# Update fixed effects
model <- fixedEffect(model,
effect = c("tvV", "tvCl", "tvV2", "tvCl2"),
value = c(16, 41, 7, 14))
# Define the engine parameters
params <- engineParams(model)
# Fit model
res <- fitmodel(model = model,
hostPlatform = host,
params = params,
simpleTables = Table01)
} # }