fitmodel.Rd
Executes an NLME simple estimation
fitmodel(
model,
hostPlatform = NULL,
params,
simpleTables,
runInBackground = FALSE,
filesToReturn = "*",
...
)
PK/PD model class object.
Host definition for model execution. See hostParams
.
If missing
, 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.
Engine parameters. See engineParams
.
If missing
, default parameters generated by engineParams(model) are used.
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.
Set to TRUE
to run in background and return prompt.
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 the Value
section can be specified.
Simple regex patterns are supported for the specification.
Additional arguments for hostParams
or arguments available inside engineParams
functions.
If engineParams
arguments are supplied through both params
argument
and additional argument (i.e., ellipsis), then the arguments in params
will be ignored
and only the additional arguments will be used with warning.
If hostParams
arguments are supplied through both the hostPlatform
argument and the ellipses, values supplied to hostPlatform
will be overridden by
additional arguments supplied via the ellipses e.g., ...
.
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.
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
if (FALSE) {
# 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)
}