Certara.RsNLME (version 3.1.0.1)

stepwiseSearch: Executes an NLME stepwise covariate search

Description

Executes an NLME stepwise covariate search

Usage

stepwiseSearch(
  model,
  hostPlatform = NULL,
  params,
  covariateModel,
  stepwiseParams,
  runInBackground = FALSE,
  ...
)

Value

if runInBackground = FALSE, a data frame is returned with stepwise search results, i.e. "Overall" comma separated file. Otherwise the StepwiseNlmeJob class object is returned.

Arguments

model

PK/PD model class object.

hostPlatform

Host definition for model execution. See hostParams. If missing, multicore local host with 4 threads is used.

params

Engine parameters. See engineParams. If missing, default parameters generated by engineParams(model) are used.

covariateModel

Covariate Effects Model providing the relationship between covariates and structural parameters to test (covariateModel(model)).

stepwiseParams

Stepwise parameters defining decision tree. See StepwiseParams

runInBackground

Set to TRUE to run in background and return prompt.

...

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., ....

See Also

hostParams, engineParams

Examples

if (FALSE) {
# Define the model
model <- pkmodel(numCompartments = 1,
                 data = pkData,
                 ID = "Subject",
                 Time = "Act_Time",
                 A1 = "Amount",
                 CObs = "Conc",
                 workingDir = tempdir())

# Add Gender covariate of type categorical
model <- addCovariate(model,
                      covariate = "Gender",
                      type = "Categorical",
                      effect = c("V", "Cl"),
                      levels = c(0, 1),
                      labels = c("Female", "Male"))

# Add Bodyweight covariate of type continuous
model <- addCovariate(model,
             covariate = "BodyWeight",
             type = "Continuous",
             direction = "Backward",
             center = "Mean",
             effect = c("V", "Cl"))

# Define the host
defaultHost <- hostParams(parallelMethod = "MULTICORE",
                   hostName = "local",
                   numCores = 8,
                   sharedDirectory = tempdir())

# Define the engine parameters
params <- engineParams(model, numIterations = 6)

# Define covariate model
cp <- covariateModel(model)

# Define the stepwise parameters
sp <- StepwiseParams(0.01, 0.001, "-2LL")

# Perform stepwise search
OverallDF <-  stepwiseSearch(model = model,
                      hostPlatform = defaultHost,
                      params = params,
                      covariateModel = cp,
                      stepwiseParams = sp,
                      runInBackground = FALSE)
}