View source: R/covariateSearch.R
covariateSearch  R Documentation 
Automatic search of the best covariate model. Automatic covariate model building is available directly in the lixoftConnectors package using the function
runModelBuilding
. Please migrate, as this function will be deprecated in the future.
Two methods for covariate model building are proposed
SCM: stepwise covariate modeling method In the forward selection, at each step, each of the remaining (i.e not yet included) parametercovariate relationships are added to the model in an univariate model (one model per relationship), and run. Among all models, the model that improves some criteria (LRT, BIC or AIC) most is selected and taken forward to the next step. During backward elimination, parametercovariate relationships are removed in an univariate manner.
COSSAC: COnditional Sampling for Stepwise Approach based on Correlation tests method COSSAC makes use of the information contained in the base model run to choose which covariate to try first (instead of trying all covariates "blindly" as in SCM). Indeed, the correlation between the individual parameters (or random effects) and the covariates hints at possibly relevant parametercovariate relationships. If the EBEs (empirical Bayes estimates) are used, shrinkage may bias the result. COSSAC instead uses samples from the a posteriori conditional distribution (available as "conditional distribution" task in MonolixSuite2018) to calculate the correlation between the random effects and covariates. A pvalue can be derived using the Pearson's correlation test for continuous covariate and ANOVA for categorical covariate. The pvalues are used to sort all the random effectcovariate relationships. Relationships with the lowest pvalue are added first, run and confirmed using a likelihood ratio test, AIC or BIC criteria.
covariateSearch(
project,
final.project = NULL,
method = NULL,
covToTest = NULL,
covToTransform = NULL,
paramToUse = NULL,
testRelations = NULL,
settings = NULL
)
project 
a Monolix project 
final.project 
[optional] string corresponding to the final Monolix project (default: 'runFinal.mlxtran' in covariate search output folder) 
method 
[optional] string correspondig to the method. It can be 'COSSAC' or 'SCM'. By default, COSSAC' is used. 
covToTest 
[optional] vector of covariates to test. Cannot be used if testRelations is defined. By default, all covariates are tested. 
covToTransform 
[optional] vector of covariates to transform. The transformation consists in a log transform of the covariate with centering by the mean value (ex: WT is transformed into log(WT/mean) with mean the mean WT value over the individuals of the data set). Both the transformed and untransformed covariate are tested by the algorithm. By default, no covariate is transformed. Note: adding a nontransformed covariate on a lognormally distributed parameter results in an exponential relationship: log(V) = log(Vpop) + beta*WT + eta <=> V = Vpop * exp(beta*WT) * exp(eta) adding a logtransformed covariate on a lognormally distributed parameter results in a power law relationship: log(V) = log(Vpop) + beta*log(WT/70) + eta <=> V = Vpop * (WT/70)^beta * exp(eta) 
paramToUse 
[optional] vector of parameters which may be function of covariates. Cannot be used if testRelations is defined. By default, all parameters are tested. 
testRelations 
[optional] list of parametercovariate relationships to test, ex: list(V=c("WT","SEX"),Cl=c("CRCL")). Cannot be used if covToTest or paramToUse is defined. By default, all parametercovariate relationships are tested. 
settings 
[optional] list of settings for the covariate search:

getModelBuildingSettings
settings for model building with lixoftConnectors
runModelBuilding
run model building with lixoftConnectors
getModelBuildingResults
results for model building with lixoftConnectors
## Not run:
# RsmlxDemo1.mlxtran is a Monolix project for modelling the pharmacokinetics (PK) of warfarin
# using a PK model with parameters ka, V, Cl.
# In this example, three covariates (wt, age, sex) are available with the data
# covariatesearch will compute the best covariate model, in term of BIC,
# for the three PK parameters using the three covariates.
r1 < covariateSearch(project="RsmlxDemo1.mlxtran")
# Instead of using the COSSAC method, we can use the SCM method:
r2 < covariateSearch(project="RsmlxDemo1.mlxtran", method = 'SCM')
# Here, the covariate model is built using age and wt only, for V and Cl only:
r3 < covariateSearch(project = "RsmlxDemo1.mlxtran",
paramToUse = c("V","Cl"),
covToTest = c("age","wt"))
## End(Not run)
# See http://monolix.lixoft.com/rsmlx/covariatesearch/ for detailed examples of covariatesearch
# Download the demo examples here: http://monolix.lixoft.com/rsmlx/installation
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