imputeMissing: imputeMissing: Impute missing phenotypes/covariates

In mauriziopaul/treatmentResponseDiallel: Package For the Analysis of Treatment-Response Phenotypes in the Influenza Diallel Project

Description

Impute missing phenotype(s)/covariate, with selective censoring, from specific diallel categories based on posteriors.

Usage

imputeMissing(dat, phenotype, covariate, postPred, postPred.cov, effectEst,
  SigmaEst, BlockEst, FixedEst, effectEst.cov, SigmaEst.cov, BlockEst.cov,
  toImpute, toCensor, colsToCopy, numImps, savedir, ...)

Arguments

dat	original data set
phenotype	the phenotype column name
covariate	the covariate column name
postPred	posterior predictive data for phenotype
postPred.cov	posterior predictive data for covariate
effectEst	posterior effect estimates for phenotype
SigmaEst	posterior sigma estimates for phenotype
BlockEst	posterior block estimates for phenotype
FixedEst	posterior fixed estimates for phenotype
effectEst.cov	posterior effect estimates for covariate
SigmaEst.cov	posterior sigma estimates for covariate
BlockEst.cov	posterior block estimates for covariate
toImpute	categories for imputation
toCensor	categories for censoring
colsToCopy	data columns from imputation frame to copy
numImps	number of timesteps to select from total (# imputed datasets to generate)
savedir	the directory the files are saved in
...	additional arguments

Value

generates (n=timestepLen) imputed datasets, returns vector of file names

Examples

## not run

mauriziopaul/treatmentResponseDiallel documentation built on May 21, 2019, 1:37 p.m.