regressionImp {VIM} | R Documentation |
Impute missing values based on a regression model.
regressionImp(formula, data, family = "AUTO", robust = FALSE, imp_var = TRUE, imp_suffix = "imp", mod_cat = FALSE)
formula |
model formula to impute one variable |
data |
A data.frame or survey object containing the data |
family |
family argument for "glm" ("AUTO" tries to choose automatically, only really tested option!!!) |
robust |
TRUE/FALSE if robust regression should be used |
imp_var |
TRUE/FALSE if a TRUE/FALSE variables for each imputed variable should be created show the imputation status |
imp_suffix |
suffix used for TF imputation variables |
mod_cat |
TRUE/FALSE if TRUE for categorical variables the level with the highest prediction probability is selected, otherwise it is sampled according to the probabilities. |
"lm" is used for family "normal" and glm for all other families. (Robust=TRUE: lmrob, glmrob)
the imputed data set.
Alexander Kowarik
data(sleep) sleepImp1 <- regressionImp(Dream+NonD~BodyWgt+BrainWgt,data=sleep) sleepImp2 <- regressionImp(Sleep+Gest+Span+Dream+NonD~BodyWgt+BrainWgt,data=sleep) data(testdata) imp_testdata1 <- regressionImp(b1+b2~x1+x2,data=testdata$wna) imp_testdata3 <- regressionImp(x1~x2,data=testdata$wna,robust=TRUE)