mice.impute.QAS: Function to use QAS in Mice

In LisaMaag/QAS: Quasi-analytical solution for logit

Description

mice.impute.QAS is used to prepare the predictQAS output for mice

Usage

mice.impute.QAS(y, ry, x, boot = TRUE, ...)

Arguments

y	The target variable
ry	a logical vector, which indicates the missing values in the target variable
x	a data frame with the independent variables
boot	if TRUE, a bayesian bootstrap sample is applied
...	further objects from other functions

Value

An object of class mice.impute.QAS is a vector containing the imputed values of the target variable.

Examples

# generate Data
y <- as.integer(c(1,0,0,0,1,1,1,0,0,1))
x <- c(15,88,90,60,24,30,26,57,69,18)
z <- as.integer(c(3,2,2,1,3,3,2,1,1,3))
example_data <- data.frame(y,x,z)

# deploy QAS.func-Function
result1 <- QAS.func(y~x+z, data=example_data, weights=NULL, seed=NULL, tau=NULL)

# deploy predictQas-Function
result2  <- predictQAS(QAS.res = result1, data=example_data)

# generate logical vector and data frame with independent variables
ry <- c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE)
x <- data.frame(x,z)

# deploy mice.impute.QAS-Funktion
impute <- mice.impute.QAS(y=y, ry=ry, x=example_data, boot=TRUE)

# run mice function
library(mice)
final <- mice(nhanes2,method=c("","pmm","QAS","pmm"))

LisaMaag/QAS documentation built on May 9, 2019, midnight