milonga: Multiple imputation for repeated binary outcomes

In DadongZ/milonga: Multiple Imputation for Repeated Binary Outcome

Description

Multiple imputation for repeated binary outcomes

Usage

milonga(data, cols = NULL, nprior = 100, fprior.list = NULL,
  burnin = NULL, rep = 10)

Arguments

data	Data frame or matrix of multivariate binary outcome with missing values to impute, NA denotes missing values
cols	NULL or a character or integer vector giving the columns for imputation
nprior	integer
fprior.list	a list of prior frequencies. See "Details"
burnin	number of burnin iterations, NULL or positive integer
rep	number of iterations for imputation, default is 10

Value

list of imputed data frames

Examples

data(polio)
milonga(polio, cols=2:7)

DadongZ/milonga documentation built on Jan. 9, 2020, 9:57 p.m.