miss.glm.model.select: miss.glm.model.select

In misaem: Linear Regression and Logistic Regression with Missing Covariates

Description

Model selection for the logistic regression model with missing data.

Usage

miss.glm.model.select(Y, X, seed = NA)

Arguments

Y	Binary response vector N * 1
X	Design matrix with missingness N * p
seed	An integer as a seed set for the random generator. The default value is 200.

Value

An object of class "miss.glm".

Examples

# Generate dataset
N <- 40  # number of subjects
p <- 3     # number of explanatory variables
mu.star <- rep(0,p)  # mean of the explanatory variables
Sigma.star <- diag(rep(1,p)) # covariance
beta.star <- c(1, 1,  0) # coefficients
beta0.star <- 0 # intercept
beta.true = c(beta0.star,beta.star)
X.complete <- matrix(rnorm(N*p), nrow=N)%*%chol(Sigma.star) +
              matrix(rep(mu.star,N), nrow=N, byrow = TRUE)
p1 <- 1/(1+exp(-X.complete%*%beta.star-beta0.star))
Y <- as.numeric(runif(N)<p1)
# Generate missingness
p.miss <- 0.10
patterns <- runif(N*p)<p.miss #missing completely at random
X <- X.complete
X[patterns] <- NA
# model selection for SAEM
miss.model = miss.glm.model.select(Y,X,seed=100)
print(miss.model)

misaem documentation built on April 12, 2021, 9:06 a.m.