miss.lm.model.select: miss.lm.model.select

In misaem: Linear Regression and Logistic Regression with Missing Covariates

Description

Model selection for the linear regression model with missing data.

Usage

miss.lm.model.select(Y, X)

Arguments

Y	Response vector N * 1
X	Design matrix with missingness N * p

Value

An object of class "miss.lm".

Examples

# Generate complete data
set.seed(1)
mu.X <- c(1, 1)
Sigma.X <- matrix(c(1, 1, 1, 4), nrow = 2)
n <- 50
p <- 2
X.complete <- matrix(rnorm(n*p), nrow=n)%*%chol(Sigma.X) +
              matrix(rep(mu.X,n), nrow=n, byrow = TRUE)
b <- c(2, 0, -1)
sigma.eps <- 0.25
y <- cbind(rep(1, n), X.complete) %*% b + rnorm(n, 0, sigma.eps)

# Add missing values
p.miss <- 0.10
patterns <- runif(n*p)<p.miss #missing completely at random
X.obs <- X.complete
X.obs[patterns] <- NA

# model selection
miss.model = miss.lm.model.select(y, X.obs)
print(miss.model)

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