miss.lm: Statistical Inference for Linear Regression Models with...

In misaem: Linear Regression and Logistic Regression with Missing Covariates

Description

This function is used to perform statistical inference for linear regression model with missing values, by algorithm EM.

Usage

miss.lm(formula, data, control = list(...), ...)

Arguments

formula	an object of class "formula" : a symbolic description of the linear regression model to be fitted.
data	an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which miss.lm is called.
control	a list of parameters for controlling the fitting process. For miss.lm.fit this is passed to miss.lm.control.
...	arguments to be used to form the default control argument if it is not supplied directly.

Value

An object of class "miss.lm": a list with following components:

coefficients	Estimated β.
ll	Observed log-likelihood.
s.resid	Estimated standard error for residuals.
s.err	Standard error for estimated parameters.
mu.X	Estimated μ.
Sig.X	Estimated Σ.
call	the matched call.
formula	the formula supplied.

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, 3, -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

# Estimate regression using EM
df.obs = data.frame(y,X.obs)
miss.list = miss.lm(y~., data=df.obs)
print(miss.list)
print(summary(miss.list))
summary(miss.list)$coef

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