miss.glm: Statistical Inference for Logistic Regression Models with...

In misaem: Linear Regression and Logistic Regression with Missing Covariates

Description

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

Usage

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

Arguments

formula	an object of class "formula" : a symbolic description of the logistic 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.glm is called.
control	a list of parameters for controlling the fitting process. For miss.glm.fit this is passed to miss.glm.control.
...	arguments to be used to form the default control argument if it is not supplied directly.

Value

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

coefficients	Estimated β.
ll	Observed log-likelihood.
var.covar	Variance-covariance matrix for estimated parameters.
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 dataset
N <- 100  # 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.obs <- X.complete
X.obs[patterns] <- NA

df.obs = data.frame(y,X.obs)

# SAEM
miss.list = miss.glm(y~., data=df.obs, print_iter=FALSE,seed=100)
print(miss.list)
print(summary(miss.list))
summary(miss.list)$coef

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