miss.lm {misaem} | R Documentation |
This function is used to perform statistical inference for linear regression model with missing values, by algorithm EM.
miss.lm(formula, data, control = list(...), ...)
formula |
an object of class " |
data |
an optional data frame containing the variables in the model. If not found in |
control |
a list of parameters for controlling the fitting process. For |
... |
arguments to be used to form the default control argument if it is not supplied directly. |
An object of class "miss.lm
": a list with following components:
coefficients |
Estimated |
ll |
Observed log-likelihood. |
s.resid |
Estimated standard error for residuals. |
s.err |
Standard error for estimated parameters. |
mu.X |
Estimated |
Sig.X |
Estimated |
call |
the matched call. |
formula |
the formula supplied. |
# 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