mlreg: Fit a Linear Model by Maximum Likelihood

Description

Fits a linear model by maximum likelihood assuming a variety of response distributions.

Usage

mlreg.fit(X, y, weights=NULL, dist="logistic", init=NULL, scale=NULL)
mlreg.fit.zero(y, weights=NULL, dist="logistic", init=NULL, scale=NULL)

Arguments

the design matrix. Rows containing missing values (in X or y) will be removed.

numeric response vector. Missing values will be removed.

weights

vector of non-negative weights.

dist

character string giving the name of the response distribution. The possibilities are "extreme", "logistic", "gaussian", "weibull", "exponential", "rayleigh", "loggauss

init

numeric vector of initial values for the parameters.

scale

if specified then the scale parameter is fixed at the given value.

Value

See the documentation for survreg.object

Details

This function is merely a convenient wrapper for calling the survreg.fit function, which is part of the survival library by Terry Therneau. It fits the model y = X*b + scale*e where b is the vector of regression coefficients and e is a vector of mean-zero errors, by maximum likelihood. The function mlreg.fit.zero assumes that the mean is zero and fits y = scale*e, estimating only the scale parameter.

Examples

Run this code

x <- 1:50
y <- x + 2*rnorm(50)
X <- cbind(1,x)
out <- mlreg.fit(X,y,dist="logistic")