ml_g: Function to fit linear regression using maximum likelihood.

In msme: Functions and Datasets for "Methods of Statistical Model Estimation"

Description

This function demonstrates the use of maximum likelihood to fit ordinary least-squares regression models, by maximizing the likelihood as a function of the parameters. Only conditional normal errors are supported.

Usage

ml_g(formula, data)

Arguments

formula	an object of class '"formula"' (or one that can be coerced to that class): a symbolic description of the model to be fitted. (See the help for 'lm' for more details).
data	a data frame containing the variables in the model.

Details

This function has limited functionality compared with R's internal lm function, which should be preferred in general.

Value

fit	the output of optim.
X	the design matrix.
y	the response variable.
call	the call used for the function.
beta.hat	the parameter estimates.
se.beta.hat	estimated standard errors of the parameter estimates.
sigma.hat	the estimated conditional standard deviation of the response variable.

Note

We use least squares to get initial estimates, which is a pretty barbaric hack. But the purpose of this function is as a starting point, not to replace existing functions.

Author(s)

Andrew Robinson and Joe Hilbe.

References

Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman \& Hall / CRC.

See Also

lm

Examples

data(ufc)
ufc <- na.omit(ufc)

ufc.g.reg <- ml_g(height.m ~ dbh.cm, data = ufc)

summary(ufc.g.reg)

msme documentation built on May 2, 2019, 5:07 a.m.