ml_glm: A function to fit generalized linear models using maximum...

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

Description

This function fits generalized linear models by maximizing the joint log-likeliood, which is set in a separate function. Only single-parameter members of the exponential family are covered. The post-estimation output is designed to work with existing reporting functions.

Usage

ml_glm(formula, data, family, link, offset = 0, start = NULL, verbose =
FALSE, ...)

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 'glm' for more details).
data	a data frame containing the variables in the model.
family	a description of the error distribution be used in the model. This must be a character string naming a family.
link	a description of the link function be used in the model. This must be a character string naming a link function.
offset	this can be used to specify an _a priori_ known component to be included in the linear predictor during fitting. This should be 0 or a numeric vector of length equal to the number of cases.
start	optional starting points for the parameter estimation.
verbose	logical flag affecting the detail of printing. Defaults to FALSE.
...	optional arguments to pass within the function.

Details

The containing package, msme, provides the needed functions to use the ml_glm function to fit the Poisson and Bernoulli families, and supports the use of the identity, log, logit, probit, and complementary log-log link functions. The object returned by the function is designed to be reported by the print.glm function.

Value

fit	the output of optim.
X	the design matrix.
y	the response variable.
call	the call used for the function.
obs	the number of observations.
df.null	the degrees of freedom for the null model.
df.residual	the residual degrees of freedom.
deviance	the residual deviance.
null.deviance	the residual deviance for the null model.
residuals	the deviance residuals.
coefficients	parameter estimates.
se.beta.hat	standard errors of parameter estimates.
aic	Akaike's Information Criterion.
i	the number of iterations required for convergence.

Note

This function is neither as comprehensive nor as stable as the inbuilt glm function. It is a lot easier to read, however.

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

irls, glm, ml_glm2

Examples

data(medpar)

ml.poi <- ml_glm(los ~ hmo + white,
                 family = "poisson",
                 link = "log",
                 data = medpar)
ml.poi
summary(ml.poi)

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