Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: glm
See also: glmcore glmlld glmstat

Macro: glmll
Description: glmll computes the individual log-likelihood.

Link:
Usage: ll = glmll(code,mu,y{,opt})
Input:
code text string, the short code for the model (e.g. "bilo" for logit or "noid" for ordinary OLS).
mu n x 1, n x 2 or n x 3 matrix, either the response function, or sums of response function in the first column. In second and third column expressions needed for computation of deviance (typically sums of mu^2 or log(mu)).
y n x 1, n x 2 or n x 3 matrix, either the response values, or sums of response values in the first column. In second and third column expressions needed for computation of deviance (typically sums of y^2 or log(y)).
opt optional, a list with optional input. The macro "glmopt" can be used to set up this parameter.
opt.phi nuisance parameter, usually the dispersion parameter. If not given, only those parts of the log-likelihood are computed, which depend on the parameter of interest
opt.nbk scalar, extra parameter k for negative binomial distribution. If not given, set to 1 (geometric distribution).
Output:
ll n x 1 matrix, log-likelihood.

Example:
library("glm")
y=1
mu=0.75
ll=glmll("bilo",mu,y)
ll
Result:
The log-likelihood for logit at y=1 and mu=0.75: 
Content of object ll
[1,] -0.28768

Library: glm
See also: glmcore glmlld glmstat

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: Marlene Mueller, 970523
(C) MD*TECH Method and Data Technologies, 28.6.1999