Usage: |
stat = glmstat(code,x,y,b,bv{,opt})
|
Input: |
| code | text string, the short code for the model (e.g.
"bilo" for logit or "noid" for ordinary PLM).
|
| x | n x p matrix, the predictor variables.
|
| y | n x 1 vector, the response variables.
(In the case of replicated data, the number of
replications should be given in opt.wx and y should
contain the sums of all responses for a replication.)
|
| b | p x 1 vector, estimated coefficients.
|
| bv | p x p matrix, inverse Hessian of optimization
procedure. This is the estimated covariance of b,
as it comes out of "glmcore", i.e. not yet corrected
for dispersion!
|
| opt | optional, a list with optional input.
"glmopt" can be used to set up this parameter.
The order of the list elements is not important.
|
| opt.weights | string, type of weights. Can be "frequency"
for replication counts, or "prior" (default)
for prior weights in weighted regression.
|
| opt.wx | scalar or n x 1 vector, frequency or prior
weights. If not given, set to 1.
|
| opt.off | scalar or n x 1 vector, offset in linear predictor.
If not given, set to 0.
|
| opt.pow | scalar, power for power link. If not given, set
to 0 (logarithm).
|
| opt.nbk | scalar, extra parameter k for negative binomial
distribution. If not given, set to 1 (geometric
distribution).
|
Output: |
| stat | list with the following statistics: |
| stat.serror | standard errors of parameter estimates. |
| stat.tvalue | t-values for parameter estimates. |
| stat.pvalue | p-values for significance of parameter estimates. |
| stat.df | degrees of freedom. |
| stat.deviance | deviance. |
| stat.pearson | generalized pearson's chi^2. |
| stat.loglik | log-likelihood. |
| stat.dispersion | dispersion parameter estimate =pearson/df. |
| stat.r2 | (pseudo) R^2. |
| stat.adr2 | adjusted (pseudo) R^2. |
| stat.aic | AIC criterion. |
| stat.bic | BIC criterion. |