Usage: |
stat = glmstat(code,x,y,b,bv{,opt})
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Input: |
| code | text string, the short code for the model (e.g.
"bilo" for logit or "noid" for ordinary PLM).
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| x | n x p matrix, the predictor variables.
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| 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.)
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| b | p x 1 vector, estimated coefficients.
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| 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!
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| opt | optional, a list with optional input. The macro
"glmopt" can be used to set up this parameter.
The order of the list elements is not important.
Parameters which are not given are replaced by
defaults (see below).
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| opt.weights | string, type of weights. Can be "frequency"
for replication counts, or "prior" (default)
for prior weights in weighted regression.
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| opt.wx | scalar or n x 1 vector, frequency or prior
weights. If not given, set to 1.
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| opt.off | scalar or n x 1 vector, offset in linear predictor.
If not given, set to 0.
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| opt.pow | scalar, power for power link. If not given, set
to 0 (logarithm).
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| opt.nbk | scalar, extra parameter k for negative binomial
distribution. If not given, set to 1 (geometric
distribution).
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Output: |
| stat | list with the following statistics:
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| stat.serror | standard errors of parameter estimates.
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| stat.tvalue | t-values for parameter estimates.
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| stat.pvalue | p-values for significance of parameter estimates.
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| stat.df | degrees of freedom.
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| stat.deviance | deviance.
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| stat.pearson | generalized pearson's chi^2.
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| stat.loglik | log-likelihood.
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| stat.dispersion | dispersion parameter estimate =pearson/df.
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| stat.r2 | (pseudo) R^2.
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| stat.adr2 | adjusted (pseudo) R^2.
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| stat.aic | AIC criterion.
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| stat.bic | BIC criterion.
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