Usage: |
myfit = glmnoid(x,y{,opt})
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Input: |
| x | n x p matrix, the predictor variables
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| y | n x 1 vector, the response variables,
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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.wx | scalar or n x 1 vector, prior weights. If not
given, set to 1.
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| opt.off | scalar or n x 1 vector, offset in linear predictor.
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Output: |
| myfit.b | p x 1 vector, estimated coefficients.
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| myfit.bv | p x p matrix, estimated covariance matrix for b.
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| myfit.mu | n x 1 vector, estimated response mu.
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| myfit.w | n x 1 vector, contains the final weights.
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| myfit.h | n x 1 vector, diagonal elements of 'hat' matrix.
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| myfit.stat | list with the following statistics:
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| myfit.stat.df | degrees of freedom.
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| myfit.stat.deviance | deviance.
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| myfit.stat.pearson | generalized pearson's chi^2.
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| myfit.stat.loglik | log-likelihood.
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| myfit.stat.dispersion | dispersion parameter estimate =pearson/df.
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| myfit.stat.r2 | (pseudo) R^2.
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| myfit.stat.adr2 | adjusted (pseudo) R^2.
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| myfit.stat.aic | AIC criterion.
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| myfit.stat.bic | BIC criterion.
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| myfit.stat.it | scalar, number of iterations needed
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| myfit.stat.ret | scalar, return code:
0 o.k.,
-1 missing values have been encountered.
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