Usage: |
myfit = glmbipro(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,
y[i] may have (integer) values between 0
and opt.wx[i] or opt.wx (if opt.wx is scalar).
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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,
usually the binomial index vector. If not
given, set to 1.
|
| opt.shf | integer, if exists and =1, some output is produced
which indicates how the iteration is going on.
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| opt.miter | integer, maximal number of iterations. The default
is 10.
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| opt.cnv | scalar, convergence criterion. The default is 0.0001.
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| opt.fscor | integer, if exists and =1, a Fisher scoring is
performed (instead of the default Newton-Raphson
procedure).
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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.
|
| 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 maximal number of iterations reached,
-1 missing values have been encountered.
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