Usage: |
myfit = eivplmnor(x,t,y,sigma,h{,opt})
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Input: |
| x | n x p matrix, the discrete predictor variables.
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| t | n x q matrix, the continuous predictor variables.
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| y | n x 1 vector, the response variables.
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| sigma | scalar, the variance of the measurement error.
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| h | q x 1 vector, the bandwidth.
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| opt | optional, a list with optional input. The macro
"gplmopt" 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.b0 | p x 1 vector, the initial coefficients. If not
given, all coefficients are put =0 originally.
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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.wt | n x 1 vector, weights for t (trimming factors).
If not given, all set to 1.
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| opt.tg | ng x 1 vector, a grid for continuous part. If tg is
given, the nonparametric function will also be
computed on this grid.
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| opt.shf | integer, if exists and =1, some output is produced
which indicates how the iteration is going on.
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| opt.nosort | integer, if exists and =1, the continuous variables
t and the grid tg are assumed to be sorted by the
1st column. Sorting is required by the algorithm!
Hence this option should be given only when data
are sorted.
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| opt.miter | integer, maximal number of iterations. The default
is 10.
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| opt.cnv | integer, convergence criterion. The default is 0.0001.
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| opt.wtc | n x 1 vector, weights for convergence criterion,
w.r.t. m(t) only. If not given, opt.wt is used.
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| opt.off | scalar or n x 1 vector, offset in predictor.
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Output: |
| myfit.b | k x 1 vector, estimated coefficients.
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| myfit.bv | k x k matrix, estimated covariance matrix for
coefficients.
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| myfit.m | n x 1 vector, estimated nonparametric part.
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| myfit.mg | ng x 1 vector, estimated nonparametric part on grid
if tg was given. This component will not exist, if
tg was not given.
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| myfit.stat | list with the following statistics:
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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.r2 | pseude R^2,
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| myfit.stat.dispersion | dispersion parameter estimate,
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| myfit.stat.it | scalar, number of iterations needed.
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