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.
|
| 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.
|
| opt.b0 | p x 1 vector, the initial coefficients. If not
given, all coefficients are put =0 originally.
|
| 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.
|
| 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.
|
| 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. |
| myfit.bv | k x k matrix, estimated covariance matrix for
coefficients. |
| myfit.m | n x 1 vector, estimated nonparametric part. |
| 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. |
| myfit.stat | list with the following statistics: |
| myfit.stat.deviance | deviance, |
| myfit.stat.pearson | generalized pearson's chi^2, |
| myfit.stat.r2 | pseude R^2, |
| myfit.stat.dispersion | dispersion parameter estimate, |
| myfit.stat.it | scalar, number of iterations needed. |