Usage: |
myfit = gplmbilobiased(x,t,y,h,b,m0{,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.
y[i] may have (integer) values between 0
and opt.wx[i] or opt.wx (if opt.wx is scalar).
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| h | q x 1 vector, the bandwith.
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| b | p x 1 vector, coefficients b from parametric fit.
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| m0 | n x 1 vector, parametric estimate for m, to which to
add the bias.
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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.wx | scalar or n x 1 vector, prior weights,
usually the binomial index vector. 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.m0g | ng x 1 vector, parametric estimate for m on grid tg.
If not given, this will aproxiammated from m0.
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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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Output: |
| myfit.m | n x 1 vector, biased version of m0
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| myfit.mg | ng x 1 vector, biased version of m0g
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| myfit.it | number of iterations needed
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