Usage: |
myfit = gplmbilobootstraptest(x,t,y,h,nboot{,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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| h | q x 1 vector, the bandwidth.
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| nboot | scalar, number of bootstrap replications
!! be careful if you computer is slow !!
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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.wt | n x 1 vector, weights for t (trimming factors).
If not given, all set to 1.
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| opt.tdesign | n x r matrix, design for parametric fit for m(t).
If not given a "truly" linear function will be
tested by using the design matrix(n)~t.
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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 you should switch if off only when the data
are already sorted.
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| opt.noinit | integer, if exists and =1, the estimation of the
partial linear model is not initialized by the
parametric fit.
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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.b | k x 1 vector, estimated coefficients
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| myfit.bv | k x k matrix, estimated covariance matrix for b
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| myfit.m | n x 1 vector, estimated nonparametric part
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| myfit.mg | ngx 1 vector, estimated nonparametric part on grid
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| myfit.rr | 3 x 1 vector, 3 test statistics, see
Haerdle/Mammen/Mueller
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| myfit.alpha | 3 x 1 vector, significance level for rejection of
the parametric hypothesis (for each of the
the three test statisctics).
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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.loglik | log-likelihood,
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| myfit.stat.r2 | pseudo R^2,
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| myfit.stat.it | 2 x 1 vector, number of iterations needed in
semiparametric and biased parametric fit
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