Usage: |
myfit = gplmbilotest(x,t,y,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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| 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.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 linear fit. If not given
a "truly" linear function will be tested by using
the design matrix(n)~t. The parameter opt.tdesign
will not be considered if opt.b0 and opt.m0 are
given.
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| opt.b0 | p x 1 vector, coefficients for parametric fit. If
not given, it will be estimated by glmnoid. The
parameter opt.b0 will not be used if opt.m0
is not given.
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| opt.m0 | n x 1 vector, parametric fit for m(t). If not
not given, it will be estimated by glmnoid. The
parameter opt.m0 will not be used if opt.b0
is not given.
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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 | ng x 1 vector, estimated nonparametric part on grid
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| myfit.rr | 3 x 1 vector, three test statistics R_j, 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.glmfit | list with output b, bv and stat from glm fit. See
macro glmbilo for details. This is empty if opt.b0
and opt.m0 were 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.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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