It takes a fitted
SemiParSampleSel object produced by
SemiParSampleSel() and plots the
estimated smooth functions on the scale of the linear predictors.
This function is a wrapper for
mgcv. Please see the documentation of
plot.gam() for full details.
The equation from which smooth terms should be considered for printing.
Other graphics parameters to pass on to plotting commands, as described for
This function produces plots showing the smooth terms of a fitted semiparametric bivariate probit model. In the case of 1-D smooths, the
x axis of each plot is labelled using the name of the regressor, while the y axis is labelled as
regr is the regressor's name, and
edf the effective degrees of freedom of the smooth. For 2-D smooths, perspective
plots are produced with the x axes labelled with the first and second variable names and the y axis
is labelled as
s(var1, var2, edf), which indicates the variables of which the term is a function and the
edf for the term.
seWithMean = TRUE then the intervals include the uncertainty about the overall mean. Note that the smooths are still shown
centred. The theoretical arguments
and simulation study of Marra and Wood (2012) suggest that
seWithMean = TRUE results in intervals with
close to nominal frequentist coverage probabilities.
The function generates plots.
The function can not deal with smooths of more than 2 variables.
Maintainer: Giampiero Marra [email protected]
Marra G. and Wood S.N. (2012), Coverage Properties of Confidence Intervals for Generalized Additive Model Components. Scandinavian Journal of Statistics, 39(1), 53-74.
## see examples for SemiParSampleSel
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