plot.SemiParBIVProbit: SemiParBIVProbit plotting

Description

It takes a fitted SemiParBIVProbit object produced by SemiParBIVProbit() and plots the estimated smooth functions on the scale of the linear predictors. This function is a wrapper for plot.gam() in mgcv. Please see the documentation of plot.gam() for full details.

Usage

## S3 method for class 'SemiParBIVProbit':
plot(x, eq, ...)

Arguments

A fitted SemiParBIVProbit object as produced by SemiParBIVProbit().

The equation from which smooth terms should be considered for printing.

...

Other graphics parameters to pass on to plotting commands, as described for plot.gam() in mgcv.

Value

The function generates plots.

WARNING

The function can not deal with smooths of more than 2 variables.

Details

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 s(regr, edf) where 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. If 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.

References

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.

Examples

Run this code

## see examples for SemiParBIVProbit

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