plot3d.gqc: plot3d Method for Class 'gqc'
In grt: General Recognition Theory

Description Usage Arguments Details References See Also Examples

plot the fitted 3D data set and quadratic decision boundaries.

## S3 method for class 'gqc'
plot3d(x, fitdb = TRUE, initdb = FALSE,
    lims = NULL, npoints = 100, alpha = .5,
    fill = TRUE, smooth = FALSE, ...)

`x`	object of class `gqc`
`fitdb`	logical. If `TRUE`, the fitted decision bound will be plotted. Default to `TRUE`
`initdb`	logical. If `TRUE`, the decision bound specified by the initial parameters will be plotted. Default to `FALSE`
`lims`	column matrix of the x, y, and z limits of the plot. If `NULL`, limits are calculated from the model matrix of `x`. Default to NULL.
`npoints`	number of points per dimension (i.e., x, y, and z) used to plot the decision bound surface. Default is 100.
`alpha`	alpha value for the decision bound surface(s) ranging from 0 (fully transparent) to 1 (opaque). Default is .5
`fill`	logical. If `TRUE`, decision bounds (if `fitdb` or `initdb` is set to `TRUE`) should be drawn with filled surfaces. If FALSE, a wire frame should be used. Default to `TRUE`.
`smooth`	logical. If `TRUE`, smooth shading should be used. Default to `FALSE`.
`...`	further arguments.

This function produces a 3D scatter plot of data matrix of x and (optionally) quadratic decision boundaries specified within (i.e., x$par and/or x$initpar), using points3d function in the rgl package and contour3d function in the misc3d package respectively.

Daniel Adler, Oleg Nenadic and Walter Zucchini (2003) RGL: A R-library for 3D visualization with OpenGL

plot.gqc, {plot3d.gqc}

## Not run: 
data(subjdemo_3d)
fit.3dq <- gqc(response ~ x + y + z, data=subjdemo_3d,
    category=subjdemo_3d$category, zlimit=7)
plot3d(fit.3dq)

## End(Not run)