plot.gqc: plot Method for Class 'gqc'

In matsukik/grt: General Recognition Theory

Description

Plot the fitted data set and quadratic decision boundary.

Usage

## S3 method for class 'gqc'
plot(x, fitdb = TRUE, initdb = FALSE, 
    xlim = NULL, ylim = NULL, bg, pch, npoints = 100, ...) 

Arguments

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.
xlim	the x limits of the plot. If NULL, limits are calculated from the model matrix. Default to NULL.
ylim	the y limits of the plot. If NULL, limits are calculated from the model matrix. Default to NULL.
bg	the background color to be used for points in 2D scatter plot. Default is c("white", "gray")[response] where response represents the response vector of the model
pch	the symbols to be used as points in 2D scatter plot. Default is c(21,24)[x$category]
npoints	number of points per dimension used to plot the decision bound. Default is 100.
...	further arguments.

Details

This function produces a scatter plot of data matrix in the x and (optionally) decision boundary (i.e., x$par and/or x$initpar).

The look of the plot differs depending on the dimension of the model. If the dimension is 2, scatter plot of the model matrix is plotted. If the dimension is 3, plot3d.gqc is called to create a 3D scatter plot. In all other cases, an error message will be returned.

See Also

lines.gqcStruct, {plot3d.gqc}

Examples

data(subjdemo_2d)
fit.2dq <- gqc(response ~ x + y, data=subjdemo_2d, 
    category=subjdemo_2d$category, zlimit=7)
plot(fit.2dq)

matsukik/grt documentation built on May 21, 2019, 12:57 p.m.