plot.SSM: Plot smooth supersaturated model main effects

In peterrobertcurtis/SSM: Fit and Analyze Smooth Supersaturated Models

Description

plot.SSM is a plot method for SSM objects. It plots the main effects of the SSM only, that is the subset of basis terms that are dependent on a single variable only. For single variable data this is a plot of the complete model.

Usage

## S3 method for class 'SSM'
plot(x, ..., grid = 200, yrange = "full", GP = TRUE)

Arguments

x	An SSM object.
...	(optional) arguments to pass to the plot function.
grid	(optional) A number. This specifies the resolution of the plot, i.e. how many model evaluations are used to construct the curve.
yrange	(optional) Character. Only "full" will have an effect.
GP	(optional) Logical. For single variable data, the credible interval of the metamodel error estimator will be plotted if TRUE.

Details

For each variable, the effect is plotted over [-1, 1] by default although passing an alternate range to the xlim argument will override this.

The yrange argument is designed to automatically compute the relevant plot range for each effect. By default a ylim value is passed to plot that covers the range of responses. "full" results in a ylim value that covers the range of predictions or, if appropriate, the range of the metamodel error credible interval.

For single variable data, setting GP to TRUE will plot a credible interval for the metamodel error estimating Gaussian process if this has been computed for the SSM object.

Examples

# A single variable example
X <- seq(-1, 1, 0.25)
Y <- sapply(X, "^", 3)
s <- fit.ssm(X, Y, GP = TRUE)
plot(s)

# A six variable example
data(attitude)
X <- transform11(attitude[ 2:7])
Y <- attitude[ , 1]
s <- fit.ssm(X, Y)
plot(s)

peterrobertcurtis/SSM documentation built on May 25, 2019, 2:10 a.m.