plot.mlcm: Plot an mlcm Object

In computational-psychology/MLCM: Maximum Likelihood Conjoint Measurement

Description

Plots the conjoint measurement scale(s) as a function of stimulus level.

Usage

## S3 method for class 'mlcm'
plot(x, standard.scale = FALSE, transpose = FALSE, SD.scale = FALSE, ...)
## S3 method for class 'mlcm'
lines(x, standard.scale = FALSE, transpose = FALSE, SD.scale = FALSE, ...)
## S3 method for class 'mlcm'
points(x, standard.scale = FALSE, transpose = FALSE, SD.scale = FALSE, ...)

Arguments

x	mlcm object, typically result of mlcm
standard.scale	logical indicating whether the plotted scales should be normalized so that the maximum scale value is 1
transpose	logical, indicating whether to transpose the matrix of the perceptual scale, when the full model is fit. Not defined if there are more than 2 dimensions.
SD.scale	logical indicating whether to plot results in units of d', the signal detection measure of signal strength in which the variance for each stimulus level is unity. Ignored if standard.scale = TRUE.
...	other parameters to be passed through to the plotting function.

Details

These functions use matplot, matlines and matpoints so their help page should be examined for information on additional parameters that can be specified.

Author(s)

Kenneth Knoblauch

See Also

matplot

Examples

plot(mlcm(BumpyGlossy), type = "b")

bg.full <- mlcm(BumpyGlossy, model = "full")
opar <- par(mfrow = c(1, 2), pty = "s")
plot(bg.full, type = "b", 
	xlab = "Gloss Level",
	ylab = "Bumpiness Model Estimates")
plot(bg.full, transpose = TRUE, type = "b",
	xlab = "Bumpiness Level",
	ylab = "Glossiness Model Estimates")
par(opar)

computational-psychology/MLCM documentation built on Jan. 12, 2022, 12:23 a.m.