plot {StratSel}

Plots a StratSel Object

Description

Plots predicted probabilities for all three possible outcomes based on an object of class StratSel.

Usage

	## S3 method for class 'StratSel'
plot(x, profile, x.move, x.range, uncertainty = FALSE, 
 n.sim = 100, ci = 0.95, ylim, xlab, ylab1, ylab2, ylab3, plot.nr, ...)

Arguments

x

An object of class StratSel

profile

Vector. The values of all independent variables including the three constants.

x.move

Scalar. Indicates which variable is changing (and displayed on the x-axis).

x.range

Vector. A vector with two elements. The x which is moving will start at x.range[1] and end at x.range[2].

uncertainty

Logical. Indicates whether confidence bands should be displayed or not.

n.sim

Scalar. If uncertainty=TRUE the prediction uncertainty is generated with a simulation based on n.sim simulations. Default is 100. It is recommended to use 1000 simulations.

ci

Scalar. Indicates which confidence interval should be plotted, the default is 0.95.

ylim

Vector. A vector with two elements defining the range of the plotted y (predicted probability).

xlab

String. A label to be used for the x-axis. Will be recycled in all three plots.

ylab1

String. Label for the y-axis of the first plot (predicted probability of outcome 1).

ylab2

String. Label for the y-axis of the second plot (predicted probability of not outcome 1).

ylab3

String. Label for the y-axis of the third plot (predicted probability of outcome 4).

plot.nr

Vector. If one does not want to plot all three outcomes, one can use this vector to indicate which plot(s) should be shown.

...

Further arguments to be supplied to plot.

Author(s)

Lucas Leemann [e-mail]

Examples

data(data.fake)
# Running just an agent error model (note: corr=FALSE) with \code{var.C} being 
#part of both actors' utilities
out1 <- StratSel(Y ~ var.A + var.B | var.C + var.D | var.E + var.C, data=data.fake, corr=FALSE)
par(mfrow=c(3,1))
plot(out1, profile=c(1,0.2,-0.2,1,0.2,-0.2,1,0.1,-0.3), 
 x.move=c(5,9),x.range=c(-15,15), ci=0.7, uncertainty=TRUE)