MBESS (version 4.8.0)

intr.plot.2d: Plotting Conditional Regression Lines with Interactions in Two Dimensions

Description

To plot regression lines for one two-way interactions, holding one of the predictors (in this function, z) at values -2, -1, 0, 1, and 2 standard deviations above the mean.

Usage

intr.plot.2d(b.0, b.x, b.z, b.xz,x.min=NULL, x.max=NULL, x=NULL, 
n.x=50, mean.z=NULL, sd.z=NULL, z=NULL,xlab="Value of X",  
ylab="Dependent Variable", sd.plot=TRUE, sd2.plot=TRUE, sd_1.plot=TRUE, 
sd_2.plot=TRUE, type.sd=2, type.sd2=3, type.sd_1=4, type.sd_2=5, 
legend.pos="bottomright", legend.on=TRUE, ... )

Arguments

b.0

the intercept

b.x

regression coefficient for predictor x

b.z

regression coefficient for predictor z

b.xz

regression coefficient for the interaction of predictors x and z

x.min,x.max

the range of x used in the plot

x

a specific predictor vector x, used instead of x.min and x.max

n.x

number of elements in predictor vector x

mean.z

mean of predictor z

sd.z

standard deviation of predictor z

z

a specific predictor vector z, used instead of z.min and z.max

xlab

title for the axis which the predictor x is on

ylab

title for the axis which the dependent y is on

sd.plot, sd2.plot, sd_1.plot, sd_2.plot

whether or not to plot the regression line holding z at values 1, 2, -1, and -2 standard deviations above the mean, respectively. Default values are all TRUE.

type.sd, type.sd2, type.sd_1, type.sd_2

types of lines to be plotted holding z at values 1, 2, -1, and -2 standard deviations above the mean, respectively. Default are line type 2,3,4, and 5, respectively.

legend.pos

position of the legend; possible options are "bottomright", "bottom", "bottomleft", "left", "center", "right", "topleft", "top", and "topright".

legend.on

whether or not to show the legend

…

allows one to potentially include parameter values for inner functions

Details

To input the predictor x, one can use either the limits of x (x.max and x.min) , or a specific vector x (x). To input the predictor z, one can use either the mean and standard deviation of z (mean.z and sd.z ), or a specific vector z (z).

References

Cohen, J., Cohen, P., West, S. G. and Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Erlbaum.

See Also

intr.plot

Examples

# NOT RUN {
## A situation where one regression line is outside the default scope of the coordinates
intr.plot.2d(b.0=16, b.x=2.2, b.z=2.6, b.xz=.4, x.min=0, x.max=20, mean.z=0, sd.z=3)

## Adjust the scope of x and y axes so that proper sections of regression lines can be seen 
intr.plot.2d(b.0=16, b.x=2.2, b.z=2.6, b.xz=.4, x.min=0, x.max=50, mean.z=0, 
sd.z=3, xlim=c(0,50), ylim=c(-20,100) )

## Use specific vector(s) to define the predictor(s) 
intr.plot.2d(b.0=16, b.x=2.2, b.z=2.6, b.xz=.4, x=c(1:10), z=c(0,2,4,6,8,10))

intr.plot.2d(b.0=16, b.x=2.2, b.z=2.6, b.xz=.4, x.min=0, x.max=20, 
z=c(1,3,6,7,9,13,16,20), ylim=c(0,100))

## Change the position of the legend so that it does not block regression lines
intr.plot.2d(b.0=10, b.x=-.3, b.z=1, b.xz=.5, x.min=0, x.max=40, mean.z=-5, sd.z=3, 
ylim=c(-100,100),legend.pos="topright" )

# }