multilevelPSA (version 1.2.5)

mlpsa.difference.plot: Creates a graphic summarizing the differences between treatment and comparison groups within and across level two clusters.

Description

Creates a graphic summarizing the differences between treatment and comparison groups within and across level two clusters.

Usage

mlpsa.difference.plot(x, xlab, ylab = NULL, title = NULL,
  overall.col = "blue", overall.ci.col = "green",
  level2.point.size = NULL, level1.points = TRUE, errorbars = TRUE,
  errorbars.adjusted.ci = TRUE, level2.rug.plot = TRUE, jitter = TRUE,
  reorder = TRUE, labelLevel2 = TRUE, sd = NULL, xlim, ...)

Arguments

x

the results of mlpsa.

xlab

label for the x-axis, or NULL to exclude.

ylab

label for the y-axis, or NULL to exclude.

title

title of the figure, or NULL to exclude.

overall.col

the color of the overall results line.

overall.ci.col

the color of the overall confidence interval.

level2.point.size

the point size of level 2 points.

level1.points

logical value indicating whether level 1 strata should be plotted.

errorbars

logical value indicating whether error bars should be plotted for for each level 1.

errorbars.adjusted.ci

whether the Bonferroni adjusted error bars should be plotted (these will be dashed lines).

level2.rug.plot

logical value indicating whether a rug plot should be plotted for level 2.

jitter

logical value indicating whether level 1 points should be jittered.

reorder

logical value indicating whether the level two clusters should be reordered from largest difference to smallest.

labelLevel2

logical value indicating whether the difference for each level 2 should be labeled.

sd

If specified, effect sizes will be plotted instead of difference in the native unit.

xlim

the limits of the x-axis.

...

currently unused.

See Also

plot.mlpsa

Examples

if (FALSE) {
data(pisana)
data(pisa.colnames)
data(pisa.psa.cols)
mlctree = mlpsa.ctree(pisana[,c('CNT','PUBPRIV',pisa.psa.cols)], formula=PUBPRIV ~ ., level2='CNT')
student.party = getStrata(mlctree, pisana, level2='CNT')
student.party$mathscore = apply(student.party[,paste0('PV', 1:5, 'MATH')], 1, sum) / 5
results.psa.math = mlpsa(response=student.party$mathscore, 
       treatment=student.party$PUBPRIV, 
       strata=student.party$strata, 
       level2=student.party$CNT, minN=5)
mlpsa.difference.plot(results.psa.math, sd=mean(student.party$mathscore, na.rm=TRUE))
}