loess.plot: Loess plot with density distributions for propensity scores...

In jbryer/PSAgraphics2: Propensity Score Analysis

Description

Loess plot with density distributions for propensity scores and outcomes on top and right, respectively.

Usage

  loess.plot(x, response, treatment, responseTitle = "",
    treatmentTitle = "Treatment",
    percentPoints.treat = 0.1,
    percentPoints.control = 0.01, points.treat.alpha = 0.1,
    points.control.alpha = 0.1, plot.strata,
    plot.strata.alpha = 0.2, ...)

Arguments

x	vector of propensity scores.
response	the response variable.
treatment	the treatment varaible as a logical type.
percentPoints.treat	the percentage of treatment points to randomly plot.
percentPoints.control	the percentage of control points to randomly plot.
points.treat.alpha	the transparency level for treatment points.
points.control.alpha	the transparency level for control points.
responseTitle	the label to use for the y-axis (i.e. the name of the response variable)
treatmentTitle	the label to use for the treatment legend.
plot.strata	an integer value greater than 2 indicating the number of vertical lines to plot corresponding to quantiles.
plot.strata.alpha	the alpha level for the vertical lines.
...	other parameters passed to geom_smooth and stat_smooth.

Value

a ggplot2 figure

See Also

plot.mlpsa

Examples

## Not run: 
require(multilevelPSA)
require(party)
data(pisana)
data(pisa.psa.cols)
cnt = 'USA' #Can change this to USA, MEX, or CAN
pisana2 = pisana[pisana$CNT == cnt,]
pisana2$treat <- as.integer(pisana2$PUBPRIV) %% 2
lr.results <- glm(treat ~ ., data=pisana2[,c('treat',pisa.psa.cols)], family='binomial')
st = data.frame(ps=fitted(lr.results),
				math=apply(pisana2[,paste('PV', 1:5, 'MATH', sep='')], 1, mean),
				pubpriv=pisana2$treat)
				st$treat = as.logical(st$pubpriv)
loess.plot(st$ps, response=st$math, treatment=st$treat, percentPoints.control = 0.4,
           percentPoints.treat=0.4)

## End(Not run)

jbryer/PSAgraphics2 documentation built on May 18, 2019, 7:01 p.m.