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

In jbryer/psa: Applied Propensity Score Analysis with R

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

Description

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

Usage

loess_plot(
  ps,
  outcome,
  treatment,
  outcomeTitle = "",
  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,
  level = 0.68,
  ...
)

Arguments

ps	vector of propensity scores.
outcome	the outcome variable.
treatment	the treatment varaible as a logical type.
outcomeTitle	the label to use for the y-axis (i.e. the name of the outcome variable)
treatmentTitle	the label to use for the treatment legend.
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.
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.
level	Level of confidence interval to use. The default is 0.68 (for 1 standard error) since the primary purpose is to compare overlap between the two lines. See this article for more details: https://towardsdatascience.com/why-overlapping-confidence-intervals-mean-nothing-about-statistical-significance-48360559900a
...	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$ps, outcome=st$math, treatment=st$treat, percentPoints.control = 0.4, 
           percentPoints.treat=0.4)

## End(Not run)

jbryer/psa documentation built on Nov. 17, 2023, 8:21 a.m.