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

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 variable as a logical type.

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.

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.

...

other parameters passed to [ggplot2::geom_smooth()] and [ggplot2::stat_smooth()].

Value

a ggplot2 figure

See also

plot.mlpsa

Examples

if (FALSE) { # \dontrun{
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)
} # }