loess.plot | R Documentation |
Loess plot with density distributions for propensity scores and outcomes on top and right, respectively.
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,
...
)
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()]. |
a ggplot2 figure
plot.mlpsa
## 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)
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