circ.psa {PSAgraphics} | R Documentation |
Generates a Propensity Score Assessment Plot
Description
Displays a graphic that summarizes outcomes in a propensity score analysis, based on strata that have been defined in the first Phase of a propensity score analysis (PSA). The graphic displays contributions of individual strata to the overall effect, weighing contributions of individual strata according to the relative sizes of the respective strata. The overall effect is plotted as a heavy dashed diagonal line that runs parallel to the identity diagonal.
Usage
circ.psa(
response,
treatment = NULL,
strata = NULL,
summary = FALSE,
statistic = "mean",
trim = 0,
revc = FALSE,
confint = TRUE,
sw = 0.4,
ne = 0.5,
inc = 0.25,
pw = 0.4,
lab = TRUE,
labcex = 1,
xlab = NULL,
ylab = NULL,
main = NULL,
mai = c(1, 1.7, 1, 1.7)
)
Arguments
response |
Either a numeric vector containing the response of interest in a propensity score analysis, or a three column array containing response, treatment and strata. |
treatment |
Binary variable of same length as |
strata |
Generally integer variable; a vector of same length as
|
summary |
Logical (default |
statistic |
A scalar summary of the center of the response distribution. Seen next item below. Default = "mean". Note that to generate this statistic the full vector of responses must have been input, not summaries. |
trim |
Allows for a trimmed mean as outcome measure, where trim is from 0 to .5 (.5 implying median). |
revc |
Logical; if |
confint |
Logical; if |
sw |
Numerical argument (default = 0.4); extends axes on lower ends, effectively moving circles to lower left. |
ne |
Numerical argument (default = 0.5); extends axes on upper ends, effectively moving circles to upper right. |
inc |
Numerical argument (default = 0.35); controls circle sizes, but
relative circle sizes are controlled via |
pw |
numerical argument (default = 0.4); controls relative circle
sizes. |
lab |
Logical (default TRUE); labels circles with stratum labels. |
labcex |
numerical argument (default = 1); controls the size of the circle labels. |
xlab |
Label for horizontal axis, by default taken from
|
ylab |
Label for vertical axis, by default taken from |
main |
Main label for graph. |
mai |
margin parameters. |
Details
A circle is plotted for each stratum, centered on the means for the
treatment and control groups (for the X
and Y
axes)
respectively. The sizes of the circles correspond to the relative sizes of
the strata. A diagonal line (lower left to upper right) shows the identity,
X=Y
, so that circles on, say, the lower side of this line show that
the corresponding X
mean is larger than the Y
mean for that
stratum, and vice-versa. Parallel projections are made from the centers of
the strata-cum-circles to difference scores that are plotted on a line
segment on the lower-left corner of the graphic; the average difference,
which corresponds to the average treatment effect (ATE) for the overall
treatment effect, is plotted as a heavy (dark blue) dashed line parallel to
the identity diagonal. Rug plots are shown on the upper and right margins of
the graphic, for the X
and Y
marginal distributions. A 95%
confidence interval for the overall effect is plotted to the left of the
distribution of the stratum difference scores, centered on the ATE. Trimmed
means can replace the conventional mean for both the ATE and the marginal
distributions (however, the confidence interval calculations are likely to
become less trustworthy as larger values of the trim argument are used).
Value
Generate a Propensity Assessment Plot, as well as numerical data for
summary.strata |
An array with rows corresponding to strata and four columns; these show counts for control and treatment groups, as well as (possibly trimmed) mean response values for control and treatment. |
wtd.Mn.(Name1) |
Weighted mean of response for (Name1) group. Name
taken from |
wtd.Mn.(Name2) |
Weighted mean of
response for (Name2) group. Name taken from |
ATE |
Average Treatment Effect. |
se.wtd |
Weighted standard error
for |
approx.t |
Ratio of the average treatement effect and a standard error based on weighting of stratum variances. |
df |
Estimate of degree of freedom; response vector length minus twice number of strata. |
CI.95 |
Approximate 95% confidence interval for overall effect size. |
Author(s)
James E. Helmreich James.Helmreich@Marist.edu
Robert M. Pruzek RMPruzek@yahoo.com
See Also
Examples
##Random data with effect size 0
response <- rnorm(1000)
treatment <- sample(c(0,1), 1000, replace = TRUE)
strata <- sample(1:6, 1000, replace = TRUE)
circ.psa(response, treatment, strata)
##Random data with effect size -.2
response <- c(rnorm(500, 0, 12), rnorm(500, 6, 12))
treatment <- c(rep(0, 500), rep(1,500))
strata <- sample(1:5, 1000, replace = TRUE)
aaa <- cbind(response, treatment, strata)
circ.psa(aaa)
##Random data with effect size -2
response <- c(rt(100,3) * 2 + 20, rt(100,12) * 2 + 18)
treatment <- rep(c("A","B"), each = 100)
strata <- sample(c("X","Y","Z","U","V"), 200, replace = TRUE)
circ.psa(response, treatment, strata)
##Tree derived strata
library(rpart)
data(lindner)
attach(lindner)
lindner.rpart <- rpart(abcix ~ stent + height + female + diabetic +
acutemi + ejecfrac + ves1proc, data = lindner, method = "class")
lindner.tree<-factor(lindner.rpart$where, labels = 1:6)
circ.psa(log(cardbill), abcix, lindner.tree)
##Loess derived strata
lindner.ps <- glm(abcix ~ stent + height + female +
diabetic + acutemi + ejecfrac + ves1proc,
data = lindner, family = binomial)
ps<-lindner.ps$fitted
lindner.loess<-loess.psa(log(cardbill), abcix, ps)
circ.psa(lindner.loess$summary.strata[, 1:4], summary = TRUE,
inc = .1, labcex = .7)