box.psa: Compare balance graphically of a continuous covariate as part...

Description Usage Arguments Details Author(s) See Also Examples

View source: R/box.psa.R

Description

Given predefined strata and two level treatment for a continuous covariate from a propensity score analysis, box.psa draws pairs of side by side boxplots corresponding to control and treatment for each stratum.

Usage

1
2
3
4
5
6
box.psa(continuous, treatment = NULL, strata = NULL, boxwex = 0.17, 
offset = 0.17, 
col = c("yellow", "orange", "black", "red", "darkorange3"),
xlab = "Stratum", legend.xy = NULL, 
legend.labels = NULL, pts = TRUE, 
balance = FALSE, trim = 0, B = 1000, ...)

Arguments

continuous

Vector or N X 3 dataframe or matrix. If a vector, then represents the quantitative covariate that is being balanced within strata in a PSA. If continuous has three columns, then the second and third are assumed to be the treatment and strata respectively. Missing values are not allowed.

treatment

Binary vector of same length as continuous representing the two treatments; can be a character vector or factor.

strata

A vector or factor of same length as continuous indicating the derived strata from estimated propensity scores. May be numeric or character vector, or factor. Strata are ordered lexicographically in plot.

boxwex

Numeric; controls width of boxes. Default = 0.17

offset

Numeric; controls distance between the two boxes in each stratum. Default = 0.17

col

Default = c("yellow", "orange", "black", "red", "darkorange3"). Color vector for the control boxes, treatment boxes, and line connecting their means.

xlab

Label for the x-axis; default = "Stratum". Other standard labels may be used as well.

legend.xy

Binary vector giving coordinates of the legend. By default the legend is placed to the top left.

legend.labels

Vector of labels for the legend; default is essentially c("Treatment (first)", "Treatment (second)", "Treatment Means Compared", "KS p-values", "Strata-Treatment Size") where treatment names are taken from treatment. Vector has four elements if balance = FALSE, ommitting "KS p-values".

pts

Logical; if TRUE then (jittered) points are added on top of the boxplots.

balance

Logical; if TRUE then bal.ms.psa provides a histogram of a permutation distribution and reference statstic to assess balance across strata; bal.ks.psa adds p-values to the graph derived from 2-sample Kologmorov-Smirnov tests of equivalence of control/treatment distributions within each stratum.

trim

If balance=TRUE, defines fraction (0 to 0.5) of observations to be trimmed from each end of stratum-treatment level before the mean is computed. See mean, bal.ms.psa.

B

Passed to bal.ms.psa if necessary, determines number of randomly generated comparison statistics. Default =1000.

...

Other graphical parameters passed to boxplot.

Details

Draws a pair of side by side boxplots for each stratum of a propensity score analysis. This allows visual comparisons within strata of the distribution of the given continuous covariate, and comparisons between strata as well. The number of observations in each boxplot are given below each box, and the means of paired treatment and control groups are connected.

Author(s)

James E. Helmreich James.Helmreich@Marist.edu

Robert M. Pruzek RMPruzek@yahoo.com

See Also

bal.ks.psa, bal.ms.psa, cat.psa

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
 
continuous<-rnorm(1000)
treatment<-sample(c(0,1),1000,replace=TRUE)
strata<-sample(5,1000,replace=TRUE)
box.psa(continuous, treatment, strata)

data(lindner)
attach(lindner)
lindner.ps <- glm(abcix ~ stent + height + female + 
      diabetic + acutemi + ejecfrac + ves1proc, 
      data = lindner, family = binomial)
ps<-lindner.ps$fitted
lindner.s5 <- as.numeric(cut(ps, quantile(ps, seq(0, 1, 1/5)),
      include.lowest = TRUE, labels = FALSE))
box.psa(ejecfrac, abcix, lindner.s5, xlab = "ejecfrac", 
      legend.xy = c(3.5,110))

lindner.s10 <- as.numeric(cut(ps, quantile(ps, seq(0, 1, 1/5)),
      include.lowest = TRUE, labels = FALSE))
box.psa(height, abcix, lindner.s10, xlab="height", 
      boxwex = .15, offset = .15, legend.xy = c(2,130), balance = TRUE)

PSAgraphics documentation built on May 2, 2019, 2:42 a.m.