Histograms and boxplots weighted by the sampling weights.
svyhist(formula, design, breaks = "Sturges", include.lowest = TRUE, right = TRUE, xlab = NULL, main = NULL, probability = TRUE, freq = !probability, ...) svyboxplot(formula, design, all.outliers=FALSE,...)
One-sided formula for
svyhist, two-sided for
A survey design object
Y-axis is probability density or frequency
Show all outliers in the boxplot, not just extremes
The histogram breakpoints are computed as if the sample were a simple random sample of the same size.
The grouping variable in
svyboxplot, if present, must be a factor.
The boxplot whiskers go to the maximum and minimum observations or to
1.5 interquartile ranges beyond the end of the box, whichever is
closer. The maximum and minimum are plotted as outliers if they are
beyond the ends of the whiskers, but other outlying points are not
requires a two-sided formula; use
variable~1 for a single boxplot.
hist, except that when
probability=FALSE, the return value includes a component
count_scale giving a scale factor between density and
counts, assuming equal bin widths.
data(api) dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, fpc = ~fpc) opar<-par(mfrow=c(1,3)) svyhist(~enroll, dstrat, main="Survey weighted",col="purple",ylim=c(0,1.3e-3)) hist(apistrat$enroll, main="Sample unweighted",col="purple",prob=TRUE,ylim=c(0,1.3e-3)) hist(apipop$enroll, main="Population",col="purple",prob=TRUE,ylim=c(0,1.3e-3)) par(mfrow=c(1,1)) svyboxplot(enroll~stype,dstrat,all.outliers=TRUE) svyboxplot(enroll~1,dstrat) par(opar)