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,...)
```

## Arguments

- formula
One-sided formula for `svyhist`

, two-sided for `svyboxplot`

- design
A survey design object

- xlab
x-axis label

- main
Main title

- probability,freq
Y-axis is probability density or frequency

- all.outliers
Show all outliers in the boxplot, not just extremes

- breaks, include.lowest, right
As for `hist`

- ...
Other arguments to `hist`

or `bxp`

## Details

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
plotted unless `all.outliers=TRUE`

. `svyboxplot`

requires a two-sided formula; use `variable~1`

for a single boxplot.

## Value

As for `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.

## Examples

```
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)
```