Estimates the population cumulative distribution function for specified variables. In contrast to svyquantile, this does not do any interpolation: the result is a right-continuous step function.

svycdf(formula, design, na.rm = TRUE,...)
# S3 method for svycdf
print(x,...)
# S3 method for svycdf
plot(x,xlab=NULL,...)

## Arguments

formula

one-sided formula giving variables from the design object

design

survey design object

na.rm

remove missing data (case-wise deletion)?

...

other arguments to plot.stepfun

x

object of class svycdf

xlab

a vector of x-axis labels or NULL for the default labels

## Value

An object of class svycdf, which is a list of step functions (of class stepfun)

svyquantile, svyhist, plot.stepfun

## Examples

data(api)
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat,
fpc = ~fpc)
cdf.est<-svycdf(~enroll+api00+api99, dstrat)
cdf.est
#> Weighted ECDFs: svycdf(~enroll + api00 + api99, dstrat)
## function
cdf.est[[1]]
#> Step function
#> Call: svycdf.default(formula = ~enroll, design = dstrat)
#>  x[1:187] =    119,    143,    153,  ...,   2552,   3156
#> 188 plateau levels =      0, 0.0024378, 0.0095754,  ..., 0.99756,      1
## evaluate the function
cdf.est[[1]](800)
#> [1] 0.8223603
cdf.est[[2]](800)
#> [1] 0.8463626

## compare to population and sample CDFs.
opar<-par(mfrow=c(2,1))
cdf.pop<-ecdf(apipop$enroll) cdf.samp<-ecdf(apistrat$enroll)
plot(cdf.pop,main="Population vs sample", xlab="Enrollment")
lines(cdf.samp,col.points="red")

plot(cdf.pop, main="Population vs estimate", xlab="Enrollment")
lines(cdf.est[[1]],col.points="red")

par(opar)