Cumulative Distribution Function

Share:

Description

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.

Usage

1
2
3
4
5
svycdf(formula, design, na.rm = TRUE,...)
## S3 method for class 'svycdf'
print(x,...)
## S3 method for class '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)

See Also

svyquantile, svyhist, plot.stepfun

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
data(api)
dstrat <- svydesign(id = ~1, strata = ~stype, weights = ~pw, data = apistrat, 
    fpc = ~fpc)
cdf.est<-svycdf(~enroll+api00+api99, dstrat)
cdf.est
## function
cdf.est[[1]]
## evaluate the function
cdf.est[[1]](800)
cdf.est[[2]](800)

## 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)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.