Cumulative Distribution Function

DESCRIPTION:

Empirical cumulative distribution function. This is a generic function; there currently exists a method for resamp objects, described separately.

USAGE:

cdf(x, q, weights=NULL, na.rm=F, normalize=T) 

REQUIRED ARGUMENTS:

x
numerical data.
q
numerical, values at which to compute the distribution function.

OPTIONAL ARGUMENTS:

weights
a vector the same length as x, containing non-negative weights (probabilities) to define a distribution function with unequal jumps. The weights are normalized to sum to 1.
na.rm
if TRUE, missing values ( NA) in x or weights cause the corresponding observations to be removed. If FALSE, missing values in x and weights are an error.
normalize
if FALSE, then return the number (rather than proportion) of the x values less than or equal to each q, or the sum of the corresponding unnormalized weights.

VALUE:

vector the same length as q, containing F( q), where F is the empirical distribution function defined by data x. Missing values in q cause missing values in the result.

SEE ALSO:

, , , .

EXAMPLES:

x <- rnorm(20) 
z <- seq(-3,3,by=.1) 
cdf(x, z) 
plot(z, cdf(x, z)) 
cdf(x, z, weights=1:20) 
pdiscrete(z, x, weights=1:20)