dtruncbeta {TruncExpFam}

Probability Density Function

Description

Calculates the PDF for a given truncated distribution

Usage

dtruncbeta(y, shape1, shape2, eta, a = 0, b = 1, ...)

dtruncbinom(y, size, prob, eta, a = 0, b = attr(y, "parameters")$size, ...)

dtruncchisq(y, df, eta, a = 0, b = Inf, ...)

dtrunccontbern(y, lambda, eta, a = 0, b = 1, ...)

dtrunccontbern(y, lambda, eta, a = 0, b = 1, ...)

dtruncexp(y, rate = 1, eta, a = 0, b = Inf, ...)

dtruncgamma(y, shape, rate = 1, scale = 1/rate, eta, a = 0, b = Inf, ...)

dtrunc(y, ...)

dtruncinvgamma(y, shape, rate = 1, scale = 1/rate, eta, a = 0, b = Inf, ...)

dtruncinvgauss(y, m, s, eta, a = 0, b = Inf, ...)

dtrunclnorm(y, meanlog = 0, sdlog = 1, eta, a = 0, b = Inf, ...)

## S3 method for class 'trunc_nbinom'
dtrunc(y, size, prob, eta, a = 0, b = Inf, ...)

dtruncnbinom(y, size, prob, eta, a = 0, b = Inf, ...)

dtruncnbinom(y, size, prob, eta, a = 0, b = Inf, ...)

dtruncnorm(y, mean = 0, sd = 1, eta, a = -Inf, b = Inf, ...)

dtruncpois(y, lambda, eta, a = 0, b = Inf, ...)

Arguments

y

output from rtrunc or any valid numeric value(s).

shape1

positive shape parameter alpha

shape2

positive shape parameter beta

eta

vector of natural parameters

a

point of left truncation

b

point of right truncation

...

size

size

target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer.

prob

probability of success on each trial

df

degrees of freedom for "parent" distribution

lambda

mean and var of "parent" distribution

rate

inverse gamma rate parameter

shape

inverse gamma shape parameter

scale

inverse gamma scale parameter

m

vector of means

s

vector of dispersion parameters

meanlog

mean of un-truncated distribution

sdlog

standard deviation of un-truncated distribution

mean

mean of parent distribution

sd

standard deviation is parent distribution

Value

The density of y for the given values of the eta parameter.

Note

Either the common or the natural parameters must be provided.

Examples

# Using the output of rtrunc
y <- rtrunc(50, mean = 5, sd = 2)
dtrunc(y, eta = c(0, -1))

# Directly-inputting values
dtruncnorm(y = c(5, 0, -10), eta = c(0, -0.05))