TruncExpFam (version 1.2.1)

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

Value

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

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. For discrete distributions, a will be included in the support of the truncated distribution.

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 untruncated distribution

sdlog

standard deviation of untruncated distribution

mean

mean of parent distribution

sd

standard deviation is parent distribution

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))