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 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
Examples
# Using the output of rtrunc
y <- rtrunc(50, mean = 5, sd = 2)
dtrunc(y, eta = c(0, -1))
#> [1] 4.462786e-03 3.657347e-14 5.553769e-01 8.757914e-12 6.671209e-18
#> [6] 4.246301e-24 8.962777e-02 8.630589e-10 8.157554e-10 1.624695e-09
#> [11] 1.481991e-07 5.540320e-18 3.544774e-37 2.751339e-02 9.705737e-17
#> [16] 1.113160e-01 9.012188e-08 2.218966e-11 4.628694e-17 2.410721e-05
#> [21] 2.798921e-16 3.255845e-15 1.856932e-03 3.469359e-19 1.973140e-34
#> [26] 5.296207e-11 3.171696e-05 1.076871e-11 7.726105e-06 1.140410e-02
#> [31] 1.777203e-21 2.027647e-13 4.723673e-14 1.633241e-30 7.926959e-13
#> [36] 1.063543e-10 1.402467e-01 1.527683e-09 2.792192e-09 4.412097e-23
#> [41] 1.893335e-12 5.442740e-07 2.107249e-11 9.301532e-10 4.863101e-16
#> [46] 5.910019e-49 3.062917e-12 1.978639e-17 6.968944e-13 1.413302e-01
# Directly-inputting values
dtruncnorm(y = c(5, 0, -10), eta = c(0, -0.05))
#> [1] 0.0361444785 0.1261566261 0.0008500367