pdf.HurdlePoisson {distributions3} | R Documentation |
Evaluate the probability mass function of a hurdle Poisson distribution
## S3 method for class 'HurdlePoisson'
pdf(d, x, drop = TRUE, elementwise = NULL, ...)
## S3 method for class 'HurdlePoisson'
log_pdf(d, x, drop = TRUE, elementwise = NULL, ...)
d |
A |
x |
A vector of elements whose probabilities you would like to
determine given the distribution |
drop |
logical. Should the result be simplified to a vector if possible? |
elementwise |
logical. Should each distribution in |
... |
Arguments to be passed to |
In case of a single distribution object, either a numeric
vector of length probs
(if drop = TRUE
, default) or a matrix
with
length(x)
columns (if drop = FALSE
). In case of a vectorized distribution
object, a matrix with length(x)
columns containing all possible combinations.
## set up a hurdle Poisson distribution
X <- HurdlePoisson(lambda = 2.5, pi = 0.75)
X
## standard functions
pdf(X, 0:8)
cdf(X, 0:8)
quantile(X, seq(0, 1, by = 0.25))
## cdf() and quantile() are inverses for each other
quantile(X, cdf(X, 3))
## density visualization
plot(0:8, pdf(X, 0:8), type = "h", lwd = 2)
## corresponding sample with histogram of empirical frequencies
set.seed(0)
x <- random(X, 500)
hist(x, breaks = -1:max(x) + 0.5)