Evaluate the probability mass function of a hurdle Poisson distribution
# S3 method for HurdlePoisson
pdf(d, x, drop = TRUE, elementwise = NULL, ...)# S3 method for HurdlePoisson
log_pdf(d, x, drop = TRUE, elementwise = NULL, ...)
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.
A HurdlePoisson object created by a call to HurdlePoisson().
A vector of elements whose probabilities you would like to
determine given the distribution d.
logical. Should the result be simplified to a vector if possible?
logical. Should each distribution in d be evaluated
at all elements of x (elementwise = FALSE, yielding a matrix)?
Or, if d and x have the same length, should the evaluation be
done element by element (elementwise = TRUE, yielding a vector)? The
default of NULL means that elementwise = TRUE is used if the
lengths match and otherwise elementwise = FALSE is used.
Arguments to be passed to dhpois.
Unevaluated arguments will generate a warning to catch mispellings or other
possible errors.
## 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)
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