Evaluate the probability mass function of a GP distribution

Usage

# S3 method for class 'GP'
pdf(d, x, drop = TRUE, elementwise = NULL, ...)

# S3 method for class 'GP'
log_pdf(d, x, drop = TRUE, elementwise = NULL, ...)

Arguments

d: A GP object created by a call to GP().
x: A vector of elements whose probabilities you would like to determine given the distribution d.
drop: logical. Should the result be simplified to a vector if possible?
elementwise: 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 dgp. Unevaluated arguments will generate a warning to catch mispellings or other possible errors.

Value

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.

Examples


set.seed(27)

X <- GP(0, 2, 0.1)
X
#> [1] "GP(mu = 0, sigma = 2, xi = 0.1)"

random(X, 10)
#>  [1] 8.571201574 0.175715851 4.600737645 0.814822940 0.509138521 1.053986338
#>  [7] 0.151089620 0.004907082 0.297083889 0.430734122

pdf(X, 0.7)
#> [1] 0.3424729
log_pdf(X, 0.7)
#> [1] -1.071563

cdf(X, 0.7)
#> [1] 0.2910812
quantile(X, 0.7)
#> [1] 2.558897

cdf(X, quantile(X, 0.7))
#> [1] 0.7
quantile(X, cdf(X, 0.7))
#> [1] 0.7