Evaluate the probability density function of an Exponential distribution

Usage

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

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

Arguments

d: An Exponential object created by a call to Exponential().
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 dexp. 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 <- Exponential(5)
X
#> [1] "Exponential(rate = 5)"

mean(X)
#> [1] 0.2
variance(X)
#> [1] 0.04
skewness(X)
#> [1] 2
kurtosis(X)
#> [1] 6

random(X, 10)
#>  [1] 0.01161126 0.28730930 1.15993941 0.29660927 0.38431337 0.04643808
#>  [7] 0.06969554 0.10900366 0.50608948 0.03759968

pdf(X, 2)
#> [1] 0.0002269996
log_pdf(X, 2)
#> [1] -8.390562

cdf(X, 4)
#> [1] 1
quantile(X, 0.7)
#> [1] 0.2407946

cdf(X, quantile(X, 0.7))
#> [1] 0.7
quantile(X, cdf(X, 7))
#> [1] 6.989008