Evaluate the cumulative distribution function of a Bernoulli distribution

Usage

# S3 method for Bernoulli
cdf(d, x, drop = TRUE, elementwise = NULL, ...)

Arguments

d: A Bernoulli object created by a call to Bernoulli().
x: A vector of elements whose cumulative 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 pbinom. 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 <- Bernoulli(0.7)
X
#> [1] "Bernoulli distribution (p = 0.7)"

mean(X)
#> [1] 0.7
variance(X)
#> [1] 0.21
skewness(X)
#> [1] -0.8728716
kurtosis(X)
#> [1] -1.238095

random(X, 10)
#>  [1] 0 1 0 1 1 1 1 1 1 1
pdf(X, 1)
#> [1] 0.7
log_pdf(X, 1)
#> [1] -0.3566749
cdf(X, 0)
#> [1] 0.3
quantile(X, 0.7)
#> [1] 1

cdf(X, quantile(X, 0.7))
#> [1] 1
quantile(X, cdf(X, 0.7))
#> [1] 0