Determine quantiles of a Beta distribution

quantile() is the inverse of cdf().

Usage

# S3 method for Beta
quantile(x, probs, drop = TRUE, elementwise = NULL, ...)

Arguments

x

A Beta object created by a call to Beta().

probs

A vector of probabilities.

drop

logical. Should the result be simplified to a vector if possible?

elementwise

logical. Should each distribution in x be evaluated at all elements of probs (elementwise = FALSE, yielding a matrix)? Or, if x and probs 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 qbeta. 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(probs) columns (if drop = FALSE). In case of a vectorized distribution object, a matrix with length(probs) columns containing all possible combinations.

Examples

set.seed(27)

X <- Beta(1, 2)
X
#> [1] "Beta distribution (alpha = 1, beta = 2)"

random(X, 10)
#>  [1] 0.014327255 0.067309943 0.636292291 0.864804440 0.758869543 0.237550867
#>  [7] 0.330895959 0.065843704 0.008265406 0.254705779

pdf(X, 0.7)
#> [1] 0.6
log_pdf(X, 0.7)
#> [1] -0.5108256

cdf(X, 0.7)
#> [1] 0.91
quantile(X, 0.7)
#> [1] 0.4522774

mean(X)
#> [1] 0.3333333
variance(X)
#> [1] 0.05555556
skewness(X)
#> [1] 1.131371
kurtosis(X)
#> [1] -0.6

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