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Evaluate the probability mass function of a Logistic distribution

Source: R/Logistic.R
pdf.Logistic.Rd

Please see the documentation of Logistic() for some properties of the Logistic distribution, as well as extensive examples showing to how calculate p-values and confidence intervals.

Usage

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

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

Arguments

d

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

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 dlogis. 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.

See also

Other Logistic distribution: cdf.Logistic(), quantile.Logistic(), random.Logistic()

Examples


set.seed(27)

X <- Logistic(2, 4)
X
#> [1] "Logistic(location = 2, scale = 4)"

random(X, 10)
#>  [1]  16.1520541  -7.5694209   9.7424712  -0.8466541  -3.0098187   0.4055911
#>  [7]  -8.1957130 -22.0364748  -5.3585558  -3.7506119

pdf(X, 2)
#> [1] 0.0625
log_pdf(X, 2)
#> [1] -2.772589

cdf(X, 4)
#> [1] 0.6224593
quantile(X, 0.7)
#> [1] 5.389191