Evaluate the probability mass function of a LogNormal distribution

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

Usage

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

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

Arguments

d

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

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 dlnorm. 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 LogNormal distribution: cdf.LogNormal(), fit_mle.LogNormal(), quantile.LogNormal(), random.LogNormal()

Examples

set.seed(27)

X <- LogNormal(0.3, 2)
X
#> [1] "LogNormal(log_mu = 0.3, log_sigma = 2)"

random(X, 10)
#>  [1] 61.21089083 13.32648994  0.29256703  0.07317767  0.15153514  2.43630473
#>  [7]  1.36857751 13.66478070 96.47421603  2.17208867

pdf(X, 2)
#> [1] 0.09782712
log_pdf(X, 2)
#> [1] -2.324553

cdf(X, 4)
#> [1] 0.7064858
quantile(X, 0.7)
#> [1] 3.852803