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.
# S3 method for LogNormal
pdf(d, x, drop = TRUE, elementwise = NULL, ...)# S3 method for LogNormal
log_pdf(d, x, drop = TRUE, elementwise = NULL, ...)
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.
A LogNormal
object created by a call to LogNormal()
.
A vector of elements whose probabilities you would like to
determine given the distribution d
.
logical. Should the result be simplified to a vector if possible?
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.
Other LogNormal distribution:
cdf.LogNormal()
,
fit_mle.LogNormal()
,
quantile.LogNormal()
,
random.LogNormal()
set.seed(27)
X <- LogNormal(0.3, 2)
X
random(X, 10)
pdf(X, 2)
log_pdf(X, 2)
cdf(X, 4)
quantile(X, 0.7)
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