# pdf.LogNormal: Evaluate the probability mass function of a LogNormal... In distributions3: Probability Distributions as S3 Objects

 pdf.LogNormal R Documentation

## Evaluate the probability mass function of a LogNormal distribution

### Description

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.

Other LogNormal distribution: `cdf.LogNormal()`, `fit_mle.LogNormal()`, `quantile.LogNormal()`, `random.LogNormal()`

### Examples

```
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)
```

distributions3 documentation built on Sept. 7, 2022, 5:07 p.m.