sens_lnorm

Expands (<code>sd_mult &gt; 1</code>) or reduces (<code>sd_mult &lt; 1</code>) the standard deviation
of the Log-Normal distribution. With high values of <code>sdlog</code> (i.e., <code>&gt; 9</code>),
and <code>sd_mult &gt; 1</code>, the mean of the adjusted distribution can be expected to
have a mean value that is very different from the original mean, however,
the proportional difference in these values should not be very different.

Functions to 'numericise' 'R' objects (coerce to numeric
objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and
calculate deviance residuals as well as 'R' translations of some
'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs
Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.

Nicole Hill

extras

Helper Functions for Bayesian Analyses

Joe Thorley

Kirill Müller

Nadine Hussein

Poisson Consulting 

sens_lnorm function

<dl><dt>meanlog</dt>
<dd>A numeric vector of the means on the log scale.</dd>
<dt>sdlog</dt>
<dd>A non-negative numeric vector of the standard deviations on the
log scale.</dd>
<dt>sd_mult</dt>
<dd>A non-negative multiplier on the standard deviation of the
distribution.</dd></dl>

Arguments

Adjust Log-Normal Distribution Parameters for Sensitivity Analysis — sens_lnorm

<dl>

<dt>meanlog</dt>
<dd>A numeric vector of the means on the log scale.</dd>


<dt>sdlog</dt>
<dd>A non-negative numeric vector of the standard deviations on the
log scale.</dd>


<dt>sd_mult</dt>
<dd>A non-negative multiplier on the standard deviation of the
distribution.</dd>

</dl>

sens_lnorm: Adjust Log-Normal Distribution Parameters for Sensitivity Analysis

Description

Usage

Value

Arguments

See Also

Examples