sens_student

Expands (<code>sd_mult &gt; 1</code>) or reduces (<code>sd_mult &lt; 1</code>) the standard deviation
of the Student's t distribution. Because the variance of this distribution
is not defined for every degree of freedom, the adjustment to the standard
deviation is approximate, and the mean of the adjusted distribution can
be expected to have shifted.

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_student function

<dl><dt>mean</dt>
<dd>A numeric vector of the means.</dd>
<dt>sd</dt>
<dd>A non-negative numeric vector of the standard deviations.</dd>
<dt>theta</dt>
<dd>A non-negative numeric vector of the dispersion for the mixture
models (student, gamma-Poisson and beta-binomial).</dd>
<dt>sd_mult</dt>
<dd>A non-negative multiplier on the standard deviation of the
distribution.</dd></dl>

Arguments

Adjust Student's t Distribution Parameters for Sensitivity Analyses — sens_student

<dl>

<dt>mean</dt>
<dd>A numeric vector of the means.</dd>


<dt>sd</dt>
<dd>A non-negative numeric vector of the standard deviations.</dd>


<dt>theta</dt>
<dd>A non-negative numeric vector of the dispersion for the mixture
models (student, gamma-Poisson and beta-binomial).</dd>


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

</dl>

sens_student: Adjust Student's t Distribution Parameters for Sensitivity Analyses

Description

Usage

Value

Arguments

See Also

Examples