ssd_gof

ssd_gof.fitdists

Returns a tbl data frame with the following columns<dl>
<dt>dist</dt>
<dd>The distribution name (chr)</dd><dt>aic</dt>
<dd>Akaike's Information Criterion (dbl)</dd><dt>bic</dt>
<dd>Bayesian Information Criterion (dbl)</dd><dt>at_bound</dt>
<dd>Parameter(s) at boundary (lgl)</dd><dt>computable</dt>
<dd>All parameter have computable standard errors (lgl)</dd>
</dl>and if the data are non-censored<dl>
<dt>aicc</dt>
<dd>Akaike's Information Criterion corrected for sample size (dbl)</dd>
</dl>and if there are 8 or more samples<dl>
<dt>ad</dt>
<dd>Anderson-Darling statistic (dbl)</dd><dt>ks</dt>
<dd>Kolmogorov-Smirnov statistic (dbl)</dd><dt>cvm</dt>
<dd>Cramer-von Mises statistic (dbl)</dd>
</dl>In the case of an object of class fitdists the function also returns<dl>
<dt>delta</dt>
<dd>The Information Criterion differences (dbl)</dd><dt>wt</dt>
<dd>The Information Criterion weights (dbl)</dd>
</dl>where <code>delta</code> and <code>wt</code> are based on <code>aic</code> for censored data
and <code>aicc</code> for non-censored data.

Species sensitivity distributions are cumulative probability
distributions which are fitted to toxicity concentrations for
different species as described by Posthuma et al.(2001)
<isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to
fit distributions such as the gamma, log-logistic, log-normal and
log-normal log-normal mixture. Multiple distributions can be averaged
using Akaike Information Criteria. Confidence intervals on hazard
concentrations and proportions are produced by bootstrapping.

Joe Thorley

ssdtools

Species Sensitivity Distributions

Rebecca Fisher

David Fox

Carl Schwarz

Angeline Tillmanns

Seb Dalgarno

Kathleen McTavish

Heather Thompson

Doug Spry

Rick van Dam

Graham Batley

Ali Azizishirazi

Nadine Hussein

Sarah Lyons

Duncan Kennedy

Stephanie Hazlitt

Hadley Wickham

Sergio Ibarra Espinosa

Andy Teucher

Emilie Doussantousse

Nan-Hung Hsieh

Florencia D'Andrea

Eduard Szöcs

Province of British Columbia 

Environment and Climate Change Canada 

Australian Government Department of Climate Change, Energy, the Environment and Water 

ssd_gof function

<dl><dt>x</dt>
<dd>The object.</dd>
<dt>...</dt>
<dd>Unused.</dd>
<dt>pvalue</dt>
<dd>A flag specifying whether to return p-values or the statistics (default) for the various tests.</dd>
<dt>wt</dt>
<dd>A flag specifying whether to return the Akaike weight as "wt" instead of "weight".</dd></dl>

Arguments

<ul>
<li><code>ssd_gof(fitdists)</code>: Goodness of Fit</li>
</ul>

Methods (by class)

Returns a tbl data frame with the following columns<dl>
<dt>dist</dt>
<dd>The distribution name (chr)</dd>

<dt>aic</dt>
<dd>Akaike's Information Criterion (dbl)</dd>

<dt>bic</dt>
<dd>Bayesian Information Criterion (dbl)</dd>

<dt>at_bound</dt>
<dd>Parameter(s) at boundary (lgl)</dd>

<dt>computable</dt>
<dd>All parameter have computable standard errors (lgl)</dd>


</dl>and if the data are non-censored<dl>
<dt>aicc</dt>
<dd>Akaike's Information Criterion corrected for sample size (dbl)</dd>


</dl>and if there are 8 or more samples<dl>
<dt>ad</dt>
<dd>Anderson-Darling statistic (dbl)</dd>

<dt>ks</dt>
<dd>Kolmogorov-Smirnov statistic (dbl)</dd>

<dt>cvm</dt>
<dd>Cramer-von Mises statistic (dbl)</dd>


</dl>In the case of an object of class fitdists the function also returns<dl>
<dt>delta</dt>
<dd>The Information Criterion differences (dbl)</dd>

<dt>wt</dt>
<dd>The Information Criterion weights (dbl)</dd>


</dl>where <code>delta</code> and <code>wt</code> are based on <code>aic</code> for censored data
and <code>aicc</code> for non-censored data.

Goodness of Fit — ssd_gof

<dl>

<dt>x</dt>
<dd>The object.</dd>


<dt>...</dt>
<dd>Unused.</dd>


<dt>pvalue</dt>
<dd>A flag specifying whether to return p-values or the statistics (default) for the various tests.</dd>


<dt>wt</dt>
<dd>A flag specifying whether to return the Akaike weight as "wt" instead of "weight".</dd>

</dl>

Goodness of Fit

ssd_gof: Goodness of Fit

Description

Usage

Value

Arguments

Methods (by class)

See Also

Examples