applyGamma

Compute Maximum likelihood estimates of the parameters in Gamma distribution, Score function evaluated at the sample,
and probability inverse transformed (PIT) values of sample.

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test a sample against Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.

Payman Nickchi

gofedf

Goodness of Fit Tests Based on Empirical Distribution Functions

Richard Lockhart

applyGamma function

<dl><dt>x</dt>
<dd>a numeric vector.</dd>
<dt>use.rate</dt>
<dd>logical. If <code>TRUE</code> the rate parameter is returned while estimating MLE. Otherwise the scale is returned.</dd></dl>

Arguments

Apply Gamma distribution to sample and compute required components for the test. — applyGamma

<dl>

<dt>x</dt>
<dd>a numeric vector.</dd>


<dt>use.rate</dt>
<dd>logical. If <code>TRUE</code> the rate parameter is returned while estimating MLE. Otherwise the scale is returned.</dd>

</dl>

applyGamma: Apply Gamma distribution to sample and compute required components for the test.

Description

Usage

Value

Arguments