lmPIT

Compute probability inverse transform values for linear models.

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

lmPIT function

<dl><dt>x</dt>
<dd>a matrix with n rows and p columns containing the explanatory variables.</dd>
<dt>y</dt>
<dd>a numeric vector of length n containing the response variable.</dd>
<dt>theta</dt>
<dd>a numeric vector of length (p+1), containing MLE of parameters in a linear model.</dd></dl>

Arguments

Compute probability inverse transform values for linear models. — lmPIT

<dl>

<dt>x</dt>
<dd>a matrix with n rows and p columns containing the explanatory variables.</dd>


<dt>y</dt>
<dd>a numeric vector of length n containing the response variable.</dd>


<dt>theta</dt>
<dd>a numeric vector of length (p+1), containing MLE of parameters in a linear model.</dd>

</dl>

lmPIT: Compute probability inverse transform values for linear models.

Description

Usage

Value

Arguments