applyLMNormal

Apply linear model and extract required components for the test

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

applyLMNormal function

<dl><dt>x</dt>
<dd>is either a numeric vector or a design matrix. In the design matrix, rows indicate observations and columns
presents explanatory variables.</dd>
<dt>y</dt>
<dd>is a vector of numeric values with the same number of observations or number of rows as x.</dd></dl>

Arguments

Apply linear model and extract required components for the test — applyLMNormal

<dl>

<dt>x</dt>
<dd>is either a numeric vector or a design matrix. In the design matrix, rows indicate observations and columns
presents explanatory variables.</dd>


<dt>y</dt>
<dd>is a vector of numeric values with the same number of observations or number of rows as x.</dd>

</dl>

applyLMNormal: Apply linear model and extract required components for the test

Description

Usage

Value

Arguments