lmMLE

Compute maximum likelihood estimates 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

lmMLE 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></dl>

Arguments

Compute maximum likelihood estimates for linear models — lmMLE

<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>

</dl>

lmMLE: Compute maximum likelihood estimates for linear models

Description

Usage

Value

Arguments