lmFisherByHessian

Compute Fisher information matrix in the case of linear model with Normal residuals.

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

lmFisherByHessian 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 Fisher information matrix in the case of linear model with Normal residuals. — lmFisherByHessian

<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>

lmFisherByHessian: Compute Fisher information matrix in the case of linear model with Normal residuals.

Description

Usage

Value

Arguments