Diagnostics for linear L1 regression (also known as LAD - Least Absolute Deviations), including: estimation, confidence intervals, tests of hypotheses, measures of leverage, methods of diagnostics for L1 regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005) <doi:10.1080/0094965042000223680>, Elian et al. (2000) <doi:10.1080/03610920008832518> and Dodge (1997) <doi:10.1006/jmva.1997.1666>. This package builds on the 'quantreg' package, which is a well-established package for tuning quantile regression models. There are also tests to verify if the errors have a Laplace distribution based on the work of Puig and Stephens (2000) <doi:10.2307/1270952>.

Kevin Rodrigues

diagL1

Routines for Fit, Inference and Diagnostics in Linear L1 and LAD
Models

Kevin Allan Sales Rodrigues

Silvia Nagib Elian

PIF function

<dl><dt>y</dt>
<dd>A vector with response variables.</dd>
<dt>x</dt>
<dd>A matrix with a single explanatory variable.</dd>
<dt>w</dt>
<dd>Explanatory variables vector for z, usually explanatory variables averages.</dd>
<dt>num_cores</dt>
<dd>Number of cores you want to use for parallel processing (default = 2).</dd></dl>

Arguments

Predictive Influence Function — PIF

<dl>

<dt>y</dt>
<dd>A vector with response variables.</dd>


<dt>x</dt>
<dd>A matrix with a single explanatory variable.</dd>


<dt>w</dt>
<dd>Explanatory variables vector for z, usually explanatory variables averages.</dd>


<dt>num_cores</dt>
<dd>Number of cores you want to use for parallel processing (default = 2).</dd>

</dl>

PIF: Predictive Influence Function

Description

Usage

Value

Arguments

Details

References