outlier_stats

<code>outlier_stats</code> returns the ordered
studentized residuals (decreasing based on magnitude) of
<code>model</code> to
Identify the most unusual observations.

Simplifies aspects of linear regression analysis, particularly simultaneous inference. Additionally, supports "A Progressive Introduction to Linear Models" by Joshua French (<https://jfrench.github.io/LinearRegression/>).

Joshua French

api2lm

Functions and Data Sets for the Book "A Progressive Introduction
to Linear Models"

outlier_stats function

<dl><dt>model</dt>
<dd>A fitted model object from the
<code><a href="/link/lm?package=api2lm&version=0.2" data-mini-rdoc="api2lm::lm">lm</a></code> function.</dd>
<dt>n</dt>
<dd>an integer vector of length up to <code>dim(x)</code> (or 1,
 for non-dimensioned objects). Values specify the indices to be
 selected in the corresponding dimension (or along the length) of the
 object. A positive value of <code>n[i]</code> includes the first/last
 <code>n[i]</code> indices in that dimension, while a negative value
 excludes the last/first <code>abs(n[i])</code>, including all remaining
 indices. <code>NA</code> or non-specified values (when <code>length(n) &lt;
 length(dim(x))</code>) select all indices in that dimension. Must
 contain at least one non-missing value.</dd></dl>

Arguments

Outlier statistics — outlier_stats

<dl>

<dt>model</dt>
<dd>A fitted model object from the
<code><a href='https://rdrr.io/r/stats/lm.html'>lm</a></code> function.</dd>


<dt>n</dt>
<dd>an integer vector of length up to <code>dim(x)</code> (or 1,
 for non-dimensioned objects). Values specify the indices to be
 selected in the corresponding dimension (or along the length) of the
 object. A positive value of <code>n[i]</code> includes the first/last
 <code>n[i]</code> indices in that dimension, while a negative value
 excludes the last/first <code>abs(n[i])</code>, including all remaining
 indices. <code>NA</code> or non-specified values (when <code>length(n) &lt;
 length(dim(x))</code>) select all indices in that dimension. Must
 contain at least one non-missing value.</dd>

</dl>

outlier_stats: Outlier statistics

Description

Usage

Value

Arguments

Examples