reg_dist

Dataset X is binned into 5 bins in x-direction. A regression line is fitted to the
data in each bin and the regression coefficients are noted. Same is done for
dataset PX. An euclidean distance is calculated between the two sets of regression
parameters. If the relationship between X and PX looks linear, number of bins should
be equal to 1.

Tools for visual inference. Generate null data sets
and null plots using permutation and simulation. Calculate distance metrics
for a lineup, and examine the distributions of metrics.

Di Cook

nullabor

Tools for Graphical Inference

Hadley Wickham

Niladri Roy Chowdhury

Heike Hofmann

reg_dist function

<dl><dt>X</dt>
<dd>a data.frame with two variables, the first column giving
the explanatory variable and the second column giving the response
variable</dd>
<dt>PX</dt>
<dd>another data.frame with two variables, the first column giving
the explanatory variable and the second column giving the response
variable</dd>
<dt>nbins</dt>
<dd>number of bins on the x-direction, by default nbins = 1</dd>
<dt>intercept</dt>
<dd>include the distances between intercepts?</dd>
<dt>scale</dt>
<dd>logical value: should the variables be scaled before computing regression coefficients?</dd></dl>

Arguments

Distance based on the regression parameters — reg_dist

<dl>

<dt>X</dt>
<dd>a data.frame with two variables, the first column giving
the explanatory variable and the second column giving the response
variable</dd>


<dt>PX</dt>
<dd>another data.frame with two variables, the first column giving
the explanatory variable and the second column giving the response
variable</dd>


<dt>nbins</dt>
<dd>number of bins on the x-direction, by default nbins = 1</dd>


<dt>intercept</dt>
<dd>include the distances between intercepts?</dd>


<dt>scale</dt>
<dd>logical value: should the variables be scaled before computing regression coefficients?</dd>

</dl>

reg_dist: Distance based on the regression parameters

Description

Usage

Value

Arguments

Examples