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(This article was first published on <strong><a href="https://designdatadecisions.wordpress.com/2016/05/05/manipulated-regression/"> R – Design Data Decisions</a></strong>, and kindly contributed to <a rel="nofollow" href="http://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>The R package ‘<em>manipulate</em>’ can be used to create interactive plots in RStudio. Though not as versatile as the ‘<em>shiny</em>’ package, <em>‘manipulate</em>’ can be used to quickly add interactive elements to standard R plots. This can prove useful for demonstrating statistical concepts, especially to a non-statistician audience.</p>
<p>The R code at the end of this post uses the ‘<em>manipulate</em>’ package with a regression plot to illustrate the effect of outliers (and influential) points on the fitted linear regression model. The resulting manipulate(d) plot in RStudio includes a gear icon, which, when clicked, opens up a slider control. The slider can be used to move some data points. The plot changes interactively with the data.</p>
<p>Here are some static figures:</p>
<p><strong>Initial state</strong>: It is possible to move two points in the scatter plot, one at the end and one at the center.<a target="_blank" href="https://designdatadecisions.files.wordpress.com/2016/05/initial-e1462448612616.png"><img height="197" alt="Initial" width="300" class="wp-image-237" src="https://designdatadecisions.files.wordpress.com/2016/05/initial-e1462448612616.png?w=300&h=197"></a></p>
<p>An outlier at center has a limited influence on the fitted regression model.</p>
<p><a target="_blank" href="https://designdatadecisions.files.wordpress.com/2016/05/movemidy-e1462449080632.png"><img height="204" alt="MoveMidY" width="300" class="wp-image-238" src="https://designdatadecisions.files.wordpress.com/2016/05/movemidy-e1462449080632.png?w=300&h=204"></a></p>
<p>An outlier at the ends of support of x and y ‘moves’ the regression line towards it and is also an<em> influential</em> <em>point!</em></p>
<p><a target="_blank" href="https://designdatadecisions.files.wordpress.com/2016/05/moveendy-e1462449244585.png"><img height="201" alt="MoveEndY" width="300" class="wp-image-239" src="https://designdatadecisions.files.wordpress.com/2016/05/moveendy-e1462449244585.png?w=300&h=201"></a></p>
<p>Here is the complete R code for generating the interactive plot. This is to be run in RStudio.</p>
<pre>
library(manipulate)

## First define a custom function that fits a linear regression line 
## to (x,y) points and overlays the regression line in a scatterplot.
## The plot is then 'manipulated' to change as y values change.

linregIllustrate &lt;- function(x, y, e, h.max, h.med){
  max.x &lt;- max(x)
  med.x &lt;- median(x)
  max.xind &lt;- which(x == max.x)
  med.xind &lt;- which(x == med.x)

  y1 &lt;- y     ## Modified y
  y1[max.xind] &lt;- y1[max.xind]+h.max  ## at the end
  y1[med.xind] &lt;- y1[med.xind]+h.med  ## at the center
  plot(x, y1, xlim=c(min(x),max(x)+5), ylim=c(min(y1),max(y1)), pch=16, 
       xlab=&quot;X&quot;, ylab=&quot;Y&quot;)
  text(x[max.xind], y1[max.xind],&quot;I'm movable!&quot;, pos=3, offset = 0.3, cex=0.7, font=2, col=&quot;red&quot;)
  text(x[med.xind], y1[med.xind],&quot;I'm movable too!&quot;, pos=3, offset = 0.3, cex=0.7, font=2, col=&quot;red&quot;)
  
  m &lt;- lm(y ~ x)  ## Regression with original set of points, the black line
  abline(m, lwd=2)

  m1 &lt;- lm(y1 ~ x)  ## Regression with modified y, the dashed red line
  abline(m1, col=&quot;red&quot;, lwd=2, lty=2)
}

## Now generate some x and y data 
x &lt;- rnorm(35,10,5)
e &lt;- rnorm(35,0,5)
y &lt;- 3*x+5+e

## Plot and manipulate the plot!
manipulate(linregIllustrate(x, y, e, h.max, h.med), 
           h.max=slider(-100, 100, initial=0, step=10, label=&quot;Move y at the end&quot;), 
           h.med=slider(-100, 100, initial=0, step=10, label=&quot;Move y at the center&quot;))
</pre>
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