Joachim Zuckarelli walks us through the `xplain`

package in R:

The above XML produces the following output (don’t worry too much about the call of

`xplain()`

, we will discuss later on in more detail how to work with the`xplain()`

function):`library(car) library(xplain) xplain(call="lm(education ~ young + income + urban, data=Anscombe)", xml="http://www.zuckarelli.de/xplain/example_lm_foreach.xml")`

##

## Call:

## lm(formula = education ~ young + income + urban, data = Anscombe)

##

## Coefficients:

## (Intercept) young income urban

## -286.83876 0.81734 0.08065 -0.10581

##

##

## Interpreting the coefficients

## —————————–

## Your coefficient ‘(Intercept)’ is smaller than zero.

##

## Your coefficient ‘young’ is larger than zero. This means that the

## value of your dependent variable ‘education’ changes by 0.82 for

## any increase of 1 in your independent variable ‘young’.

##

## Your coefficient ‘income’ is larger than zero. This means that the

## value of your dependent variable ‘education’ changes by 0.081 for

## any increase of 1 in your independent variable ‘income’.

##

## Your coefficient ‘urban’ is smaller than zero. This means that the

## value of your dependent variable ‘education’ changes by -0.11 for

## any increase of 1 in your independent variable ‘urban’.

I’ll be interested in looking at this in more detail, though my first glance indication is that it’ll be useful mostly in large shops with different teams creating and using models.

Kevin Feasel

2018-05-21

Data Science, R