Clever Geek Handbook

Automatically use LRT to assess the significance of the entire factor variable

R for a multivariable regression model that includes one or more factorial variables does not automatically include a likelihood ratio test (LRT) of the value of the entire factorial variable in the model. For example:

fake = data.frame( x1=rnorm(100), x2=sample(LETTERS[1:4], 
       size=100, replace=TRUE), y=rnorm(100) )

head(fake)
          x1 x2          y
1  0.6152511  A  0.7682467
2 -0.8215727  A -0.5389245
3 -1.3287208  A -0.1797851
4  0.5837217  D  0.9509888
5 -0.2828024  C -0.9829126
6  0.3971358  B -0.4895091

m = lm(fake$y ~ fake$x1 + fake$x2)
summary(m)

If we want to test the significance of an entire variable x2

in a model, we can put in a scaled-down model m.red

and use LRT:

m.red = lm(fake$y ~ fake$x1)
anova(m, m.red, test="LRT")

But if there are many factor variables in the model, then it gets ridiculous over and over again. Should I believe there is a built-in approach?

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1 answer


I think what you are looking for is drop1

:



drop1(m,test="Chisq")
## Single term deletions

## Model:
## fake$y ~ fake$x1 + fake$x2
##         Df Sum of Sq    RSS     AIC Pr(>Chi)
## <none>               79.814 -12.547         
## fake$x1  1   0.33741 80.152 -14.125   0.5160
## fake$x2  3   2.88510 82.699 -14.996   0.3142
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