```
library('rms')
<- function(wrong = TRUE){
sim #generate completely random data
<- rnorm(100)
y <- rnorm(100)
x #fit a model with a restricted cubic spline
<- ols(y ~ rcs(x))
mod
if (wrong == TRUE & anova(mod)[2, 5] > 0.05){
#if the test for non-linearity is not "significant", remove nonlinear terms
<- ols(y ~ x)
mod
} #save the p-value
anova(mod)[1, 5]
}
```

This is a tale of the dire **type 1 error** consequences that occur when you test for linearity.

*Edvard Munch’s The Scream (1893), coincidentally also the face Frank Harrell makes when he sees students testing for linearity.*

First, my favorite explanation of **type 1 error** 🐺:

@jgschraiber @eagereyes Pro-tip that changed my life: in The Boy Who Cried Wolf, the villagers first make a Type 1, and then a Type 2 error.

— Sam (@geometrywarrior) September 28, 2016

We generally fix (or claim to fix) this **type 1 error** at 0.05, but sometimes our procedures can make this go awry!

I’ve prepared a **very** basic simulation.

- generate 100 data points from two independent random normal distributions, an outcome \(y\) and a predictor \(x\) (Since these are generated randomly, we would
**not**expect there to be an association between \(x\) and \(y\). If all goes as planned, our**type 1 error**would be 0.05) - fit simple linear model with a restricted cubic spline on the predictor \(x\)
- test whether the nonlinear terms are significant
- if they are, leave them in and test the association between \(x\) and \(y\)
- if they are not, remove them and refit the model with only a linear term for \(x\) & proceed to test the association between \(x\) and \(y\).

- calculate the
**type 1 error**, how many times we detected a spurious significant association between \(x\) and \(y\).

Here’s my simulation code (run it yourself!):

hank you Pua Yong Hao for pointing out a typo in the original version of this function – it has been updated!

[Type 1 error when removing non-significant nonlinear terms]

```
<- replicate(10000, sim())
test cat("The type 1 error is", mean(test <= 0.05))
```

`The type 1 error is 0.0812`

Uh oh! That **type 1 error** is certainly higher than the nominal 0.05 we claim!

[Type 1 error when not removing non-significant nonlinear terms]

We would expect the **type 1 error** to be 0.05 – I perform the same simulation omitting the step of removing non-significant nonlinear terms and calculate the **type 1 error** again.

```
<- replicate(10000, sim(wrong = FALSE))
test cat("The type 1 error is", mean(test <= 0.05))
```

`The type 1 error is 0.0501`

Much better 👯

The conclusion: fit flexible models - skip the tests for linearity!

*This has been elegently demonstrated by others, check out Grambsch and O’Brien.*