Cross Validated Asked by Jordan on September 8, 2020

I’m wondering if someone can assist me in extracting GLM relativities when using splines? I have searched CV and cannot find an easily understandable answer.

Here is some code in R (my apologies as I do not know python).

```
#load segemented package for plant data
library(segmented)
#load splines package
library(splines)
#get data
data <- data("plant")
#run GLM and get summary
glm_model <- glm(y ~ time, data = plant)
summary(glm_model)
#add spline and get summary
glm_model2 <- glm(y ~ bs(time, degree = 1, knots = c(366.5)), data = plant)
summary(glm_model2)
```

Here is the summary from the splines model.

`Call:`

`glm(formula = y ~ bs(time, degree = 1, knots = c(366.5)), data = plant)`

`Deviance Residuals:`

`Min 1Q Median 3Q Max`

`-0.37187 -0.15317 0.05867 0.12065 0.23452`

`Coefficients:`

`Estimate Std.`

`Error t value`

`Pr(>|t|)`

`(Intercept) 0.36133 0.04236 8.529 1.63e-13 ***`

`bs(time, degree = 1, knots = c(366.5))1 0.48467 0.05794 8.365 3.71e-13 ***`

`bs(time, degree = 1, knots = c(366.5))2 0.42415 0.05469 7.756 7.59e-12 ***`

How would I convert the polynomial coeefficients in the spline summary to actual GLM relativities? It doesn’t need to be in `R`

if someone can explain the math but if anyone has an `R`

example, that would be great!

By "relative" do you mean "relative risk"? If so, this question doesn't have a simple answer, since it will be different for different values of $x$ (time). You can do this empirically for two values of time. Something like this should work:

```
pred1 <- predict(glm_model, data.frame(time = whatever1), type = 'response')
pred2 <- predict(glm_model, data.frame(time = whatever2), type = 'response')
relative_risk <- pred1 / pred2
```

Answered by Tim Atreides on September 8, 2020

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