ieaggie - 1 month ago 11

R Question

I've been wracking my brains over this and cannot figure out what the best method to go about this is.

So far, I conducted my initial MLR

`reg=lm(register~.,data=train)`

From there, I checked for interaction effects using

`testinter=glm(register~(.-atemp)*(.-atemp),data=train)`

After Determining the Significant Interactions, I included all in my model.

`reg=lm(register~season:month+season:temp+year:month+`

year:weekday+year:temp+month:temp+holiday:windspeed+weekday:

weathersit+weekday:hum+weathersit:hum+

hum+weathersit+season+year+temp, data=train)

However, after looking at some of the variables, hum and temp needed to be transformed to polynomial terms. hum^2 and temp^3.

My question is how can I include these in the interaction effects?

This is my attempt so far, where I switched out "hum" with poly(hum,2,raw=T) but Im not sure if its correct.

`reg=lm(register~season:month+season:poly(temp,3,raw=T)+year:month+`

year:weekday+year:poly(temp,3,raw=T)+month:poly(temp,3,raw=T)+holiday:windspeed+weekday:

weathersit+weekday:poly(hum,2,raw=T)+weathersit:poly(hum,2,raw=T)+

poly(hum,2,raw=T)+weathersit+season+year+poly(temp,3,raw=T), data=train)

Answer

You should use `I()`

when you need to do a transformation within an `lm`

or `glm`

equation.

Here's an example using the `iris`

dataset:

```
data(iris)
reg <- lm(Sepal.Length~Sepal.Width:I(Petal.Length^2), data=iris)
summary(reg)
```

`Call: lm(formula = Sepal.Length ~ Sepal.Width:I(Petal.Length^2), data = iris) Residuals: Min 1Q Median 3Q Max -0.9405 -0.2357 0.0029 0.2224 0.8241 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.8649126 0.0478357 101.70 <2e-16 *** Sepal.Width:I(Petal.Length^2) 0.0192699 0.0007492 25.72 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.3552 on 148 degrees of freedom Multiple R-squared: 0.8172, Adjusted R-squared: 0.816 F-statistic: 661.6 on 1 and 148 DF, p-value: < 2.2e-16`

Source (Stackoverflow)

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