Fun with residuals
Random stuff II: Plotting residuals
I was poking around my old teaching files and I found an old file and I wasn’t sure what it was:
dat <- read.table("https://raw.githubusercontent.com/flh3/pubdata/main/Stefanski_2007/mizzo_1_data_yx1x5.txt")
head(dat)
## V1 V2 V3 V4 V5 V6
## 1 -0.224 0.00546 0.3803 0.01351 0.2092 0.14671
## 2 0.844 0.10737 -0.0265 0.04586 0.0130 -0.02719
## 3 1.062 0.09112 0.1813 0.05017 -0.1887 -0.01208
## 4 -1.042 0.44049 0.2460 0.00542 -0.2129 0.10152
## 5 0.157 -0.17051 0.1476 0.08363 -0.0953 -0.00785
## 6 -0.135 0.06160 -0.8041 -0.02595 0.2917 -0.07838
dim(dat)
## [1] 3785 6
Turns out it was an old data file I had used in class discussing regression diagnostics. We often talk about the assumption of the homoskedasticity of residuals and we graphically depict that by plotting the fitted values on the X axis and the residuals on the y axis. If all is well, we are told that we should have any discernible pattern.
So this is a dataset of 3,785 observations and 6 variables. We can predict the first variable (V1) using all the other variables in the dataset (V2 to V6).
m1 <- lm(V1 ~ ., data = dat)
If we plot the residuals, we get:
plot(fitted(m1), resid(m1))
Just thought that was neat. This is based on the work of:
Stefanski, L. A. (2007). Residual (sur)realism. The American Statistician, 61(2), 163-177. https://doi.org/10.1198/000313007X190079
I can’t find the original website where this came from but definitely check out the paper!
Here’s the original image:
MU TIGERS
– END
