Open actuarialcat opened 4 years ago
The question require us to scale each observation to have mean 0 and SD 1, not each predictor, in order to produce the desire proportionality.
Suggested answer:
data(USArrests)
scale_data = scale(t(USArrests))
correalation = cor(scale_data) corr.1.minus.r = 1 - as.dist(correalation)
distance.squared = dist(t(scale_data), method = "euclidean") ^ 2
summary(corr.1.minus.r/distance.squared)
The question require us to scale each observation to have mean 0 and SD 1, not each predictor, in order to produce the desire proportionality.
Suggested answer:
data(USArrests)
scale_data = scale(t(USArrests))
correalation = cor(scale_data) corr.1.minus.r = 1 - as.dist(correalation)
distance.squared = dist(t(scale_data), method = "euclidean") ^ 2
summary(corr.1.minus.r/distance.squared)