Currently I am studying QDA and am using R software to analyze my data.
The data was downloaded from the below link:
https://www.kaggle.com/uciml/pima-indians-diabetes-database
I want to check the QDA assumption i.e. the two groups are multivariate normally distributed, hence have used the below command in R.
library(MVN)
group1 <- discrim[1:500, 1:8]
result<- mardiaTest(group1, qqplot = FALSE) #To check whether our data from group1 is MND
group2 <- discrim[501:765, 1:8]
result2 <- mardiaTest(group2, qqplot= TRUE)#To check whether our data from group2 is MND
Both groups are non-normally distributed, so I want to normalize the data and have coded the below to normalize the data for the first group.
x1bar <- t(t(as.vector(sapply(as.data.frame(group1),mean))))
x1bartilda<- (x1bar - mean(x1bar))/sd(x1bar)
Similarly for group2, however mean vector X for group1 didn't give a result that is close to 0.
Can anyone help me what is the way forward please?
If you want to normalize with mean 0 and std 1 you can use scale commmand.
Example:
my_data <- data.frame(x = rnorm(10, 30, .2), y = runif(10, 3, 5))
my_data_scaled <- scale(my_data)
summary(my_data_scaled)
The result is:
x y
Min. :-1.91046 Min. :-1.59037
1st Qu.:-0.57552 1st Qu.:-0.39842
Median : 0.06104 Median : 0.01998
Mean : 0.00000 Mean : 0.00000
3rd Qu.: 0.47280 3rd Qu.: 0.84296
Max. : 1.74638 Max. : 1.10514
The mean is 0.