Hello,

I am trying to implement PCA for one of the problems.There are two functions in R to do the same:

**princomp and prcomp.**

While princomp expresses the data in terms of the principal components(-scores) prcomp doesn’t I guess,so:

**Q1.**How do I get the data expressed in terms of the PC’s using prcomp?

**Q2.**Which matrix do I use ultimately,as in the scores or the rotated ones?

After rotation the details look like:

So do I interpret this as: The Comp1 has maximum contribution from Total.Day.Minutes and Total.Day.Charge and hence it can be named something like :Day.Comp and then I use the scores matrix with these names for further analysis??

This ques has risen because of my confusion in how to use the rotated components and scores.

**Q3.** What does ‘x’ in prcomp return when retx = F?The code:

```
churn.pca <- prcomp(churn[,5:18],center = T,scale. = T)
churn.pca.x <- data.frame(churn.pca$x)
# With the option retx = T
churn.pca <- prcomp(churn[,5:18],center = T,scale. = T,retx = T)
churn.pca.retx <- data.frame(churn.pca$x)
sum(which(churn.pca.retx != churn.pca.x))
```

x with retx = T returns the rotated variables (is this the same as the scores matrix from princomp after rotation).What does x contain when retx = F as `sum(which(churn.pca.retx != churn.pca.x))`

came out to be 0.

Can somebody please clarify these to me??

Thanks!