Hi StackExchange Community, I am performing a Principal Components Analyses (PCA). I would like to know how to extrapolate some PCA components with other variables that were not considered in the PCA function.
I have a nutritional survey with 60 questions that was applied to 420 people. The frequency of consumption was measured in servings and It is standardized for each type of food. I have a clearly Components identified using the following criteria:
a. Selected components by eigen-value >1.5
b. Varimax rotation loadings >0.2 for variable .
The Results of PCA+varimax rotation:
PC1: Orange, Apple, Watermelon
PC2: Homemade fries, Mayonesa, Pizza
PC3: Eggs, Walnuts, Hazelnuts
PC4: Witefish , fatty fish small, fatty fish big
Then, I want to know if it is possible to carry out post-PCA statistical analysis with the standardized scores of the Varimax rotation of each subject in the component and cross-check that information with other confounding variables such as sex, age, education level, etc.
This table illustrates that I want to compute: https://ijbnpa.biomedcentral.com/articles/10.1186/s12966-016-0353-2/tables/4
Other studies where similar approach was applied:
Can I recover the position of the subjects in the components? I tried to do something using info of this link but I'm not sure if it's correct. I think that with this step I could compute an ANAVOA test or Chi-Square to confounding variables such as sex, education, diet calories etc
How to compute varimax-rotated principal components in R?
#Code for RStudio library(factoextra) #PCA prc <- prcomp(df, center=TRUE, scale=TRUE) prc$sdev^2 # Choose components with the eigenvalues >1.5 #Varimax and loadings varimax_df = varimax ( prc$rotation [, 1:4] ) varimax_df$loadings varimax_df$rotmat #Scaling component to row. Standarized scores for each row newData <- scale(df) %*% varimax_df$loadings