Fit PCA on the training set and apply the same transformation to the test set. The goal is to use principal components in prediction models as a smaller number of variables instead of all the marker predictors.

apply_pca(split, geno, num_pcs = 100, ...)

Arguments

split

An object of class split, corresponding to one element of the total cv_object generated by one of the functions predict_cv0(), predict_cv00(), predict_cv1(), or predict_cv2(), and containing the following items:

  • training: data.frame Training dataset

  • test: data.frame Test dataset

geno

data.frame It corresponds to a geno element within an object of class METData.

num_pcs

integer Number of principal components to extract.

Value

pc_values A data.frame containing the principal components in columns and the names of all lines used in the study is contained in the first column 'geno_ID'. PCs for the lines present in the test set were computed based on the transformation done on the training set.

Author

Cathy C. Westhues cathy.jubin@uni-goettingen.de