I am having a biparental mapping population of rice with dataset of various morphological and post harvest parameters from F6 and F7 and 6 checks in augmented block design.
#For PCA
library(caret)
library(e1071)
pca = preProcess(x = training_set [ - output], method = pca, pcaComp =2)
training_set = predict( pca,training_set)
training_set = training_set[]
test_set = predict( pca,test_set)
test_set = test_set[]
Use logistic regression classifier
#For PCA
library(caret)
library(e1071)
pca = preProcess(x = training_set [ - output], method = pca, pcaComp =2)
training_set = predict( pca,training_set)
training_set = training_set[]
test_set = predict( pca,test_set)
test_set = test_set[]
Use logistic regression classifier
R-codes for principal component analysis can be found here:
https://www.datacamp.com/community/tutorials/pca-analysis-r
or here:
http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/118-principal-component-analysis-in-r-prcomp-vs-princomp/
- Badges
- Science topic
- Similar topics
- Geoscience
- Cartography
- Mapping