I recommend use of R-package
RXshrink. Principal components regression is an (extreme) special case of "generalized" ridge regression. The statistics and graphics produced by
RXshrink functions help researchers select the best (normal-theory maximum likelihood) choices for both the "
Q"-shape (curvature) of the shrinkage path and also the best extent,
M, of shrinkage along that path. Q = -5 is essentially principal components regression. With
P=4 predictors, 0 <= M <= 4 is the approximate rank-deficiency in your predictor data. "Good" choices for Q and M enable shrinkage to correct wrong-signs problems and reduce MSE risk in estimation of the true Beta coefficient vector. My advice is to be conservative (and get better predictions) by doing somewhat less shrinkage than what appears optimal for estimating Betas.
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Bob Obenchain
Principal Consultant
Risk Benefit Statistics LLC
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Original Message:
Sent: 02-19-2019 14:15
From: Ikenna Nnabue
Subject: principal component regression analysis
Dear all, please i am soliciting for clarification on principal component regression aanlysis(PCRA). I have four climatic parameters( rainfall, relative humidity, sunshine and temperature) and yearly cassava production in Benue state Nigeria. Cassava data is my dependent variable while the climate data are my independent variables. Firstly, i want to run a PCA to know among the climate data which one(s) contribute most to the total variation observed. I am using R programming language. Right now i have done the PCA but dont know how to proceed to PCRA. Pease your input will be highly appreciated. Thanks.
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Ikenna Nnabue
Research Officer
National Root Crops Research Institute.
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