Principal Components for Stepwise Multiple Linear Regression

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By Darrell Schulze1, Joshua Minai1

Purdue University

Independent predictor variables for stepwise multiple linear regression.

Version 1.0 - published on 18 Nov 2019 doi:10.4231/0FGR-Z715 - cite this Content may change until committed to the archive on 18 Dec 2019

Licensed under CC0 1.0 Universal


To ensure that multivariate covariates are independent of each other, Gobin (2000) used principal components instead of the original environmental covariates as predictors to improve on the prediction for soil-landscape modelling. Therefore, all the original environment covariates for digital soil mapping were subjected to a standardized principal component analysis (PCA) to generate a smaller number of linear combinations that capture most of the variation within the raster stack as a whole (Crawley, 2012). RStudio version 3.5.1 was used to conduct a standardized PCA using the RStoolbox package (Leutner and Horning, 2017). These principal components were uses as the predictors for stepwise multiple linear regression.

Crawley, M.J. (2012). The R book. John Wiley & Sons, pp. 1051.

Gobin, A. (2000). Participatory and spatial-modelling methods for land resources analysis. Doctoral dissertation, Katholieke Universiteit Leuven, pp. 344.

Leutner, B., & Horning, N. (2017). RStoolbox: tools for remote sensing data analysis. R package version 0.1,7.

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