Utility functions to build linear models using Phylogenetic Eigenvector Maps among their explanatory variables.
lmforwardsequentialAICc(y, x, object)lmforwardsequentialsidak(y, x, object, alpha = 0.05)
An lm-class object.
A response variable.
Descriptors (numeric of factor) to be used as
auxiliary traits.
A PEM-class object.
The p-value threshold above which the function will stop adding variables.
lmforwardsequentialAICc(): Forward Stepwise Regression AICc
Forward stepwise variable addition using the sample-size-corrected Akaike Information Criterion.
lmforwardsequentialsidak(): Forward Stepwise Regression Sidak
Forward stepwise variable addition using a Sidak multiple testing corrected alpha error threshold as the stopping criterion.
tools:::Rd_package_author("MPSEM") Maintainer: tools:::Rd_package_maintainer("MPSEM")
Function lmforwardsequentialsidak, performs a forward
stepwise selection of the PEM eigenvectors until the familywise test of
significance of the new variable to be included exceeds the p-value
threshold alpha. The familiwise type I error probability is obtained
using the Holm-Sidak correction of the testwise probabilities, thereby
correcting for type I error rate inflation due to multiple testing.
Function lmforwardsequentialAICc carries out forward stepwise
selection of the eigenvectors as long as the candidate model features a
sample-size-corrected Akaike information criterion lower than the previous
model. The final model should be regarded as overfitted from the
Neyman-Pearson (i.e. frequentist) point of view, but this is the model
that minimizes information loss from the standpoint of information theory.
Burnham, K. P. & Anderson, D. R. 2002. Model selection and multimodel inference: a practical information-theoretic approach, 2nd ed. Springer-Verlag. xxvi + 488 pp.
Holm, S. 1979. A simple sequentially rejective multiple test procedure. Scand. J. Statist. 6: 65-70
Sidak, Z. 1967. Rectangular confidence regions for means of multivariate normal distributions. J. Am. Stat. Ass. 62, 626-633