Linear modelling utility functions
Utility functions to build linear models using Phylogenetic Eigenvector Maps as their features.
lmforwardsequentialsidak(y, x, object, alpha=0.05) lmforwardsequentialAICc(y, x, object)
y |
a response variable |
x |
descriptors to be used as auxiliary traits |
object |
a |
alpha |
the threshold above which to stop adding variables |
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
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. lmforwardsequentialAICc carries out
forward stepwise selection of the eigenvectors as long as the
candidate model features a lower sample-size-corrected Akaike
information criterion than the previous model. The final model should
be regarded as overfit from the Neyman-Pearson (i.e.
frequentist) point of view, but it is the model that minimizes
information loss from the standpoint of information theory.
Both functions return a lm class object.
Guillaume Guénard, Département de sciences biologiques Université de Montréal, Montréal, QC, Canada.
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.
## No example has yet been produced.
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