Package: mmodely 0.2.5

mmodely: Modeling Multivariate Origins Determinants - Evolutionary Lineages in Ecology

Perform multivariate modeling of evolved traits, with special attention to understanding the interplay of the multi-factorial determinants of their origins in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>). This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>; Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>) such as model averaging and selection of phylogenetic models. Accessory functions are also implemented for coef standardization (Cade 2015), selection uncertainty, and variable importance (Burnham & Anderson 2000). There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature.

Authors:David M Schruth

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mmodely.pdf |mmodely.html
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NEWS

# Install 'mmodely' in R:
install.packages('mmodely', repos = c('https://dranthropoid.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

37 exports 0.00 score 9 dependencies 4 scripts 247 downloads

Last updated 1 years agofrom:b434e85c5b. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 08 2024
R-4.5-winNOTESep 08 2024
R-4.5-linuxNOTESep 08 2024
R-4.4-winNOTESep 08 2024
R-4.4-macNOTESep 08 2024
R-4.3-winOKSep 08 2024
R-4.3-macOKSep 08 2024

Exports:average.fit.modelscalc.q2n.ratioceptcomp.datacompare.data.gs.vs.tree.tipscorrect.AICcount.mod.varsct.possible.modelsdrop.na.datafit.1ln.rprtget.mod.clmnsget.mod.outcomeget.mod.varsget.model.combosget.pgls.coefsget.phylo.statsgs.checkgs.names.mismatch.checkgs.renameinterpolatemissing.datamissing.fill.inpgls.iterpgls.iter.statspgls.printpgls.reportpgls.wrapplot.confound.gridplot.pgls.itersplot.pgls.R2AICplot.transformed.phyloplot.xy.ab.pselect.best.modelssparge.modseltrim.phylovariable.importanceweight.IC

Dependencies:apecapercarolinedigestlatticeMASSmvtnormnlmeRcpp

Information-theoretic PGLS model selection workflow: a primate vision example

Rendered fromSchruth-mmodely-vignette-Vision.Rnwusingutils::Sweaveon Sep 08 2024.

Last update: 2023-05-17
Started: 2022-11-04

Information-theoretic PGLS model selection workflow: a primate vocal communication example

Rendered fromSchruth-mmodely-vignette-Vocal.Rnwusingutils::Sweaveon Sep 08 2024.

Last update: 2023-05-17
Started: 2022-11-04

Readme and manuals

Help Manual

Help pageTopics
Calculate a weighted average of pglmaverage.fit.models variable.importance
Calculate the ratio of fit predictor variables to sample sizecalc.q2n.ratio
Include all variables except ...cept
Comparative Datacomp.data
Find data being dropped by mismatches to the treecompare.data.gs.vs.tree.tips
Correct AICcorrect.AIC
Count the predictor variables in a modelcount.mod.vars
Count all possible model combinationsct.possible.models
Drop any rows with NA valuesdrop.na.data
Report a model fit in a single line of text outputfit.1ln.rprt
Get model columnsget.mod.clmns
Get the outcome variable from a model stringget.mod.outcome
Get model variable namesget.mod.vars
All combinations of predictor variablesget.model.combos
Get coeficients from a list of PGLS model-fits (from each selected subset)get.pgls.coefs
Get tree statistics for a traitget.phylo.stats
Check "Genus species" name formattinggs.check
Check "Genus species" name formattinggs.names.mismatch.check
Rename the Genus species information in a data framegs.rename
Interpolate missing data in a data frameinterpolate
Report missing values in a dataframemissing.data
Fill in missing values in a dataframe with a secondary sourcemissing.fill.in
Iterate through PGLS estimationspgls.iter
Statistics from PGLS runspgls.iter.stats
Print the results of a PGLS model fitpgls.print
Report PGLS results as a tablepgls.report
A Wrapper for PGLS modelpgls.wrap
Plot a grid of x y plots split by a confounder zplot.confound.grid
Plot the PGLS iterationsplot.pgls.iters
Plot (R2 vs AIC) results of a collection of fit PGLS modelsplot.pgls.R2AIC
Plot a transformed phylogenetic treeplot.transformed.phylo
An x/y scatterplot with a linear regression line and p-valueplot.xy.ab.p
Get the best model from list of PGLS model fitsselect.best.models
Coeficients distribution [sparge] plot of models selected from each subsetsparge.modsel
Trim a phylogenetic tree using Genus species namestrim.phylo
Get IC weightsweight.IC