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mmodely (version 0.2.5)

Modeling Multivariate Origins Determinants - Evolutionary Lineages in Ecology

Description

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 ). 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 ; Garamszegi, 2011 ) 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features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature.

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Version

Install

install.packages('mmodely')

Monthly Downloads

203

Version

0.2.5

License

Apache License

Maintainer

David Schruth

Last Published

May 17th, 2023

Functions in mmodely (0.2.5)

average.fit.models

Calculate a weighted average of pglm
ct.possible.models

Count all possible model combinations
get.pgls.coefs

Get coeficients from a list of PGLS model-fits (from each selected subset)
get.mod.clmns

Get model columns
cept

Include all variables except ...
get.mod.outcome

Get the outcome variable from a model string
gs.rename

Rename the Genus species information in a data frame
calc.q2n.ratio

Calculate the ratio of fit predictor variables to sample size
comp.data

Comparative Data
gs.check

Check "Genus species" name formatting
interpolate

Interpolate missing data in a data frame
drop.na.data

Drop any rows with NA values
get.phylo.stats

Get tree statistics for a trait
pgls.print

Print the results of a PGLS model fit
pgls.wrap

A Wrapper for PGLS model
gs.names.mismatch.check

Check "Genus species" name formatting
fit.1ln.rprt

Report a model fit in a single line of text output
pgls.report

Report PGLS results as a table
plot.confound.grid

Plot a grid of x y plots split by a confounder z
trim.phylo

Trim a phylogenetic tree using Genus species names
pgls.iter

Iterate through PGLS estimations
weight.IC

Get IC weights
plot.pgls.R2AIC

Plot (R2 vs AIC) results of a collection of fit PGLS models
get.mod.vars

Get model variable names
select.best.models

Get the best model from list of PGLS model fits
sparge.modsel

Coeficients distribution [sparge] plot of models selected from each subset
pgls.iter.stats

Statistics from PGLS runs
plot.pgls.iters

Plot the PGLS iterations
get.model.combos

All combinations of predictor variables
missing.data

Report missing values in a dataframe
missing.fill.in

Fill in missing values in a dataframe with a secondary source
plot.transformed.phylo

Plot a transformed phylogenetic tree
plot.xy.ab.p

An x/y scatterplot with a linear regression line and p-value
compare.data.gs.vs.tree.tips

Find data being dropped by mismatches to the tree
correct.AIC

Correct AIC
count.mod.vars

Count the predictor variables in a model