Function reference • mvabund

Included datasets

Tasmania: Tasmania Dataset

antTraits: Ant data, with species traits

solberg: Solberg Data

spider: Spider data

tikus: Tikus Island Dataset

Functions for modelling and analysis

mvabund-package: Statistical methods for analysing multivariate abundance data

mvabund() as.mvabund() is.mvabund(): Multivariate Abundance Data Objects

mvformula() as.mvformula() is.mvformula(): Model Formulae for Multivariate Abundance Data

manyany() print(<manyany>): Fitting Many Univariate Models to Multivariate Abundance Data

manyglm(): Fitting Generalized Linear Models for Multivariate Abundance Data

manylm(): Fitting Linear Models for Multivariate Abundance Data

manylm.fit() manylm.wfit(): workhose functions for fitting multivariate linear models

anova(<manyany>) print(<anova.manyany>): Analysis of Deviance for Many Univariate Models Fitted to Multivariate Abundance Data

anova(<manyglm>) print(<anova.manyglm>): Analysis of Deviance for Multivariate Generalized Linear Model Fits for Abundance Data

anova(<manylm>) print(<anova.manylm>): ANOVA for Linear Model Fits for Multivariate Abundance Data

anova(<traitglm>): Testing for a environment-by-trait (fourth corner) interaction by analysis of deviance

glm1(): Fits a Generalised Linear Models with a LASSO (or L1) penalty, given a value of the penalty parameter.

glm1path(): Fits a path of Generalised Linear Models with LASSO (or L1) penalties, and finds the model that minimises BIC.

coefplot(<manyglm>): Plots the coefficients of the covariates of a manyglm object with confidence intervals.

plot(<manylm>) plot(<manyglm>): Plot Diagnostics for a manylm or a manyglm Object

predict(<manyglm>): Predict Method for MANYGLM Fits

predict(<traitglm>): Predictions from fourth corner model fits

residuals(<manyglm>): Residuals for MANYGLM, MANYANY, GLM1PATH Fits

summary(<manyglm>) print(<summary.manyglm>): Summarizing Multivariate Generalized Linear Model Fits for Abundance Data

traitglm(): Fits a fourth corner model for abundance as a function of environmental variables and species traits.

ridgeParamEst(): Estimation of the ridge parameter

best.r.sq(): Use R^2 to find the variables that best explain a multivariate response.

formulaUnimva(): Create a List of Univariate Formulas

extend.x.formula(): Extend a Formula to all of it's Terms

cv.glm1path(): Fits a path of Generalised Linear Models with LASSO (or L1) penalties, and finds the best model by corss-validation.

Generic and other helpful functions

anova(<manyany>) print(<anova.manyany>): Analysis of Deviance for Many Univariate Models Fitted to Multivariate Abundance Data

anova(<manyglm>) print(<anova.manyglm>): Analysis of Deviance for Multivariate Generalized Linear Model Fits for Abundance Data

anova(<manylm>) print(<anova.manylm>): ANOVA for Linear Model Fits for Multivariate Abundance Data

manyany() print(<manyany>): Fitting Many Univariate Models to Multivariate Abundance Data

summary(<manyglm>) print(<summary.manyglm>): Summarizing Multivariate Generalized Linear Model Fits for Abundance Data

summary(<manylm>) print(<summary.manylm>): Summarizing Linear Model Fits for Multivariate Abundance Data

predict(<manyglm>): Predict Method for MANYGLM Fits

predict(<manylm>): Model Predictions for Multivariate Linear Models

predict(<traitglm>): Predictions from fourth corner model fits

logLik(<manylm>): Calculate the Log Likelihood

deviance(<manylm>): Model Deviance

mvabund() as.mvabund() is.mvabund(): Multivariate Abundance Data Objects

unabund(): Remove the mvabund Class Attribute

Plotting functions

plot(<manyany>): Plot Diagnostics for a manyany or glm1path Object

plot(<manylm>) plot(<manyglm>): Plot Diagnostics for a manylm or a manyglm Object

plot(<mvabund>) plot(<mvformula>): Plot Multivariate Abundance Data and Formulae

plotMvaFactor(): Draw a Mvabund Object split into groups.

boxplot(<mvabund>) boxplot(<mvformula>): Boxplots for multivariate abundance Data

coefplot(<manyglm>): Plots the coefficients of the covariates of a manyglm object with confidence intervals.

residuals(<manyglm>): Residuals for MANYGLM, MANYANY, GLM1PATH Fits

meanvar.plot(): Construct Mean-Variance plots for Multivariate Abundance Data

shiftpoints(): Calculate a shift for plotting overlapping points