# mvabund v3.13.1

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## Statistical Methods for Analysing Multivariate Abundance Data

A set of tools for displaying, modeling and analysing
multivariate abundance data in community ecology. See
'mvabund-package.Rd' for details of overall package organization.
The package is implemented with the Gnu Scientific Library
(<http://www.gnu.org/software/gsl/>) and 'Rcpp'
(<http://dirk.eddelbuettel.com/code/rcpp.html>) 'R' / 'C++' classes.

## Readme

## Functions in mvabund

Name | Description | |

coefplot.manyglm | Plots the coefficients of the covariates of a manyglm object with confidence intervals. | |

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

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

boxplot.mvabund | Boxplots for multivariate abundance Data | |

Tasmania | Tasmania Dataset | |

anova.manyany | Analysis of Deviance for Many Univariate Models Fitted to Multivariate Abundance Data | |

anova.manyglm | Analysis of Deviance for Multivariate Generalized Linear Model Fits for Abundance Data | |

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 | |

antTraits | Ant data, with species traits | |

deviance.manylm | Model Deviance | |

manylm | Fitting Linear Models for Multivariate Abundance Data | |

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. | |

manylm.fit | workhose functions for fitting multivariate linear models | |

meanvar.plot | Construct Mean-Variance plots for Multivariate Abundance Data | |

mvabund-internal | Internal mvabund Objects | |

formulaUnimva | Create a List of Univariate Formulas | |

logLik.manylm | Calculate the Log Likelihood | |

mvabund-package | Statistical methods for analysing multivariate abundance data | |

mvabund | Multivariate Abundance Data Objects | |

manyany | Fitting Many Univariate Models to Multivariate Abundance Data | |

manyglm | Fitting Generalized Linear Models for Multivariate Abundance Data | |

plotMvaFactor | Draw a Mvabund Object split into groups. | |

predict.manyglm | Predict Method for MANYGLM Fits | |

summary.manylm | Summarizing Linear Model Fits for Multivariate Abundance Data | |

tikus | Tikus Island Dataset | |

extend.x.formula | Extend a Formula to all of it's Terms | |

mvformula | Model Formulae for Multivariate Abundance Data | |

plot.manyany | Plot Diagnostics for a manyany or glm1path Object | |

spider | Spider data | |

predict.manylm | Model Predictions for Multivariate Linear Models | |

predict.traitglm | Predictions from fourth corner model fits | |

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

unabund | Remove the mvabund Class Attribute | |

plot.manylm | Plot Diagnostics for a manylm or a manyglm Object | |

plot.mvabund | Plot Multivariate Abundance Data and Formulae | |

shiftpoints | Calculate a shift for plotting overlapping points | |

residuals.manyglm | Residuals for MANYGLM, MANYANY, GLM1PATH Fits | |

ridgeParamEst | Estimation of the ridge parameter | |

solberg | Solberg Data | |

summary.manyglm | Summarizing Multivariate Generalized Linear Model Fits for Abundance Data | |

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## Last month downloads

## Details

Date | 2018-01-18 |

LinkingTo | Rcpp, RcppGSL |

License | LGPL (>= 2.1) |

NeedsCompilation | yes |

Packaged | 2018-01-19 10:31:46 UTC; David Warton |

RoxygenNote | 6.0.1 |

Repository | CRAN |

Date/Publication | 2018-01-19 13:09:00 UTC |

imports | MASS , methods , parallel , Rcpp , statmod , stats , tweedie |

depends | R (>= 3.0.0) |

linkingto | RcppGSL |

Contributors | Dirk Eddelbuettel, Yi Wang, Ulrike Naumann, Stephen Wright, Ian Renner, Jenni Niku, Julian Byrnes, Ralph dos Santos Silva |

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