mvabund (version 3.1)

mvabund-package: Statistical methods for analysing multivariate abundance data

Description

This package provides tools for a model-based approach to the analysis of multivariate abundance data in ecology (Warton 2011), where 'abundance' should be interpreted loosely - as well as counts you could have presence/absence, ordinal or biomass (via manyany), etc. There are graphical methods for exploring the properties of data and the community-environment association, flexible regression methods for estimating and making robust inferences about the community-environment association, 'fourth corner models' to explain environmental response as a function of traits, and diagnostic plots to check the appropriateness of a fitted model (Wang et. al 2012). There is an emphasis on design-based inferences about these models, e.g. bootstrapping rows of residuals via anova calls, or cross-validation across rows, to make multivariate inferences that are robust to failure of assumptions about correlation. Another emphasis is on presenting diagnostic tools to check assumptions, especially via residual plotting.

Details

The key functions available in this package are the following. For graphical display of the data: [object Object],[object Object],[object Object] For estimating and displaying Linear Models: [object Object],[object Object],[object Object],[object Object] For estimating and displaying Generalized Linear Models: [object Object],[object Object],[object Object],[object Object] Other generic functions like residuals, predict, AIC can be applied to manyglm objects. For estimating and displaying 'fourth corner models' with species traits as well as environmental predictors: [object Object],[object Object] Other generic functions like plot, residuals, predict, AIC can be applied to traitglm objects. Note traitglm can work slowly, as it fits a single big model to vectorised data (then wants to resample it when you call anova.traitglm). For fitting more flexible models: [object Object],[object Object],[object Object],[object Object] Other generic functions like residuals, predict, AIC can be applied to manyany and glm1path objects. These functions also can be on the slow side, especially if all rare species are included. For providing a data structure: [object Object],[object Object] Example datasets: [object Object],[object Object],[object Object],[object Object],[object Object] For more details, see the documentation for any of the individual functions listed above.

References

Brown AM, Warton DI, Andrew NR, Binns M, Cassis G and Gibb H (2014) The fourth corner solution - using species traits to better understand how species traits interact with their environment, Methods in Ecology and Evolution 5, 344-352. Warton D.I. (2008a). Raw data graphing: an informative but under-utilized tool for the analysis of multivariate abundances. Austral Ecology 33, 290-300. Warton D.I. (2008b). Penalized normal likelihood and ridge regularization of correlation and covariance matrices. Journal of the American Statistical Association 103, 340-349. Warton D.I. (2011). Regularized sandwich estimators for analysis of high dimensional data using generalized estimating equations. Biometrics, 67, 116-123. Warton DI, Shipley B & Hastie T (2015) CATS regression - a model-based approach to studying trait-based community assembly, Methods in Ecology and Evolution 6, 389-398. Warton D. I., Wright S., and Wang, Y. (2012). Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3, 89-101. Wang Y., Neuman U., Wright S. and Warton D. I. (2012). mvabund: an R package for model-based analysis of multivariate abundance data. Methods in Ecology and Evolution, 3, 471-473.

See Also

plot.mvabund, meanvar.plot, manyany, manylm, manyglm, traitglm, summary.manylm, anova.manyany, anova.manylm, anova.traitglm, anova.manyglm, plot.manylm

Examples

require(graphics)

## Load the spider dataset:
data(spider)

## Create the mvabund object spiddat:
spiddat <- mvabund(spider$abund)
X <- spider$x

## Draw a plot of the spider data:
plot(spiddat, col="gray1", n.vars=8, transformation="sqrt", 
xlab=c("Hunting Spider"), ylab="Spider Species", scale.lab="s",
t.lab="t", shift=TRUE, fg= "lightblue", col.main="red", main="Spiders") 


## A mean-variance plot, data organised by year, 
## for 1981 and 1983 only, as in Figure 7a of Warton (2008a):
data(tikus)
tikusdat <- mvabund(tikus$abund)
year <- tikus$x[,1]
is81or83 <- year==81 | year==83
meanvar.plot(tikusdat~year,legend=TRUE, subset=is81or83, col=c(1,10)) 	

## Create a formula for multivariate abundance data:
foo <- mvformula( spiddat~X )

## Create a List of Univariate Formulas:
fooUni <- formulaUnimva(spiddat~X)
fooUniInt <- formulaUnimva(spiddat~X, intercept=TRUE)

## Find the three variables that best explain the response:
best.r.sq( foo, n.xvars= 3)

## Fit a multivariate linear model:
foo <- mvformula( spiddat~X )
lm.spider <- manylm(foo)

## Plot Diagnostics for a multivariate linear model:
plot(lm.spider,which=1:2,col.main="red",cex=3,overlay=FALSE)

## Obtain a summary of test statistics using residual resampling:
summary(lm.spider, nBoot=500)

## Calculate a ANOVA Table:
anova(lm.spider, nBoot=500)