anova.manyany: Analysis of Deviance for Many Univariate Models Fitted to...

Description Usage Arguments Details Value Warning Author(s) References See Also Examples

View source: R/anova.manyany.R


Compute an analysis of deviance table for many univariate model fits. Slowly!


## S3 method for class 'manyany'
anova(object, ..., nBoot=99, p.uni="none", block=object1$block, nCores=1,
       bootID=NULL, replace=TRUE) 

## S3 method for class 'anova.manyany'
print(x, ...) 



of class manyany under the null hypothesis, typically the result of a call to manyany.


other generic anova methods. NEEDS TO INCLUDE A SECOND manyany object for the alternative hypothesis to be tested.


the number of Bootstrap iterations, default is nBoot=99.


whether to calculate univariate test statistics and their P-values.
"none" = No univariate P-values (default)
"unadjusted" = A test statistic and (ordinary unadjusted) P-value are reported for each response variable. If the manyany object is compositional (composition=TRUE), this option is unavailable as yet.


a factor specifying the sampling level to be resampled. Default is resampling rows (if composition=TRUE in the manyany command, this means resampling rows of data as originally sent to manyany).


Number of cores to use for computations (for parallel computing).


A user-entered matrix of indices for which observations to use in which resample. Bootstrap resamples in rows, observations in columns. When specified, overwrites nBoot and block. Default is NULL.


whether to sample with or without replacement, as in the sample function. = FALSE for PIT-permutation, = TRUE for PIT-trap.


anova.manyany object to be printed.


The anova.manyany function returns a table summarising the statistical significance of a fitted manyany model under the alternative hypothesis (object2) as compared to a fit under the null hypothesis (object). Typically the alternative model is nested in the null although it doesn't need to be (but consider seriously if what you are doing makes sense if they are not nested).

This function is quite computationally intensive, and a little fussy - it is an early version we hope to improve on. Feedback welcome!

This function behaves a lot like anova.manyglm, the most conspicuous differences being in flexibility and computation time. Since this function is based on manyany, it offers much greater flexibility in terms of types of models that can be fitted (most fixed effects model with predict and family arguments could be accommodated). For information on the different types of data that can be modelled using manyany, see manyany.

However this flexibility comes at considerable cost in terms of computation time, and the default nBoot has been set to 99 to reflect this (although rerunning at 999 is recommended). Other more cosmetic differences from anova.manyglm are that two and only two models can be supplied as input here; adjusted univariate P-values are not yet implemented; and the range of test statistics and resampling algorithms is more limited. All test statistics constructed here are sum-of-likelihood ratio statistics as in Warton et al (2012), and the resampling method used here is the PIT-trap (short for 'probability integral transform residual bootstrap', Warton et al 2017).

To check model assumptions, use plot.manyany.

The block argument allows for block resampling, such that valid inferences can be made across independent blocks of correlated sets of observations. For example, if data have multiple rows of records for each site, e.g. multi-species data with entries for different species on different rows, you can use your site ID variable as the block argument to resample sites, for valid cross-site inferences despite within-site species correlation. Well, valid assuming sites are independent. You could do similarly for a repeated measures design to make inferences robust to temporal autocorrelation. Note that block needs to be balanced, e.g. equal number of species entries for each site (i.e. include rows for zero abundances too).

The anova.manyany function is designed specifically for high-dimensional data (that, is when the number of variables p is not small compared to the number of observations N). In such instances a correlation matrix is computationally intensive to estimate and is numerically unstable, so by default the test statistic is calculated assuming independence of variables. Note however that the resampling scheme used ensures that the P-values are approximately correct even when the independence assumption is not satisfied.

Rather than stopping after testing for multivariate effects, it is often of interest to find out which response variables express significant effects. Univariate statistics are required to answer this question, and these are reported if requested. Setting p.uni="unadjusted" returns resampling-based univariate P-values for all effects as well as the multivariate P-values, if composition=FALSE. There are currently no univariate P-value options when composition=TRUE (it's not entirely clear how such P-values should be obtained) and if univariate P's are of interest why not rerun the model with composition=FALSE.

A current limitation of the function is that composition needs to be set to the same value in each manyany object being compared - it is not currently possible to compare models with and without a compositional term in them.



the observed value of the test statistic.


the P-value as estimated from nBoot resamples.


the values of the test statistic in each of the nBoot resamples.


the P-value in each of the nBoot resamples.


the p.uni argument supplied.

If p.uni="unadjusted" the output list also contains


a table showing the test statistics of the univariate tests.


a table showing the p-values of the univariate tests.


a matrix of values of the univariate test statistics in each of the nBoot resamples.


The comparison between two or more models by anova.manyglm will only be valid if they are fitted to the same dataset. This may be a problem if there are missing values and R's default of na.action = na.omit is used.


David Warton <[email protected]>.


Warton D. I., Wright S., and Wang, Y. (2012). Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3(1), 89-101.

Warton D. I., Thibaut L., Wang Y. A. (2017). The PIT-trap - A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses. PLoS One, 12(7), e0181790.

See Also

manyany, anova.manyglm.


## Try fitting Tikus Islands data with Tweedie models with power parameter 1.5,
## to test for compositional effect:
coral <- as.matrix(tikus$abund[1:20,])
sumSpp = apply(coral>0,2,sum)

coral <- coral[,sumSpp>6] ## cutting to just species with seven(!) or more presences to cut
## computation time.  Maybe rerun with less (e.g. 4 or more presences) if curious and patient.
coralX <- tikus$x[1:20,]


ftTimeRep <- manyany("glm", coral, coral ~ time+rep, data=coralX, 
family=tweedie(var.power=1.5, link.power=0), var.power=1.5, composition=TRUE)

ftRep <- manyany("glm",coral, coral ~ rep, data=coralX, 
family=tweedie(var.power=1.5, link.power=0), var.power=1.5, composition=TRUE)
anova(ftRep,ftTimeRep,nBoot=9) #this takes a few seconds to run even for just 9 resamples
## This should be rerun for nBoot=999, which would take maybe five minutes...

mvabund documentation built on Jan. 20, 2018, 9:19 a.m.