| manyany {mvabund} | R Documentation |
Fitting Many Univariate Models to Multivariate Abundance Data
Description
manyany is used to fit many univariate models (GLMs, GAMs, otherwise) to high-dimensional data, such as multivariate abundance data in ecology. This is the base model-fitting function - see plot.manyany for assumption checking, and anova.manyany for significance testing.
Usage
manyany(formula, fn, family="negative.binomial", data, composition = FALSE,
block = NULL, get.what="details", var.power=NA, na.action = "na.exclude", ...)
## S3 method for class 'manyany'
print(x, digits = max(3L, getOption("digits") - 3L), ...)
Arguments
formula |
an object of class |
fn |
a character string giving the name of the function for the univariate model to be applied. e.g. "glm". |
family |
a description of the error distribution function to be used
in the model, either as a character string, a |
data |
an optional data frame containing predictor variables (a matrix is also acceptable). |
composition |
logical. FALSE (default) fits a separate model to each species. TRUE fits a single model to all variables, including site as a row effect, such that all other terms model relative abundance (compositional effects). |
block |
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). |
get.what |
what to return from each model fit: "details" (default) includes predicted values and residuals in output, "models" also returns the fitted objects for each model, "none" returns just the log-likelihood (mostly for internal use). |
var.power |
the power parameter, if using the tweedie distribution. |
na.action |
Default set to |
... |
further arguments passed to the fitting function. |
x |
an object of class "manyany", usually, a result of a call to
|
digits |
how many digits to include in the printed anova table. |
Details
manyany can be used to fit the model type specified in fn to many variables
simultaneously, a generalisation of manyglm to handle other model types.
It should be able to handle any fixed effects modelling function that has
predict and logLik functions, and that accepts a family argument,
provided that the family is on our list (currently 'gaussian', 'poisson', 'binomial',
'negative.binomial' and 'tweedie', although models for ordinal data are also accepted
if using the clm function from the ordinal package). Models for manyany
are specified symbolically, see for example the details section of lm
and formula.
Unlike manyglm, this function accepts family functions as arguments
instead of just character strings, giving greater flexibility. For example, you could
use family=binomial(link="probit") to fit a model using the probit link, rather
than being restricted to the default logit link or cloglog links available in manyglm.
A data argument is required, and it must be a dataframe.
Setting composition=TRUE enables compositional analyses, where predictors are
used to model relative abundance rather than mean abundance. This is achieved by
vectorising the response matrix and fitting a single model across all variables, with
a row effect to account for differences in relative abundance across rows.
The default composition=FALSE just fits a separate model for each variable.
Value
manyany returns an object inheriting from "manyany".
The function anova (i.e. anova.manyany) will produce a significance test comparing two manyany objects.
Currently there is no summary resampling function for objects of this class.
The generic accessor functions fitted.values, residuals, logLik, AIC, plot
can be used to extract various useful features of the value returned by manyany.
An object of class "manyany" is a list containing at least the
following components:
logL |
a vector of log-likelihood terms for each response variable in the fitted model. |
fitted.values |
the matrix of fitted mean values, obtained by transforming the linear predictors by the inverse of the link function. |
residuals |
the matrix of probability integral transform (PIT) residuals. If the fitted model is a good fit, these will be approximately standard uniformly distributed. |
linear.predictor |
the linear fit on link scale. But for ordinal models fitted using |
family |
a vector of |
call |
the matched call. |
model |
the |
terms |
a list of |
Author(s)
David Warton <David.Warton@unsw.edu.au>.
References
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.
See Also
anova.manyany, residuals.manyany, plot.manyany.
Examples
data(spider)
spider = list(abund=spider$abund, x = as.matrix(spider$x))
## To fit a log-linear model assuming counts are negative binomial, via manyglm:
spidNB <- manyany(abund~x,"manyglm",data=spider,family="negative.binomial")
logLik(spidNB) # a number of generic functions are applible to manyany objects
## To fit a glm with complementary log-log link to presence/absence data:
PAdat = pmin(as.matrix(spider$abund),1) #constructing presence/absence dataset
spidPA <- manyany(PAdat~x,"glm",data=spider,family=binomial("cloglog"))
plot(spidPA)
# There are some wild values in there for the Pardmont variable (residuals >5 or <-8).
#The Pardmont model didn't converge, coefficients are a bit crazy:
coef(spidPA)
# could try again using the glm2 package to fit the models, this fixes things up:
## Not run: library(glm2)
## Not run: spidPA2<-manyany(PAdat~x,"glm",data=spider,family=binomial("cloglog"),method="glm.fit2")
## Not run: plot(spidPA2) #looks much better.
## To simultaneously fit models to ordinal data using the ordinal package:
## Not run: library(ordinal)
## First construct an ordinal dataset:
## Not run: spidOrd = spider$abund
## Not run: spidOrd[spider$abund>1 & spider$abund<=10]=2
## Not run: spidOrd[spider$abund>10]=3
## Now fit a model using the clm function:
## Not run: manyOrd=manyany(spidOrd~bare.sand+fallen.leaves,"clm",data=data.frame(spider$x))
## Not run: plot(manyOrd)