best.r.sq {mvabund} | R Documentation |
Finds the subset of explanatory variables in a formula that best explain the variation in a multivariate response, as measured by a chosen definition of R^2. Modifications are included for high dimensional data, such as multivariate abundance data in ecology.
best.r.sq(formula, data = parent.frame(), subset, var.subset, n.xvars= min(3, length(xn)), R2="h", ...)
formula |
a mvformula, a multivariate formula. |
data |
optional, the data.frame (or list) from which the variables in formula should be taken. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
var.subset |
an optional vector specifying the subset of the responses to be used. |
n.xvars |
the number of independent variables with the highest average R^2 that should be found. |
R2 |
the type of R^2 (correlation coefficient) that should be shown, possible values are: "h" = Hooper's R^2 = tr(SST^(-1)SSR))/p "v" = vector R^2 = det(SSR)/det(SST) "n" = none Note that for a univariate response, all of these are equivalent to the ordinary product-moment correlation coefficient. |
... |
further arguments that are passed on to lm. |
best.r.sq
finds the n.xvars
influence variables obtained
by a forward selection in a multivariate linear model given by formula
.
Only the response variables given by var.subset
are considered. However, if
var.subset
is NULL
all response variables are considered.
Interactions are excluded from the search mechanism, however the indices that are
returned correspond to the indices in the model.
This function should not be used for model selection,
but only in plots.
A vector giving the indices of the independent variables with the greatest explanatory power.
Ulrike Naumann and David Warton <David.Warton@unsw.edu.au>.
data(spider) spiddat <- mvabund(spider$abund) X <- spider$x best.r.sq( spiddat~X )