scores.wrda {douconca} | R Documentation |
Extract results of a weighted redundancy analysis (wrda)
Description
This function works very much like the vegan
scores
function, in particular
scores.cca
, but with regression coefficients for
predictors.
Usage
## S3 method for class 'wrda'
scores(
x,
...,
choices = 1:2,
display = "all",
scaling = "sym",
which_cor = "in model",
tidy = FALSE
)
Arguments
x |
object of class |
... |
Other arguments passed to the function (currently ignored). |
choices |
integer vector of which axes to obtain. Default: all wrda axes. |
display |
a character vector, one or more of |
scaling |
numeric (1,2 or 3) or character |
which_cor |
character vector environmental variables names in the data frames for which inter-set correlations must calculated. Default: a character ("in_model") for all predictors in the model, including collinear variables and levels. |
tidy |
Return scores that are compatible with |
Details
The function is modeled after scores.cca
.
An example of which_cor is: which_cor = c("acidity", "humidity")
Value
A data frame if tidy = TRUE
. Otherwise, a matrix if a single
item is asked for and a named list of matrices if more than one item is
asked for. The following names can be included: c("sites",
"constraints_sites", "centroids", "regression", "t_values", "correlation",
"intra_set_correlation", "biplot", "species")
. Each matrix has an
attribute "meaning"
explaining its meaning. With tidy = TRUE
,
the resulting data frame has attributes "scaling"
and
"meaning"
; the latter has two columns: (1) name of score type and (2)
its meaning, usage and interpretation.
An example of the meaning of scores in scaling "symmetric"
with
display = "all"
:
- sites
CMWs of the trait axes (constraints species) in scaling 'symmetric' optimal for biplots and, almost so, for inter-site distances.
- constraints_sites
linear combination of the environmental predictors and the covariates (making the ordination axes orthogonal to the covariates) in scaling 'symmetric' optimal for biplots and, almost so, for inter-site distances.
- regression
mean, sd, VIF, standardized regression coefficients and their optimistic t-ratio in scaling 'symmetric'.
- t_values
t-values of the coefficients of the regression of the CWMs of the trait composite on to the environmental variables
- correlation
inter set correlation, correlation between environmental variables and the sites scores (CWMs)
- intra_set_correlation
intra set correlation, correlation between environmental variables and the dc-ca axis (constrained sites scores)
- biplot
biplot scores of environmental variables for display with biplot-traits for fourth-corner correlations in scaling 'symmetric'.
- centroids
environmental category means of the site scores in scaling 'symmetric' optimal for biplots and, almost so, for inter-environmental category distances.
- species
SNC on the environmental axes (constraints sites) in scaling 'symmetric' optimal for biplots and, almost so, for inter-species distances.
The statements on optimality for distance interpretations are based on the
scaling
and the relative magnitude of the dc-CA eigenvalues of the
chosen axes.
Examples
data("dune_trait_env")
# rownames are carried forward in results
rownames(dune_trait_env$comm) <- dune_trait_env$comm$Sites
response <- dune_trait_env$comm[, -1] # must delete "Sites"
w <- rep(1, 20)
w[1:10] <- 8
w[17:20] <- 0.5
object <- wrda(formula = ~ A1 + Moist + Mag + Use + Condition(Manure),
response = response,
data = dune_trait_env$envir,
weights = w)
object # Proportions equal to those Canoco 5.15
mod_scores <- scores(object, display = "all")
scores(object, which_cor = c("A1", "X_lot"), display = "cor")
anova(object)