scores.wrda {douconca} | R Documentation |
This function works very much like the vegan
scores
function, in particular
scores.cca
, but with regression coefficients for
predictors.
## S3 method for class 'wrda'
scores(
x,
...,
choices = 1:2,
display = "all",
scaling = "sym",
which_cor = "in model",
normed = TRUE,
tidy = FALSE
)
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. |
normed |
logical (default |
tidy |
Return scores that are compatible with |
The function is modeled after scores.cca
.
An example of which_cor is: which_cor = c("acidity", "humidity")
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"
:
CMWs of the trait axes (constraints species) in scaling 'symmetric' optimal for biplots and, almost so, for inter-site distances.
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.
mean, sd, VIF, standardized regression coefficients and their optimistic t-ratio in scaling 'symmetric'.
t-values of the coefficients of the regression of the CWMs of the trait composite on to the environmental variables
inter set correlation, correlation between environmental variables and the sites scores (CWMs)
intra set correlation, correlation between environmental variables and the dc-ca axis (constrained sites scores)
biplot scores of environmental variables for display with biplot-traits for fourth-corner correlations in scaling 'symmetric'.
environmental category means of the site scores in scaling 'symmetric' optimal for biplots and, almost so, for inter-environmental category distances.
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.
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)