predict.wrda {douconca} | R Documentation |
Prediction from cca0 and wrda models
Description
Prediction of response and lc scores from environment data
using cca0
and wrda
models.
Usage
## S3 method for class 'wrda'
predict(
object,
...,
type = c("response", "lc"),
rank = "full",
newdata = NULL,
weights = NULL,
scaling = "symmetric"
)
Arguments
object |
|
... |
Other arguments passed to the function (currently ignored). |
type |
type of prediction, |
rank |
rank (number of axes to use). Default "full" for all axes (no rank-reduction). |
newdata |
Data in which to look for variables with which to predict. |
weights |
list of weights of species and of sites in |
scaling |
numeric (1,2 or 3) or character |
Details
Variables that are in the model but not in newdata
are set to their
weighted means in the training data. Predictions are thus at the (weighted)
mean of the quantitative variables not included. Predictions with
not-included factors are at the reference level (the first level of the
factor).
In a cca0
model with type = "response"
,
many of the predicted values may be negative,
indicating expected absences (0) or small expected response values.
Value
a matrix with the predictions. The exact content of the matrix
depends on the type
of predictions that are being made.
Examples
data("dune_trait_env")
# rownames are carried forward in results
rownames(dune_trait_env$comm) <- dune_trait_env$comm$Sites
abun <- dune_trait_env$comm[, -1] # must delete "Sites"
mod <- cca0(formula = abun ~ A1 + Moist + Mag + Use + Manure,
data = dune_trait_env$envir)
mod # Proportions equal to those Canoco 5.15
scores(mod, which_cor = c("A1", "X_lot"), display = "cor")
set.seed(123)
anova(mod)
anova(mod, by = "axis")
mod2 <- vegan::cca(abun ~ A1 + Moist + Mag + Use + Manure,
data = dune_trait_env$envir)
anova(mod2, by = "axis")
dat <- dune_trait_env$envir
dat$Mag <- "SF"
predict(mod, type = "lc", newdata = dat)