douconca (version 1.2.3)

wrda: Performs a weighted redundancy analysis

Description

wrda is formula-based implementation of weighted redundancy analysis.

Usage

wrda(
  formula,
  response = NULL,
  data,
  weights = rep(1/nrow(data), nrow(data)),
  traceonly = FALSE,
  cca_object = NULL,
  object4QR = NULL
)

Value

All scores in the wrda object are in scaling "sites" (1): the scaling with Focus on Case distances.

Arguments

formula

one or two-sided formula for the rows (samples) with row predictors in data. The left hand side of the formula is ignored if it is specified in the next argument (response). Specify row covariates (if any ) by adding + Condition(covariate-formula) to formula as in rda. The covariate-formula should not contain a ~ (tilde).

response

matrix or data frame of the abundance data (dimension n x m). Rownames of response, if any, are carried through. Can be NULL if cca_object is supplied or if the response is formula is two-sided.

data

matrix or data frame of the row predictors, with rows corresponding to those in response (dimension n x p).

weights

row weights (a vector). If not specified unit weights are used.

traceonly

logical, default FALSE. If TRUE, only the explained variance of the predictors and the Condition() are returned, i.e without performing a singular value decompostion.

cca_object

a vegan-type cca-object of transposed response, from which centred abundance values and row and column weights can be obtained.

object4QR

a vegan-type cca-object with weighted QR's for formula, i.e. qr(Z) and qr(XZ) obtainable via get_QR(object4QR, model = "pCCA") and get_QR(object4QR, model = "CCA"), respectively.

Details

The algorithm is a modified version of published R-code for weighted redundancy analysis (ter Braak, 2022).

Compared to rda, wrda does not have residual axes, i.e. no SVD or PCA of the residuals is performed.

References

ter Braak C.J.F. and P. Šmilauer (2018). Canoco reference manual and user's guide: software for ordination (version 5.1x). Microcomputer Power, Ithaca, USA, 536 pp.

Oksanen, J., et al. (2022) vegan: Community Ecology Package. R package version 2.6-4. https://CRAN.R-project.org/package=vegan.

See Also

scores.wrda, anova.wrda, print.wrda

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 = response ~ A1 + Moist + Mag + Use + Condition(Manure),
               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)