douconca (version 1.2.3)

FS.wrda: Forward selection of predictor variables using wrda or cca0

Description

Forward selection of predictor variables using wrda or cca0

Usage

# S3 method for wrda
FS(
  mod,
  ...,
  consider = NULL,
  permutations = 999,
  n_axes = "all",
  initial_model = "1",
  factor2categories = TRUE,
  test = TRUE,
  threshold_P = 0.1,
  PvalAdjustMethod = "holm",
  max_step = 10,
  verbose = FALSE
)

Value

list with three elements: final... with selected variables and model_final and process with account of the selection process If is.numeric(n_axes), then the variance in the returned table is the sum of the n_axes eigenvalues of the current model (all variables so far included).

Arguments

mod

initial wrda or cca0 model with at least on predictor variable,

...

unused.

consider

character vector of names in mod$data to consider for addition.

permutations

a list of control values for the permutations as returned by the function how, or the number of permutations required (default 999), or a permutation matrix where each row gives the permuted indices.

n_axes

number of eigenvalues to select upon. The sum of n_axes eigenvalues is taken as criterion. Default "full" for selection without dimension reduction to n_axes. If n_axes =1, selection is on the first eigenvalue for selection of variables that form an optimal one-dimensional model.

initial_model

character specifying what should be inside Condition(). Default: "1" (nothing, the intercept only). Examples: "region" for a within-region analysis or "A*B" for a within analysis specified by the interaction of factors A and B, with region, A, B in the data.

factor2categories

logical, default TRUE, to convert factors to their categories (set(s) of indicator values). If FALSE, the selection uses, the fit of a factor divided by its number of categories minus 1.

test

logical; default: TRUE.

threshold_P

significance level, after adjustment for testing multiplicity, for addition of a variable to the model.

PvalAdjustMethod

method for correction for multiple testing in p.adjust, default "holm", which is an improved version Bonferroni.

max_step

maximal number of variables selected.

verbose

show progress, default: TRUE.

Details

The selection is on the basis of the additional fit (inertia) of a variable given the variables already in the model.

The names in consider may include transformations of predictor variables, such as log(.), if consider does not include factors or if factor2categories=FALSE. If consider does include factors, such transformations give in a error in the default setting (factor2categories=TRUE).

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 <- dc_CA(formulaEnv = abun ~ Moist + Mag,
             formulaTraits = ~ F + R + N + L,
             dataEnv = dune_trait_env$envir,
             dataTraits = dune_trait_env$traits,
             verbose = FALSE)

# selection of traits with environmental model of mod (~ Moist+Mag)
out1 <- FS(mod, consider = c("F", "R", "N", "L"), 
           select = "traits", verbose = FALSE) 

names(out1)
out1$finalWithOneExtra
out1$model_final

# selection of environmental variables with trait model of mod (~ F + R + N + L)
out2 <- FS(mod, consider =  c("A1", "Moist", "Mag", "Use", "Manure"), 
           select= "env", verbose = FALSE) 

names(out2)
out2$finalWithOneExtra
out2$model_final

# selection of environmental variables without a trait model 
# i.e. with a single constraint
mod3 <- cca0(mod$data$Y ~ Moist, data = mod$data$dataEnv)
out3 <- FS(mod3, consider = c("A1", "Moist", "Mag", "Use", "Manure"), 
           threshold_P = 0.05)

out3$finalWithOneExtra
out3$model_final

# selection of traits without an environmental model 
#                         i.e. with a single constraint
tY <- t(mod$data$Y)

mod4 <- cca0(tY ~ L, data = mod$data$dataTraits)

names(mod$data$dataTraits)
out4 <- FS(mod4, 
           consider =  c("SLA", "Height", "LDMC", "Seedmass", "Lifespan", 
                         "F", "R", "N", "L"))

out4$finalWithOneExtra
out4$model_final