Print a summary of a wrda or cca0 object
Usage
# S3 method for class 'wrda'
print(x, ...)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$envir,
weights = w)
object # Proportions equal to those Canoco 5.15
#>
#> Call: wrda(formula = response ~ A1 + Moist + Mag + Use + Condition(Manure),
#> data = dune_trait_env$envir, weights = w)
#>
#> Inertia Proportion Rank
#> Total 65.7007 1.0000
#> Conditional 7.3839 0.1124 2
#> Constrained 36.4952 0.5555 6
#> Unconstrained 21.8217 0.3321 19
#>
#> Inertia is weighted variance
#>
#> Eigenvalues for constrained axes:
#> wRDA1 wRDA2 wRDA3 wRDA4 wRDA5 wRDA6
#> 18.133 8.233 4.047 3.155 2.238 0.689
#>
#> Eigenvalues for unconstrained axes:
#> wPCA1 wPCA2 wPCA3 wPCA4 wPCA5 wPCA6 wPCA7 wPCA8
#> NA NA NA NA NA NA NA NA
#> (Showing 8 of 19 unconstrained eigenvalues)
#>
#> mean, sd, VIF and canonical coefficients with their optimistic [!] t-values:
#> Avg SDS VIF Regr1 tval1
#> Manure 2.4659 1.2056 10.5656 -0.2010 -0.1344
#> A1 4.1466 1.3786 1.4841 0.1013 0.1807
#> Moist 2.1761 1.4762 1.4883 -4.1077 -7.3195
#> MagBF 0.1932 0.3948 6.8216 0.7873 0.6553
#> MagHF 0.4545 0.4979 5.8054 1.8392 1.6594
#> MagNM 0.0455 0.2083 4.8878 0.1327 0.1304
#> Use 1.9205 0.7107 2.6787 0.2101 0.2791
#>
mod_scores <- scores(object, display = "all")
scores(object, which_cor = c("A1", "X_lot"), display = "cor")
#> wRDA1 wRDA2
#> A1 -0.2365172 -0.3510007
#> X_lot -0.4987793 0.4250562
#> attr(,"meaning")
#> [1] "inter set correlation, correlation between environmental variables and the sites scores (CWMs)"
anova(object)
#> $table
#> Permutation test for weighted reduncancy analysis
#> Model: wrda(formula = response ~ A1 + Moist + Mag + Use + Condition(Manure), data = dune_trait_env$envir, weights = w)
#> Residualized predictor permutation
#> Permutation: free
#> Number of permutations: 999
#>
#> df Variance R2 F Pr(>F)
#> wRDA 6 36.495 0.62581 3.3449 0.009 **
#> Residual 12 21.822
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> $eigenvalues
#> wRDA1 wRDA2 wRDA3 wRDA4 wRDA5 wRDA6
#> 18.1328543 8.2332883 4.0467247 3.1546579 2.2383295 0.6893052
#>