Error with ordistep(dbrda) vegan package #670

ParideBalzani · 2024-07-09T16:15:23Z

I have an issue when I do a variable selection with a dbrda object.

Here my code:

b<-vegdist(CWM_tot.2[,1:16], method="bray")

dibbierredia.b<-dbrda(b~ ALTITUDE + SLOPE + MICRORELIEF +
                        LITTER + VEGETATION + ROCKS + HEIGHT + DEPTH +
                        pH + C_N + Northerness + Easterness, data= env_tot.3, 
                      strata=site, center=T, scale=T)

ord.null.b<-dbrda(b~1, data=env_tot.3)

ord.sel.b<-ordistep(ord.null.b, scope=formula(dibbierredia.b), direction="forward", permutations = 9999)

And here the Error:

Error in dimnames(u) <- list(dnam[[1]], c(axnam, negnam)) : 
  length of 'dimnames' [2] not equal to array extent

> traceback()
10: ordConstrain(Y, X, Z)
9: ordConstrained(X, d$Y, d$Z, method = "dbrda")
8: dbrda(formula = b ~ Condition(pH + HEIGHT + C_N + Northerness + 
       Easterness + LITTER) + ROCKS, data = env_tot.3)
7: eval(call, parent.frame())
6: eval(call, parent.frame())
5: update.default(object, fla)
4: update(object, fla)
3: add1.cca(object, scope = scope$add, test = "perm", permutations = permutations, 
       alpha = Pin, trace = trace, ...)
2: add1(object, scope = scope$add, test = "perm", permutations = permutations, 
       alpha = Pin, trace = trace, ...)
1: ordistep(ord.null.b, scope = formula(dibbierredia.b), direction = "forward", 
       permutations = 9999)

Here also the structure of the objects:

> str(b)
 'dist' num [1:3003] 0.146 0.128 0.179 0.164 0.173 ...
 - attr(*, "maxdist")= num 1
 - attr(*, "Size")= int 78
 - attr(*, "Labels")= chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
 - attr(*, "Diag")= logi FALSE
 - attr(*, "Upper")= logi FALSE
 - attr(*, "method")= chr "bray"
 - attr(*, "call")= language vegdist(x = CWM_tot.2[, 1:16], method = "bray")

> str(dibbierredia.b)
List of 13
 $ tot.chi  : num 2.53
 $ Ybar     : num [1:78, 1:78] 0.0483 0.0245 0.0243 0.0213 0.0157 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  ..- attr(*, "METHOD")= chr "DISTBASED"
 $ method   : chr "dbrda"
 $ call     : language dbrda(formula = b ~ ALTITUDE + SLOPE + MICRORELIEF + LITTER + VEGETATION + ROCKS + HEIGHT +      DEPTH + pH + C_N| __truncated__ ...
 $ pCCA     : NULL
 $ CCA      :List of 13
  ..$ eig        : Named num [1:12] 0.9401 0.2356 0.1018 0.0318 0.0218 ...
  .. ..- attr(*, "names")= chr [1:12] "dbRDA1" "dbRDA2" "dbRDA3" "dbRDA4" ...
  ..$ poseig     : int 8
  ..$ u          : num [1:78, 1:8] -0.1416 -0.2072 -0.1469 -0.0752 -0.0489 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  .. .. ..$ : chr [1:8] "dbRDA1" "dbRDA2" "dbRDA3" "dbRDA4" ...
  ..$ v          : logi[0 , 1:8] 
  ..$ wa         : num [1:78, 1:8] -0.2242 -0.1131 -0.1135 -0.0969 -0.0703 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  .. .. ..$ : chr [1:8] "dbRDA1" "dbRDA2" "dbRDA3" "dbRDA4" ...
  ..$ alias      : NULL
  ..$ biplot     : num [1:12, 1:8] 0.354 -0.433 -0.229 0.504 0.361 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. .. ..$ : chr [1:8] "dbRDA1" "dbRDA2" "dbRDA3" "dbRDA4" ...
  ..$ rank       : int 12
  ..$ qrank      : int 12
  ..$ tot.chi    : num 1.29
  ..$ QR         :List of 4
  .. ..$ qr   : num [1:78, 1:12] -8.775 0.0754 0.0736 0.094 0.0677 ...
  .. .. ..- attr(*, "assign")= int [1:12] 1 2 3 4 5 6 7 8 9 10 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  .. .. .. ..$ : chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. .. ..- attr(*, "scaled:center")= Named num [1:12] 1.18e-16 -1.18e-16 -5.21e-17 2.36e-16 1.78e-16 ...
  .. .. .. ..- attr(*, "names")= chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. ..$ rank : int 12
  .. ..$ qraux: num [1:12] 1.07 1.02 1.11 1.07 1.14 ...
  .. ..$ pivot: int [1:12] 1 2 3 4 5 6 7 8 9 10 ...
  .. ..- attr(*, "class")= chr "qr"
  ..$ envcentre  : Named num [1:12] 1.18e-16 -1.18e-16 -5.21e-17 2.36e-16 1.78e-16 ...
  .. ..- attr(*, "names")= chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  ..$ imaginary.u: num [1:78, 1:4] -0.279 0.0245 -0.0442 -0.0686 0.1095 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  .. .. ..$ : chr [1:4] "idbRDA1" "idbRDA2" "idbRDA3" "idbRDA4"
 $ CA       :List of 7
  ..$ eig        : Named num [1:65] 0.46 0.312 0.258 0.175 0.102 ...
  .. ..- attr(*, "names")= chr [1:65] "MDS1" "MDS2" "MDS3" "MDS4" ...
  ..$ poseig     : int 28
  ..$ u          : num [1:78, 1:28] 0.1273 -0.1221 -0.014 -0.027 0.0489 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  .. .. ..$ : chr [1:28] "MDS1" "MDS2" "MDS3" "MDS4" ...
  ..$ v          : logi[0 , 1:28] 
  ..$ rank       : int 65
  ..$ tot.chi    : num 1.24
  ..$ imaginary.u: num [1:78, 1:37] -0.1202 0.0789 0.0485 0.0823 -0.0661 ...
  .. ..- attr(*, "dimnames")=List of 2
  .. .. ..$ : chr [1:78] "PC_1_02" "PC_1_03" "PC_1_04" "PC_1_05" ...
  .. .. ..$ : chr [1:37] "iMDS1" "iMDS2" "iMDS3" "iMDS4" ...
 $ colsum   : logi NA
 $ terms    :Classes 'terms', 'formula'  language b ~ ALTITUDE + SLOPE + MICRORELIEF + LITTER + VEGETATION + ROCKS + HEIGHT + DEPTH + pH +      C_N + Northerness + Easterness
  .. ..- attr(*, "variables")= language list(b, ALTITUDE, SLOPE, MICRORELIEF, LITTER, VEGETATION, ROCKS, HEIGHT, DEPTH, pH, C_N,      Northerness, Easterness)
  .. ..- attr(*, "factors")= int [1:13, 1:12] 0 1 0 0 0 0 0 0 0 0 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:13] "b" "ALTITUDE" "SLOPE" "MICRORELIEF" ...
  .. .. .. ..$ : chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. ..- attr(*, "term.labels")= chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. ..- attr(*, "specials")=Dotted pair list of 1
  .. .. ..$ Condition: NULL
  .. ..- attr(*, "order")= int [1:12] 1 1 1 1 1 1 1 1 1 1 ...
  .. ..- attr(*, "intercept")= int 1
  .. ..- attr(*, "response")= int 1
  .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
 $ terminfo :List of 4
  ..$ terms  :Classes 'terms', 'formula'  language ~ALTITUDE + SLOPE + MICRORELIEF + LITTER + VEGETATION + ROCKS + HEIGHT + DEPTH + pH + C_N +      Northerness + Easterness
  .. .. ..- attr(*, "variables")= language list(ALTITUDE, SLOPE, MICRORELIEF, LITTER, VEGETATION, ROCKS, HEIGHT, DEPTH, pH, C_N, Northerness,      Easterness)
  .. .. ..- attr(*, "factors")= int [1:12, 1:12] 1 0 0 0 0 0 0 0 0 0 ...
  .. .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. .. ..$ : chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. .. .. .. ..$ : chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. .. ..- attr(*, "term.labels")= chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
  .. .. ..- attr(*, "order")= int [1:12] 1 1 1 1 1 1 1 1 1 1 ...
  .. .. ..- attr(*, "intercept")= int 1
  .. .. ..- attr(*, "response")= num 0
  .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv> 
  ..$ assign : int [1:12] 1 2 3 4 5 6 7 8 9 10 ...
  ..$ xlev   : Named list()
  ..$ ordered: Named logi [1:12] FALSE FALSE FALSE FALSE FALSE FALSE ...
  .. ..- attr(*, "names")= chr [1:12] "ALTITUDE" "SLOPE" "MICRORELIEF" "LITTER" ...
 $ sqrt.dist: logi FALSE
 $ adjust   : num 1
 $ inertia  : chr "squared Bray distance"
 - attr(*, "class")= chr [1:3] "dbrda" "rda" "cca"

There is no correlation among predictors, and they are scaled.

If I remove one variable it works, but I have previously successfully run the same code with another distance matrix (same predictors and code) and it worked perfectly, so I don't understand why this one does not work.

thank you in advance for any help!
Paride

jarioksa · 2024-07-09T17:13:40Z

Unfortunately I cannot reproduce this. Can you prepare a reproducible example?

What is your vegan version?

ParideBalzani · 2024-07-27T09:53:34Z

Here is a reproducible example:

library(reprex)
#> Warning: package 'reprex' was built under R version 4.3.3
library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.6-4
library(FD)
#> Loading required package: ade4
#> Loading required package: ape
#> Loading required package: geometry

ant_tot<- data.frame(matrix(NA, nrow = 78, ncol = 25))
rownames(ant_tot)<- paste0("plot", seq_len(78))
colnames(ant_tot)<- paste0("sp", seq_len(25))

ant_tot$sp1<- c(0,0,0,0,0,1,0,0,0,0,2,1,3,0,0,0,0,1,1,0,2,1,1,1,3,3,4,1,2,0,4,1,1,2,4,4,4,3,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,3,0,0,0,0,2,0,1,0,0,0)
ant_tot$sp2<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,2,0,2,4,0,3,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,4,0,0,4,4,0,0,0,0,0,4,3,3,4,0,4,4,0,1,2)
ant_tot$sp3<- c(1,4,3,4,3,4,0,3,4,4,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,4,0,1,0,0,0,0,0,4,0,3,2,3,0,4,0,3,0,2,4,4,0,0,2,4,0,3,2,1,2,3,1,3,1,1,2,3,0,3,0)
ant_tot$sp4<-c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp5<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,4,4,4,4,4,4,4,4,4,2,0,3,4,2,4,4,4,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp6<- c(0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,2,0,0,0,2,1)
ant_tot$sp7<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp8<- c(0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp9<- c(4,4,4,3,3,4,4,4,4,4,4,1,4,3,4,2,4,4,3,2,3,1,4,4,3,4,2,3,3,4,3,0,4,4,4,4,4,3,0,0,0,0,0,1,0,0,0,0,0,1,0,1,4,1,4,0,0,0,0,0,0,4,4,0,0,0,0,3,0,0,0,0,1,1,0,0,1,0)
ant_tot$sp10<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,2,1,0,0,0,0,1,1,1,2,1,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp11<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp12<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,4,2,3,1,1,0,3,1,3,3,3,2,0,1,2,4,4,0,2,4,3,3,1,1,4,3,0,2,0,1,2,0,1,2,3,0,1,0)
ant_tot$sp13<- c(0,0,0,0,1,0,0,0,0,0,1,0,0,1,3,0,0,0,0,0,0,4,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp14<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp15<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,1,0,0,0,0,0,0,0)
ant_tot$sp16<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,4,1,0,0,0,1,0,0,1,3,0,1,3,0,0,2,2,3,1)
ant_tot$sp17<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,0,1,0,0,0,0,3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp18<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp19<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,1,1,0,0,3,3,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,1,0,0,0,0,0,0,0)
ant_tot$sp20<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp21<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,0,0,0,0,3,0,0,0,0,0,0,0,0,0,3,0,0,2,2,1,4,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp22<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp23<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp24<- c(0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,2,0,0,0,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0)
ant_tot$sp25<- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,2,1,2,0,4,1,1,2,3,4,3,4,4,1,2,4,3,3,2,2,0,0,4,3,0,1,2,0,2,0,0,1,0,0,1,0,0,0,0)



tratti_tot <- data.frame(matrix(NA, nrow = 25, ncol = 8))
rownames(tratti_tot)<- colnames(ant_tot)
colnames(tratti_tot)<- paste0("tr", seq_len(8))

tratti_tot$tr1<- c(2.228,2.068,2.193,1.716,1.679,0.991,1.284,1.303,1.076,0.805,1.026,1.471,1.58,1.185,1.554,1.581,1.742,1.366,1.609,1.09,1.045,1.051,0.873,1.079,0.905)
tratti_tot$tr2<- c(1.995,1.989,1.9,1.616,1.505,0.791,1.152,1.113,0.982,0.481,0.746,1.152,1.213,0.696,1.287,1.272,1.34,1.246,1.283,0.857,0.862,0.917,0.607,0.766,0.682)
tratti_tot$tr3<- c(1.462,1.437,1.477,1.245,1.19,0.828,1.01,1.002,0.871,0.662,0.912,1.097,1.166,0.949,1.132,1.187,1.323,1.133,1.189,0.9,0.778,0.836,0.712,0.836,0.777)
tratti_tot$tr4<- c("Generalist","Sugar","Generalist","Generalist","Generalist","Sugar","Sugar","Sugar","Sugar","Generalist","Generalist","Generalist","Generalist","Generalist","Generalist","Generalist","Generalist","Generalist","Generalist","Generalist","Sugar","Sugar","Generalist","Generalist","Generalist")
tratti_tot$tr5<- c(0,1,0,1,1,0,1,1,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,1)
tratti_tot$tr6<- c("Monogyny","Facultative","Polygyny","Facultative","Facultative","Facultative","Polygyny","Monogyny","Monogyny","Polygyny","Facultative","Monogyny","Polygyny","Polygyny","Facultative","Polygyny","Facultative","Polygyny","Monogyny","Monogyny","Polygyny","Polygyny","Monogyny","Monogyny","Monogyny")
tratti_tot$tr7<- c("Facultative","Polydomy","Polydomy","Polydomy","Facultative","Monodomy","Polydomy","Monodomy","Polydomy","Monodomy","Monodomy","Monodomy","Monodomy","Polydomy","Monodomy","Monodomy","Monodomy","Monodomy","Facultative","Monodomy","Polydomy","Polydomy","Monodomy","Polydomy","Monodomy")
tratti_tot$tr8<- c("Independent","Dependent","Independent","Independent","Dependent","Independent","Dependent","Independent","Independent","Dependent","Dependent","Facultative","Facultative","Facultative","Facultative","Facultative","Facultative","Facultative","Facultative","Independent","Dependent","Dependent","Independent","Independent","Independent")


tratti_tot$tr4<-as.factor(tratti_tot$tr4)
tratti_tot$tr5<-as.factor(tratti_tot$tr5)
tratti_tot$tr6<-as.factor(tratti_tot$tr6)
tratti_tot$tr7<-as.factor(tratti_tot$tr7)
tratti_tot$tr8<-as.factor(tratti_tot$tr8)

data_ant<-as.matrix(ant_tot)

CWM_tot <- functcomp(tratti_tot, data_ant, CWM.type = "all")

CWM_tot.2 <- CWM_tot
CWM_tot.2$fascia<-c("1", "1", "1", "1", "1", "1", "1", "1", "1","1", 
                    "2", "2", "2", "2", "2", "2","2", "2", "2", 
                    "3", "3", "3", "3", "3", "3", "3", "3", "3", 
                    "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
                    "1", "1", "1", "1", "1", "1", "1", "1", "1","1", 
                    "2", "2", "2", "2", "2", "2","2", "2", "2", "2", 
                    "3", "3", "3", "3", "3", "3", "3", "3", "3", "3", 
                    "4", "4", "4", "4", "4", "4", "4", "4", "4", "4")
CWM_tot.2$fascia<- as.factor(CWM_tot.2$fascia)

CWM_tot.2$site <- c(rep("site1", 38),  rep("site2", 40))
CWM_tot.2$site<- as.factor(CWM_tot.2$site)


env_tot<- data.frame(matrix(NA, nrow = 78, ncol = 12))
rownames(env_tot)<- rownames(ant_tot)
colnames(env_tot)<- c("ALTITUDE", "SLOPE", "MICRORELIEF", "LITTER", "VEGETATION", 
                      "ROCKS", "HEIGHT", "DEPTH", "pH", "C_N", "Northerness", "Easterness")

env_tot$ALTITUDE<- c(1640,1661,1657,1702,1644,1742,1717,1746,1677,1721,1453,1426,1480,1399,1440,1399,1408,1396,1481,1340,1264,1267,1300,1312,1287,1326,1233,1334,1188,1035,1207,1185,1124,1057,1149,1102,1150,1169,1859,1907,1984,1862,1953,1890,1924,1922,1921,1872,1678,1678,1733,1774,1723,1729,1713,1784,1727,1761,1425,1399,1417,1407,1402,1391,1408,1417,1419,1582,1239,1251,1276,1322,1347,1326,1333,1361,1339,1353)
env_tot$SLOPE <- c(43,30,30,40,35,30,32,30,34,47,44,40,30,40,35,30,30,30,20,20,20,30,40,20,20,35,30,25,25,10,30,40,40,24,20,20,15,28,28,10,19,28,20,32,40,20,20,30,10,18,28,15,30,30,37,20,30,42,20,8,30,15,20,0,15,30,10,20,15,20,30,40,7,20,15,10,10,20)
env_tot$MICRORELIEF <- c(11,13,21,20,31,20,17,25,20,29,14,9,8,15,13,19,15,8,12,12,18,26,24,20,15,23,19,15,16,11,30,40,25,12,12,22,23,18,9,7,20,9,10,17,14,17.5,5,9,2,8,5,4,18,9,35,14,18,3,25,15,17,12,15,18,9,21,17,15,5,8,13,17,8,16,13,15,12,14)
env_tot$LITTER <- c(96,40,60,80,45,75,75,40,75,55,95,99,95,50,98,75,45,95,85,70,1,90,90,5,50,85,90,3,80,97,90,70,50,70,70,85,80,75,97,40,85,75,90,45,90,65,50,50,98,95,95,75,70,95,65,90,70,100,100,100,100,85,40,100,100,100,100,65,100,96,100,100,98,100,100,100,100,95)
env_tot$VEGETATION <- c(90,90,80,75,90,70,67,75,67,50,90,95,93,100,96,100,95,90,80,65,30,90,90,65,95,80,90,60,95,90,90,95,65,75,95,95,100,85,95,85,95,70,95,60,80,80,98,75,80,75,75,90,75,85,85,85,90,90,95,100,95,75,70,80,85,95,95,100,100,100,90,85,90,95,100,98,90,95)
env_tot$ROCKS <- c(25,15,40,30,10,50,30,30,40,70,5,4,7,10,5,0,2,3,30,40,20,2,5,10,5,8,18,60,0,6,0,0,45,35,15,1,0,1,15,1,5,0,0,75,0,0,0,0,0,1,0,5,45,45,5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0)
env_tot$HEIGHT <- c(39.4,15.2,24.2,21.6,25.4,24.2,32.6,19.8,11.8,24,30.4,32.8,27.6,39.4,31.4,35.6,30.6,29.4,28.2,20.2,26.6,34.4,26.8,25,28.6,17.8,16.8,33.4,26.4,40.4,32.2,30.2,27.8,48.2,40.2,30.6,35.6,30.2,19.2,16.2,16.8,16.2,12.8,20,23.2,15,12.3,14.2,32.6,41.8,21.6,15.4,28.2,32.4,23.2,15.6,37.2,34.6,58.2,92.6,56.2,56.6,37.8,53.2,40.8,50.6,49.8,26.8,45.4,54.6,46.2,46.4,42.8,50,49.4,48.2,46,42.6)
env_tot$DEPTH <- c(19.8,8,9.4,3,15.8,5.6,11.2,4.8,10,7.8,15,23.2,24.8,40.4,7.4,21.4,17,17.2,8.4,6.4,15.2,22,18.2,17,37.2,26.4,5.4,6.8,26.4,11.2,19.4,27,3.2,14.4,4.6,12.2,22.4,20.2,19,16.8,26.4,36.6,30,11.6,28,34.2,22.8,34.8,26.6,34,34,13.8,5.2,11.6,40.4,27.4,22,28.4,27.4,33.4,39.9,17.4,18.4,33.6,38.2,48.4,28.6,33.4,46,47,35.2,22.2,41.6,21.2,53.6,38.4,37,57.2)
env_tot$pH <- c(7.09,6.91,6.51,5.82,5.16,5.13,6.84,5.53,6.58,6.8,6.56,6.75,5.53,5.69,5.68,5.06,6.7,7.35,7.34,7.31,7.71,5.52,5.71,7.73,5.45,6.82,7.54,6.98,7.01,7.6,7.04,5.55,7.35,6.95,7.09,6.82,7.05,6.92,4.6,4.21,4.43,4.08,4.26,4.63,4.45,4.02,3.79,3.76,4.08,4.28,4.05,4.14,5.33,6.01,4.95,4.15,3.88,4.57,4.51,4.47,4.65,5.53,4.94,4.64,4.41,4.49,4.55,4.44,4.92,5.33,6.09,5.07,4.39,4.04,4.52,4.61,4.15,4.52)
env_tot$C_N <- c(9.9317647127757,9.07590623486224,7.51433132471259,9.1979283096896,9.97149694838346,11.661431647422,9.8479669328786,10.2559908190182,9.80655042066058,10.0333732982956,10.0476300660018,10.5900118749961,8.38873237078218,10.5731314202627,12.4618106235287,12.9127609597884,7.66553088572237,13.6409072323435,11.3326485535448,12.8496807732952,9.68427368651668,13.203335610773,11.8142434122683,6.75824349786527,11.7062377027595,3.83784554883534,10.7878941313131,11.1575366845919,10.2787502327992,12.3409002800254,11.0707995525736,12.4837031508761,11.4859245219589,5.9286643535668,11.5939705062939,12.1468754480496,10.9453282058355,10.5608091738822,12.7977588899141,13.5625356938892,9.50542596124335,13.2603809539321,15.2199602320537,9.47123306008305,13.4961257665475,14.6220704174749,14.1634085982676,14.2153613338162,9.9491369044148,11.5440174851785,15.4755576598194,13.0056205588768,14.0872120948374,14.2517839565214,12.8274067319351,12.7885828210575,12.0039060192172,11.662936396241,10.5400666433773,11.8572940295238,13.6727757705085,10.1607012859428,10.1452956496494,9.79661482429789,12.7252001747315,11.9510012537688,11.3845530285972,13.0031938933471,12.8548941477065,10.2661184022007,12.0318724085839,10.4480035965479,11.0863477034558,12.438707761121,11.1587693369191,10.5426283644628,10.2519128101179,11.431073347874)
env_tot$Northerness <- c(0.406736643075801,0.17364817766693,-1.83690953073357e-16,-0.642787609686539,-1.83690953073357e-16,0.5,-0.342020143325669,-0.642787609686539,0.309016994374947,-0.642787609686539,-0.544639035015027,-0.629320391049837,0.342020143325668,-0.642787609686539,-0.17364817766693,1,-0.809016994374948,0.848048096156425,0.342020143325668,0.156434465040231,-0.766044443118978,0.5,0.17364817766693,-0.642787609686539,0.342020143325668,0.17364817766693,0.342020143325668,-0.939692620785908,-0.642787609686539,-0.913545457642601,-0.939692620785908,-0.939692620785908,-0.939692620785908,-1,-0.939692620785908,-1,-0.342020143325669,-0.994521895368273,0.17364817766693,-0.939692620785908,-0.939692620785908,-0.719339800338651,-0.766044443118978,-0.939692620785908,-0.984807753012208,-0.984807753012208,-0.939692620785908,-0.90630778703665,-0.766044443118978,-0.0697564737441256,-0.139173100960065,0.17364817766693,-0.939692620785908,0.17364817766693,-0.939692620785908,-0.939692620785908,-0.984807753012208,-0.275637355816999,-0.190808995376545,-0.342020143325669,-0.17364817766693,-0.819152044288992,-0.939692620785908,0.996194698091746,-0.342020143325669,-0.17364817766693,-1.83690953073357e-16,-0.866025403784439,0.0871557427476579,0.0871557427476579,-0.0871557427476582,-0.17364817766693,-0.766044443118978,-0.544639035015027,0.17364817766693,-0.17364817766693,-0.939692620785908,-0.121869343405147)
env_tot$Easterness <- c(-0.913545457642601,-0.984807753012208,-1,-0.766044443118978,-1,-0.866025403784439,-0.939692620785908,-0.766044443118978,-0.951056516295154,-0.766044443118978,-0.838670567945424,-0.777145961456971,-0.939692620785909,-0.766044443118978,-0.984807753012208,0,-0.587785252292473,-0.529919264233206,-0.939692620785909,-0.987688340595138,-0.642787609686539,-0.866025403784439,-0.984807753012208,-0.766044443118978,-0.939692620785909,-0.984807753012208,-0.939692620785909,-0.342020143325669,0.766044443118978,-0.4067366430758,-0.342020143325669,-0.342020143325669,-0.342020143325669,1.22460635382238e-16,-0.342020143325669,1.22460635382238e-16,0.939692620785908,0.104528463267654,-0.984807753012208,-0.342020143325669,-0.342020143325669,0.694658370458997,-0.642787609686539,0.342020143325669,-0.17364817766693,-0.17364817766693,-0.342020143325669,-0.422618261740699,-0.642787609686539,-0.997564050259824,-0.99026806874157,-0.984807753012208,-0.342020143325669,-0.984807753012208,0.342020143325669,-0.342020143325669,-0.17364817766693,-0.961261695938319,-0.981627183447664,-0.939692620785908,-0.984807753012208,-0.573576436351046,-0.342020143325669,-0.0871557427476583,-0.939692620785908,-0.984807753012208,-1,-0.5,-0.996194698091746,-0.996194698091746,-0.996194698091746,-0.984807753012208,-0.642787609686539,-0.838670567945424,-0.984807753012208,-0.984807753012208,-0.342020143325669,-0.992546151641322)

env_tot$site <- c(rep("site1", 38),  rep("site2", 40))
env_tot$site<- as.factor(env_tot$site)


b<-vegdist(CWM_tot.2[,1:16], method="bray")

env_tot[,1:12]<- scale(env_tot[,1:12], center=T, scale=T)


dibbierredia.b<-dbrda(b~ ALTITUDE + SLOPE + MICRORELIEF +
                        LITTER + VEGETATION + ROCKS + HEIGHT + DEPTH +
                        pH + C_N + Northerness + Easterness, data= env_tot, 
                      strata=site, center=T, scale=T)

ord.null.b<-dbrda(b~1, data=env_tot)

ord.sel.b<-ordistep(ord.null.b, scope=formula(dibbierredia.b), direction="forward", permutations = 9999) 
#> 
#> Start: b ~ 1 
#> 
#>               Df    AIC       F Pr(>F)    
#> + pH           1 46.035 34.7625 0.0001 ***
#> + DEPTH        1 61.748 14.5545 0.0001 ***
#> + C_N          1 63.849 12.1472 0.0001 ***
#> + LITTER       1 66.647  9.0416 0.0001 ***
#> + ROCKS        1 67.990  7.5897 0.0002 ***
#> + SLOPE        1 69.566  5.9174 0.0023 ** 
#> + ALTITUDE     1 70.224  5.2297 0.0041 ** 
#> + HEIGHT       1 70.749  4.6847 0.0058 ** 
#> + VEGETATION   1 70.961  4.4656 0.0087 ** 
#> + Northerness  1 72.499  2.8948 0.0389 *  
#> + Easterness   1 72.596  2.7962 0.0418 *  
#> + MICRORELIEF  1 73.331  2.0574 0.1051    
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Step: b ~ pH 
#> 
#>               Df    AIC      F Pr(>F)   
#> + HEIGHT       1 43.035 4.9656 0.0013 **
#> + Northerness  1 43.641 4.3471 0.0015 **
#> + Easterness   1 43.607 4.3815 0.0029 **
#> + LITTER       1 43.782 4.2030 0.0032 **
#> + ALTITUDE     1 43.931 4.0523 0.0032 **
#> + C_N          1 44.165 3.8153 0.0067 **
#> + DEPTH        1 45.981 2.0018 0.0972 . 
#> + SLOPE        1 46.521 1.4707 0.2176   
#> + VEGETATION   1 46.670 1.3246 0.2638   
#> + MICRORELIEF  1 47.197 0.8103 0.5207   
#> + ROCKS        1 47.498 0.5189 0.7232   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Step: b ~ pH + HEIGHT 
#> 
#>               Df    AIC      F Pr(>F)   
#> + Easterness   1 41.169 3.7599 0.0067 **
#> + C_N          1 41.324 3.6055 0.0075 **
#> + Northerness  1 41.335 3.5951 0.0090 **
#> + LITTER       1 42.498 2.4462 0.0551 . 
#> + ALTITUDE     1 43.234 1.7290 0.1465   
#> + SLOPE        1 43.506 1.4648 0.2183   
#> + DEPTH        1 43.971 1.0161 0.4003   
#> + MICRORELIEF  1 44.056 0.9343 0.4506   
#> + VEGETATION   1 44.437 0.5692 0.6867   
#> + ROCKS        1 44.548 0.4633 0.7608   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Step: b ~ pH + HEIGHT + Easterness 
#> 
#>               Df    AIC      F Pr(>F)  
#> + C_N          1 39.748 3.2731 0.0151 *
#> + LITTER       1 40.073 2.9557 0.0219 *
#> + Northerness  1 40.053 2.9753 0.0266 *
#> + ALTITUDE     1 40.960 2.0973 0.0850 .
#> + MICRORELIEF  1 41.108 1.9546 0.1080  
#> + DEPTH        1 42.122 0.9868 0.4131  
#> + SLOPE        1 42.127 0.9816 0.4229  
#> + VEGETATION   1 42.535 0.5958 0.6695  
#> + ROCKS        1 42.951 0.2042 0.9155  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Step: b ~ pH + HEIGHT + Easterness + C_N 
#> 
#>               Df    AIC      F Pr(>F)  
#> + Northerness  1 38.376 3.1809 0.0165 *
#> + LITTER       1 39.025 2.5576 0.0431 *
#> + ALTITUDE     1 39.400 2.2005 0.0736 .
#> + MICRORELIEF  1 39.962 1.6678 0.1684  
#> + DEPTH        1 40.654 1.0174 0.3918  
#> + SLOPE        1 40.657 1.0144 0.4074  
#> + VEGETATION   1 41.090 0.6099 0.6552  
#> + ROCKS        1 41.575 0.1596 0.9307  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Step: b ~ pH + HEIGHT + Easterness + C_N + Northerness 
#> 
#>               Df    AIC      F Pr(>F)  
#> + LITTER       1 37.482 2.6836 0.0329 *
#> + ALTITUDE     1 37.861 2.3267 0.0632 .
#> + MICRORELIEF  1 38.467 1.7593 0.1429  
#> + SLOPE        1 39.345 0.9445 0.4414  
#> + DEPTH        1 39.409 0.8861 0.4856  
#> + VEGETATION   1 39.566 0.7414 0.5724  
#> + ROCKS        1 40.234 0.1298 0.9416  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Step: b ~ pH + HEIGHT + Easterness + C_N + Northerness + LITTER
#> Error in dimnames(u) <- list(dnam[[1]], c(axnam, negnam)): length of 'dimnames' [2] not equal to array extent

jarioksa · 2024-07-27T11:02:45Z

Confirmed.

To make things easier for other to study, this is the model that causes the error:

dbrda(b ~ Condition(pH + HEIGHT + Easterness + C_N + Northerness + LITTER) + ROCKS, env_tot)

This model is fit internally in ordistep when trying to evaluate should ROCKS be added to the last accepted model within Condition().

This is a strange geometric quirk that needs a bit thinking: the conditional (partial) model with ROCKS has no numerically positive eigenvalues: the largest ("leading") eigenvalue is 1.2 × 10^-17 that we regard as zero within numerical precision. However, there is one numerically non-zero negative eigenvalue of −3 × 10^-3. If you look at the permutation tests of the model that would include ROCKS, you see this peculiarity:

Permutation test for dbrda under reduced model
Terms added sequentially (first to last)
Permutation: free
Number of permutations: 999

Model: dbrda(formula = b ~ pH + HEIGHT + Easterness + C_N + Northerness + LITTER + ROCKS, data = env_tot)
            Df SumOfSqs       F Pr(>F)    
pH           1  0.79424 40.5281  0.001 ***
HEIGHT       1  0.10783  5.5021  0.001 ***
Easterness   1  0.07875  4.0183  0.012 *  
C_N          1  0.06651  3.3938  0.023 *  
Northerness  1  0.06276  3.2025  0.033 *  
LITTER       1  0.05174  2.6401  0.051 .  
ROCKS        1 -0.00297 -0.1516  0.998    
Residual    70  1.37180

ROCKS only has imaginary contribution!

Your case is so enchanting that I was carried along with details. The main point is that we should handle this kind of situation smoothly and I'll fix this. Really an exciting case!

(cherry picked from commit 2a3bf84)

ParideBalzani changed the title ~~Error with ordistep(dbrda)~~ Error with ordistep(dbrda) vegan package Jul 9, 2024

jarioksa closed this as completed in 2a3bf84 Jul 27, 2024

jarioksa added a commit that referenced this issue Jul 27, 2024

Merge branch 'issue-#670'

c21b118

jarioksa added a commit that referenced this issue Jul 27, 2024

dbrda component may only have negative eigenvalues: fixes #670

cb425a2

(cherry picked from commit 2a3bf84)

jarioksa added a commit that referenced this issue Aug 1, 2024

NEWS: dbrda fix #670; dbrda(decorana()) & strwidth/height bugs

f1d2ecd

jarioksa added a commit that referenced this issue Aug 1, 2024

NEWS: mainly plot.cca, ordipointlabel & dbrda issue #670

e35b938

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Error with ordistep(dbrda) vegan package #670

Error with ordistep(dbrda) vegan package #670

ParideBalzani commented Jul 9, 2024 •

edited by jarioksa

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jarioksa commented Jul 9, 2024

ParideBalzani commented Jul 27, 2024 •

edited by gavinsimpson

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jarioksa commented Jul 27, 2024

Error with ordistep(dbrda) vegan package #670

Error with ordistep(dbrda) vegan package #670

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ParideBalzani commented Jul 9, 2024 • edited by jarioksa Loading

jarioksa commented Jul 9, 2024

ParideBalzani commented Jul 27, 2024 • edited by gavinsimpson Loading

jarioksa commented Jul 27, 2024

ParideBalzani commented Jul 9, 2024 •

edited by jarioksa

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ParideBalzani commented Jul 27, 2024 •

edited by gavinsimpson

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