# Analysis of community ecology data in R

David Zelený

### Others

You are not allowed to perform this action
en:forward_sel_examples

Section: Ordination analysis

## Variable selection (constrained ordination)

### Example 1: tb-RDA with forward selection: Which chemical variables measured in fen water are important for vegetation of Carpathian wetlands?

We will use data about species composition of vascular plants in Carpathian wetlands, and ask which chemical variables, measured in fen water running through these wetlands, are the most important to explain the compositional changes.

First, prepare the data:

# Carpathian wetlands - import data

# transform data using Hellinger transformation to prepare them for tb-RDA:
library (vegan)
vasc.hell <- decostand (vasc, 'hell')

# the last variable in the 'chem' dataset is 'slope', which is not a chemical variable - remove it:
chem1 <- chem[,-15]

Then, test the global model (including all explanatory variables from which you plan to do selection) to see whether it is significant; if it is not, the forward selection should not be done.

tb_rda.all <- rda (vasc.hell ~ ., chem1)
anova (tb_rda.all)  # 0.001 *** - it is significant
adjR2.tbrda <- RsquareAdj (tb_rda.all)$adj.r.squared # 0.2011048 - adjusted R2 explained by all 14 variables is 20.1% #### Option a) Use of forward.sel function The function forward.sel from the package adespatial is elaborated forward selection approach based on linear constrained ordination (i.e. based on RDA - if you want to calculate CCA, you cannot use this function and need to resolve to use ordiR2step from vegan instead). Along to the testing significance of selected variable, this function includes also other stopping rules, published by Blanchet et al. (2008). The method based on significance is prone to be inflated, which may result into models with too many variables; Blanchet et al. (2008) came with another stopping rules along to the significance one. First, adjusted R2 of the model including all variables (from which the selection is made) is calculated. While selecting variables during the selection process, the selection stops if (1) the next selected variables is not significant (does not significantly improve the explained variation), and (2) if the R2adj of the model including this variable exceeds the R2adj of the global model. # install.packages ('adespatial') # run this code if you haven't install the adespatial package before library (adespatial) sel.fs <- forward.sel (Y = vasc.hell, X = chem1, adjR2thresh = adjR2.tbrda) # NOTE: forward.sel calculates only RDA (or tb-RDA in case of Hell. transformed data) Testing variable 1 Testing variable 2 Testing variable 3 Testing variable 4 Testing variable 5 Testing variable 6 Testing variable 7 Testing variable 8 Procedure stopped (alpha criteria): pvalue for variable 8 is 0.118000 (> 0.050000) See the table with selected variables saved in sel.fs object: sel.fs  variables order R2 R2Cum AdjR2Cum F pval 1 Ca 1 0.13898260 0.1389826 0.1263206 10.976337 0.001 2 conduct 14 0.03248342 0.1714660 0.1467337 2.626796 0.001 3 Si 6 0.02722659 0.1986926 0.1622696 2.242529 0.001 4 NH3 10 0.02398868 0.2226813 0.1748463 2.005953 0.002 5 NO3 9 0.02112004 0.2438013 0.1847233 1.787470 0.001 6 Mg 2 0.01914995 0.2629513 0.1927562 1.636862 0.003 7 pH 13 0.01744989 0.2804012 0.1991562 1.503467 0.024 Or, if you want to see only the names of selected variables, subset it from the sel.fs object: sel.fs$variables
[1] "Ca"      "conduct" "Si"      "NH3"     "NO3"     "Mg"      "pH"

Since there is (a potentially high) number of tests of significance during the forward selection procedure, it is better to apply a correction for multiple testing issue. The most common correction (but rather conservative) is Bonferroni, which multiplies each calculated P-value by the overall number of tests. In the forward selection, the overall number of tests (which can be potentially done) equals to the number of variables from which the selection is made (i.e. not the number of selected variables), and this should be considered while applying the adjustment.

The function available for adjusting the P-values is p.adjust. It can be applied on a single P-value or a vector of P-values, and optionally accepts also the argument n indicating the number of tests.

The P-values from the forward selection above are in the object sel.fs$pval, and the adjustement can be done in the following way (Holm's correction): n.tests <- ncol (chem1) # number of tests equals to number of all variables from which is being selected pval.adj <- p.adjust (sel.fs$pval, method = 'holm', n = n.tests)
sel.fs$pval.adj <- pval.adj sel.fs  variables order R2 R2Cum AdjR2Cum F pval pval.adj 1 Ca 1 0.13898260 0.1389826 0.1263206 10.976337 0.001 0.014 2 conduct 14 0.03248342 0.1714660 0.1467337 2.626796 0.001 0.014 3 Si 6 0.02722659 0.1986926 0.1622696 2.242529 0.001 0.014 4 NH3 10 0.02398868 0.2226813 0.1748463 2.005953 0.003 0.033 5 NO3 9 0.02112004 0.2438013 0.1847233 1.787470 0.005 0.050 6 Mg 2 0.01914995 0.2629513 0.1927562 1.636862 0.014 0.126 7 pH 13 0.01744989 0.2804012 0.1991562 1.503467 0.020 0.160 After the correction, the last two variables (Mg and pH) are already not significant. It is possible to help the intrepretation by adding the significance stars (e.g. three stars for P < 0.001). This can be done by many ways; perhaps the simplest is to use the function stars.pval from the package gtools (you need to install it if you haven't used it before): # install.packages ('gtools') sel.fs$pval.adj.stars <- gtools::stars.pval (pval.adj)
sel.fs
  variables order         R2     R2Cum  AdjR2Cum         F  pval pval.adj pval.adj.stars
1        Ca     1 0.13898260 0.1389826 0.1263206 10.976337 0.001    0.014              *
2   conduct    14 0.03248342 0.1714660 0.1467337  2.626796 0.001    0.014              *
3        Si     6 0.02722659 0.1986926 0.1622696  2.242529 0.001    0.014              *
4       NH3    10 0.02398868 0.2226813 0.1748463  2.005953 0.003    0.033              *
5       NO3     9 0.02112004 0.2438013 0.1847233  1.787470 0.005    0.050              *
6        Mg     2 0.01914995 0.2629513 0.1927562  1.636862 0.014    0.126
7        pH    13 0.01744989 0.2804012 0.1991562  1.503467 0.020    0.160    

Here, even the first selected variable has the significance P < 0.05 (one star). The reason is the number of permutations used in the forward.sel function, which by default equals to 999 (argument nperm). The lowest P-value which can be obtained with 999 permutations is 1/(999+1) = 0.001, and the correction will considerably increase this value (to 0.014 in case of 14 variables). The workaround is to increase the number of permutations to be able to get lower P-values, which even after correction could still fall below 0.001 threshold (here using 49,999 permutations to achieve P-value 1/50000 = 0.00002).

sel.fs <- forward.sel (vasc.hell, chem1, nperm = 49999)
pval.adj <- p.adjust (sel.fs$pval, method = 'holm', n = ncol (chem1)) sel.fs$pval.adj <- pval.adj
sel.fs$pval.adj.stars <- gtools::stars.pval (pval.adj) sel.fs  variables order R2 R2Cum AdjR2Cum F pval pval.adj pval.adj.stars 1 Ca 1 0.13898260 0.1389826 0.1263206 10.976337 0.00002 0.00028 *** 2 conduct 14 0.03248342 0.1714660 0.1467337 2.626796 0.00004 0.00052 *** 3 Si 6 0.02722659 0.1986926 0.1622696 2.242529 0.00016 0.00192 ** 4 NH3 10 0.02398868 0.2226813 0.1748463 2.005953 0.00080 0.00880 ** 5 NO3 9 0.02112004 0.2438013 0.1847233 1.787470 0.00304 0.03040 * 6 Mg 2 0.01914995 0.2629513 0.1927562 1.636862 0.00824 0.07416 . 7 pH 13 0.01744989 0.2804012 0.1991562 1.503467 0.02094 0.16752  Since the forward.sel function is not optimised, the calculation with 49,999 permutation take some time, but the reward is the P-values (after correction) below 0.001 in case of the first two variables. You can see that forward.sel function selected (after the correction) five environmental variables, which together explain 18.5% of the variance (adjusted R2, the column AdjR2Cum in the table above showing cumulative adjusted R2 of all variables). This is just slightly less than 20.1% which is explained by global model with 14 variables (see the result of RsquareAdj (rda.all)$adj.r.squared above).

In this case, the results of forward.sel function are the same as ordiR2step and ordistep functions below (option b and c), with seven included environmental variables (if calculated without correction for multiple testing).

#### Option b) Use of ordiR2step function

The vegan's function ordiR2step does similar job as forward.sel from adespatial, but it's use is a bit more complex. On the other side, the method is more general - it allows to use also CCA or db-RDA methods, while forward.sel is based purely on linear constrained ordination (RDA, and tb-RDA in the case that the species composition data are pre-transformed e.g. by Hellinger transformation). In the default setting the criteria for including the variable is based on both significance of the newly selected variables, and the comparison of adjusted variation (R2adj) explained by the selected variables to R2adj explained by the global model (with all variables); if the new variable is not significant or the R2adj of the model including this new variable would exceed the R2adj of the global model, the selection will be stopped.

To use the ordiR2step function, you need to specify two models - the “empty” model containing only intercept, and the “full” model including all variables (aka global model). Also, the argument direction allows for selecting the selection direction (forward, backward, both, with the default to both):

tb_rda.vasc.0 <- rda (vasc.hell ~ 1, data = chem1) # model containing only species matrix and intercept
tb_rda.vasc.all <- rda (vasc.hell ~ ., data = chem1) # model including all variables from matrix chem1 (the dot after tilda (~) means "include all from data")

The function ordiR2step then uses the empty model and the scope (variables) from the full model to proceed the selection. Set direction = 'forward'  to ensure that forward instead of stepwise selection is done, and increse the number of permutations to 999 (instead of default 499) to make results comparable to forward.sel (which by default is based on 999 permutations):

sel.osR2 <- ordiR2step (tb_rda.vasc.0, scope = formula (tb_rda.vasc.all), R2scope = adjR2.tbrda, direction = 'forward', permutations = 999)

Step: R2.adj= 0
Call: vasc.hell ~ 1

<All variables>  0.201104784
+ Ca             0.126320581
+ conduct        0.109418731
+ Mg             0.106926067
+ Corg           0.076260217
+ pH             0.074169805
+ Na             0.043030978
+ NH3            0.031875777
+ Si             0.022863900
+ SO4            0.018929801
+ K              0.015918981
+ NO3            0.008112568
+ Fe             0.004863012
+ PO4            0.002933349
<none>           0.000000000
+ Cl            -0.002347612

Df     AIC      F Pr(>F)
+ Ca  1 -47.149 10.976  0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: vasc.hell ~ Ca

<All variables>   0.2011048
+ conduct         0.1467337
+ NH3             0.1417066
+ Na              0.1403837
+ Si              0.1403827
+ pH              0.1403289
+ Mg              0.1386000
+ Corg            0.1375824
+ NO3             0.1340217
+ K               0.1302515
+ Fe              0.1299960
+ SO4             0.1266017
<none>            0.1263206
+ Cl              0.1254155
+ PO4             0.1235944

Df     AIC      F Pr(>F)
+ conduct  1 -47.841 2.6268  0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: vasc.hell ~ Ca + conduct

<All variables>   0.2011048
+ Si              0.1622696
+ NH3             0.1592776
+ NO3             0.1572584
+ Mg              0.1565375
+ Corg            0.1538422
+ Na              0.1533884
+ Fe              0.1517167
+ pH              0.1513672
+ K               0.1511453
+ SO4             0.1495485
+ Cl              0.1470011
<none>            0.1467337
+ PO4             0.1444723

Df    AIC      F Pr(>F)
+ Si  1 -48.18 2.2425  0.002 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: vasc.hell ~ Ca + conduct + Si

<All variables>   0.2011048
+ NH3             0.1748463
+ NO3             0.1739710
+ Mg              0.1708815
+ Corg            0.1689184
+ pH              0.1667252
+ SO4             0.1666206
+ Na              0.1661906
+ Fe              0.1655162
+ K               0.1653789
<none>            0.1622696
+ Cl              0.1620351
+ PO4             0.1599773

Df     AIC     F Pr(>F)
+ NH3  1 -48.308 2.006  0.001 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: vasc.hell ~ Ca + conduct + Si + NH3

<All variables>   0.2011048
+ NO3             0.1847233
+ Mg              0.1835775
+ SO4             0.1799079
+ pH              0.1798906
+ Corg            0.1793391
+ Fe              0.1786200
+ K               0.1766585
+ Na              0.1756240
<none>            0.1748463
+ Cl              0.1737180
+ PO4             0.1725072

Df     AIC      F Pr(>F)
+ NO3  1 -48.236 1.7875  0.005 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: vasc.hell ~ Ca + conduct + Si + NH3 + NO3

<All variables>   0.2011048
+ Mg              0.1927562
+ pH              0.1907475
+ Corg            0.1890699
+ SO4             0.1876281
+ Fe              0.1869468
+ K               0.1868746
+ Na              0.1848251
<none>            0.1847233
+ Cl              0.1834910
+ PO4             0.1826904

Df     AIC      F Pr(>F)
+ Mg  1 -48.032 1.6369  0.011 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: vasc.hell ~ Ca + conduct + Si + NH3 + NO3 + Mg

<All variables>   0.2011048
+ pH              0.1991562
+ Corg            0.1963215
+ SO4             0.1958876
+ K               0.1956246
+ Fe              0.1953612
<none>            0.1927562
+ Na              0.1922729
+ Cl              0.1915194
+ PO4             0.1907648

Df     AIC      F Pr(>F)
+ pH  1 -47.709 1.5035  0.028 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Call: vasc.hell ~ Ca + conduct + Si + NH3 + NO3 + Mg + pH

+ Corg            0.2023822
+ Fe              0.2015249
+ K               0.2014550
+ SO4             0.2012822
<All variables>   0.2011048
<none>            0.1991562
+ Na              0.1988753
+ PO4             0.1974424
+ Cl              0.1974018
sel.osR2
Call: rda(formula = vasc.hell ~ Ca + conduct + Si + NH3 + NO3 + Mg + pH, data = chem1)

Inertia Proportion Rank
Total          0.5674     1.0000
Constrained    0.1591     0.2804    7
Unconstrained  0.4083     0.7196   62
Inertia is variance

Eigenvalues for constrained axes:
RDA1    RDA2    RDA3    RDA4    RDA5    RDA6    RDA7
0.09590 0.01904 0.01205 0.01027 0.00871 0.00718 0.00595

Eigenvalues for unconstrained axes:
PC1     PC2     PC3     PC4     PC5     PC6     PC7     PC8
0.03601 0.03279 0.02383 0.01908 0.01687 0.01593 0.01504 0.01358
(Showed only 8 of all 62 unconstrained eigenvalues)

The summary table is part of the ordiR2step function output, stored in the component $anova: sel.osR2$anova
                 R2.adj Df     AIC       F Pr(>F)
+ Ca            0.12632  1 -47.149 10.9763  0.001 ***
+ conduct       0.14673  1 -47.841  2.6268  0.001 ***
+ Si            0.16227  1 -48.180  2.2425  0.002 **
+ NH3           0.17485  1 -48.308  2.0060  0.001 ***
+ NO3           0.18472  1 -48.236  1.7875  0.005 **
+ Mg            0.19276  1 -48.032  1.6369  0.011 *
+ pH            0.19916  1 -47.709  1.5035  0.028 *
<All variables> 0.20111
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

The result is exactly the same as results of forward.sel function and also as results of ordistep method based on P-values and AIC criteria (below). P-values can be adjusted for multiple testing (e.g. by Holm's correction) as follows:

sel.osR2_adj <- sel.osR2
sel.osR2_adj$anova$Pr(>F) <- p.adjust (sel.osR2$anova$Pr(>F), method = 'holm', n = ncol (chem1))
sel.osR2_adj$anova  R2.adj Df AIC F Pr(>F) + Ca 0.12632 1 -47.149 10.9763 0.014 * + conduct 0.14673 1 -47.841 2.6268 0.014 * + Si 0.16227 1 -48.180 2.2425 0.022 * + NH3 0.17485 1 -48.308 2.0060 0.014 * + NO3 0.18472 1 -48.236 1.7875 0.050 * + Mg 0.19276 1 -48.032 1.6369 0.099 . + pH 0.19916 1 -47.709 1.5035 0.224 <All variables> 0.20111 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 You may consider to increase the number of permutations (argument permutations in ordiR2step to reach lower P-values; in the forward.sel example above we increased the permutations to 49,999 to make sure that even after adjustement of P-values the most significant one could be smaller than 0.001. #### Option c) Use of ordistep function Function ordistep performs step-wise selection of environmental variables based two criteria: if their inclusion into the model leads to significant increase of explained variance (the same as in forward.sel and ordiR2step), and if the AIC of the new model is lower than AIC of the more simple model. In contrary to previous functions, the function does not consider as criteria whether the adjusted R2 of the model exceeds the adjusted R2 of the global model. The use of ordistep function has the same logic as ordiR2step. rda.vasc.0 <- rda (vasc.hell ~ 1, data = chem1) # model containing only species matrix and intercept rda.vasc.all <- rda (vasc.hell ~ ., data = chem1) # model including all variables from matrix chem1 (the dot after tilda (~) means ALL!) sel.os <- ordistep (rda.vasc.0, scope = formula (rda.vasc.all), direction = 'forward') Start: vasc.hell ~ 1 Df AIC F Pr(>F) + Ca 1 -47.149 10.9763 0.005 ** + conduct 1 -45.808 9.4775 0.005 ** + Mg 1 -45.612 9.2612 0.005 ** + Corg 1 -43.249 6.6964 0.005 ** + pH 1 -43.091 6.5277 0.005 ** + Na 1 -40.775 4.1026 0.005 ** + NH3 1 -39.964 3.2718 0.005 ** + Si 1 -39.316 2.6145 0.005 ** + SO4 1 -39.034 2.3314 0.005 ** + K 1 -38.820 2.1162 0.010 ** + NO3 1 -38.267 1.5643 0.060 . + Fe 1 -38.038 1.3372 0.105 + PO4 1 -37.902 1.2030 0.150 + Cl 1 -37.532 0.8384 0.700 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step: vasc.hell ~ Ca Df AIC F Pr(>F) + conduct 1 -47.841 2.6268 0.005 ** + NH3 1 -47.430 2.2190 0.005 ** + pH 1 -47.318 2.1081 0.005 ** + Na 1 -47.322 2.1125 0.010 ** + Si 1 -47.322 2.1124 0.010 ** + Mg 1 -47.177 1.9694 0.010 ** + Corg 1 -47.095 1.8880 0.010 ** + NO3 1 -46.806 1.6047 0.035 * + K 1 -46.502 1.3073 0.110 + Fe 1 -46.482 1.2873 0.115 + SO4 1 -46.209 1.0219 0.435 + Cl 1 -46.114 0.9296 0.590 + PO4 1 -45.968 0.7885 0.890 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step: vasc.hell ~ Ca + conduct Df AIC F Pr(>F) + Si 1 -48.180 2.2425 0.005 ** + NH3 1 -47.931 1.9997 0.005 ** + NO3 1 -47.763 1.8367 0.005 ** + Mg 1 -47.703 1.7788 0.005 ** + Corg 1 -47.480 1.5629 0.020 * + Na 1 -47.442 1.5266 0.035 * + K 1 -47.257 1.3482 0.040 * + pH 1 -47.275 1.3658 0.050 * + SO4 1 -47.125 1.2218 0.085 . + Fe 1 -47.304 1.3936 0.095 . + Cl 1 -46.916 1.0210 0.420 + PO4 1 -46.709 0.8229 0.810 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step: vasc.hell ~ Ca + conduct + Si Df AIC F Pr(>F) + NH3 1 -48.308 2.0060 0.005 ** + NO3 1 -48.234 1.9350 0.005 ** + Mg 1 -47.972 1.6855 0.020 * + Corg 1 -47.807 1.5280 0.025 * + SO4 1 -47.614 1.3446 0.055 . + pH 1 -47.622 1.3529 0.075 . + Na 1 -47.577 1.3104 0.095 . + Fe 1 -47.521 1.2568 0.105 + K 1 -47.509 1.2459 0.130 + Cl 1 -47.229 0.9815 0.470 + PO4 1 -47.058 0.8199 0.805 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step: vasc.hell ~ Ca + conduct + Si + NH3 Df AIC F Pr(>F) + NO3 1 -48.236 1.7875 0.005 ** + Mg 1 -48.138 1.6951 0.005 ** + SO4 1 -47.824 1.4012 0.035 * + pH 1 -47.822 1.3998 0.045 * + Corg 1 -47.775 1.3558 0.045 * + Fe 1 -47.714 1.2986 0.125 + K 1 -47.547 1.1431 0.235 + Na 1 -47.459 1.0613 0.370 + Cl 1 -47.298 0.9112 0.665 + PO4 1 -47.195 0.8163 0.805 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step: vasc.hell ~ Ca + conduct + Si + NH3 + NO3 Df AIC F Pr(>F) + Mg 1 -48.032 1.6369 0.010 ** + pH 1 -47.858 1.4764 0.040 * + Corg 1 -47.713 1.3430 0.040 * + SO4 1 -47.588 1.2288 0.135 + Fe 1 -47.530 1.1750 0.170 + K 1 -47.523 1.1693 0.210 + Na 1 -47.347 1.0080 0.480 + Cl 1 -47.233 0.9034 0.600 + PO4 1 -47.164 0.8408 0.785 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step: vasc.hell ~ Ca + conduct + Si + NH3 + NO3 + Mg Df AIC F Pr(>F) + pH 1 -47.709 1.5035 0.040 * + SO4 1 -47.424 1.2453 0.095 . + Corg 1 -47.462 1.2795 0.100 . + Fe 1 -47.378 1.2040 0.115 + K 1 -47.401 1.2247 0.135 + Na 1 -47.110 0.9623 0.580 + Cl 1 -47.044 0.9036 0.650 + PO4 1 -46.979 0.8450 0.775 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step: vasc.hell ~ Ca + conduct + Si + NH3 + NO3 + Mg + pH Df AIC F Pr(>F) + Corg 1 -47.130 1.2508 0.140 + Fe 1 -47.054 1.1839 0.150 + K 1 -47.048 1.1785 0.175 + SO4 1 -47.033 1.1650 0.185 + Na 1 -46.823 0.9783 0.595 + PO4 1 -46.697 0.8676 0.715 + Cl 1 -46.694 0.8645 0.735 Results are stored in sel.os object: sel.os Call: rda(formula = vasc.hell ~ Ca + conduct + Si + NH3 + NO3 + Mg + pH, data = chem1) Inertia Proportion Rank Total 0.5674 1.0000 Constrained 0.1591 0.2804 7 Unconstrained 0.4083 0.7196 62 Inertia is variance Eigenvalues for constrained axes: RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 0.09590 0.01904 0.01205 0.01027 0.00871 0.00718 0.00595 Eigenvalues for unconstrained axes: PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 0.03601 0.03279 0.02383 0.01908 0.01687 0.01593 0.01504 0.01358 (Showed only 8 of all 62 unconstrained eigenvalues) Selected variables are Ca, conduct, Si, NH3, NO3, Mg and pH. ### Example 2: CCA with forward selection on data from Carpathian wetlands This example is using the same data as the Example 1 above, namely composition of vascular plants in Carpathian wetlands and chemical variables measured in fen water running through them. While in Example 1 we analysed the data using tb-RDA (RDA applied on data after Hellinger transformation), here we do this using CCA (note: no Hellinger transformation is done). Since the function forward.sel from adespatial package is using only RDA algorithm, we cannot use it here; instead, we will do the calculation using ordiR2step from vegan, which also implements variable selection with double stopping criteria: the selection is finished if the new variable to be selected is not significant at certain alpha level, or if the adjusted R2 explained by the model with this variable exceeds the adjusted R2 of the global model. # Carpathian wetlands - import data vasc <- read.delim ('https://raw.githubusercontent.com/zdealveindy/anadat-r/master/data/vasc_plants.txt', row.names = 1) chem <- read.delim ('https://raw.githubusercontent.com/zdealveindy/anadat-r/master/data/chemistry.txt', row.names = 1) # the last variable in the 'chem' dataset is 'slope', which is not a chemical variable - remove it: chem1 <- chem[,-15] We do similar steps as in the option b) of Example 1 above: create global model with CCA (including all variables) and test it's significance, and if it is significant, we will create also an empty model with only intercept and use the ordiR2step function with appropriate arguments: cca.all <- cca (vasc ~ ., data = chem1) anova (cca.all) # P < 0.001 - yes, significant! adjRsq.cca <- RsquareAdj (cca.all)$adj.r.square  # adjR2 as stopping criteria used in ordiR2step
cca.0 <- cca (vasc ~ 1, data = chem1) # empty model only with intercept

The function ordiR2step needs to know the variables which are being selected (this is defined by the empty model and the scope taken from the global model), the direction of the selection (forward, backward, both - default is both, which means combined forward and backward), the threshold of adjusted R2 which should be used as stopping criteria (R2scope), number of permutations for the Monte Carlo permutation test applied on each variable (default is 499). Additionally, I set also argument trace = FALSE (default is TRUE), which limits the verbal output of the function (as you can see above, the function is rather talkative).

sel.cca <- ordiR2step (cca.0, scope = formula (cca.all), R2scope = adjRsq.cca, direction = 'forward', permutations = 999, trace = FALSE)
sel.cca
Call: cca(formula = vasc ~ Ca + conduct + Si + NH3 + NO3 + SO4 + Corg, data = chem1)

Inertia Proportion Rank
Total          3.0238     1.0000
Constrained    0.6772     0.2240    7
Unconstrained  2.3466     0.7760   62
Inertia is scaled Chi-square

Eigenvalues for constrained axes:
CCA1   CCA2   CCA3   CCA4   CCA5   CCA6   CCA7
0.3388 0.0942 0.0618 0.0569 0.0485 0.0418 0.0352

Eigenvalues for unconstrained axes:
CA1     CA2     CA3     CA4     CA5     CA6     CA7     CA8
0.17567 0.16047 0.12242 0.10303 0.09427 0.08952 0.08455 0.07697
(Showing 8 of 62 unconstrained eigenvalues)
sel.cca$anova  R2.adj Df AIC F Pr(>F) + Ca 0.082091 1 385.90 7.1738 0.001 *** + conduct 0.095737 1 385.81 2.0268 0.001 *** + Si 0.108800 1 385.76 1.9704 0.001 *** + NH3 0.117746 1 385.96 1.6923 0.006 ** + NO3 0.125431 1 386.27 1.5578 0.019 * + Corg 0.130893 1 386.75 1.3870 0.022 * + SO4 0.136586 1 387.17 1.4114 0.041 * + Mg 0.141018 1 387.65 1.3382 0.043 * <All variables> 0.141602 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 The first five variables (Ca, conductivity, Si, NH3 and NO3) are the same as in tb-RDA analysis above, the last two differ. If we increase the number of permutations to 49,999 (to reduce the lowest P-value we can obtain - but this takes quite some time!) and adjust the P-values with Holm's correction for multiple testing issue, only the first four variables will be selected (compared to the first five in the case of tb-RDA): sel.cca.49999 <- ordiR2step (cca.0, scope = formula (cca.all), direction = 'forward', R2scope = adjRsq.cca, permutations = 49999, trace = F) sel.cca.49999_adj <- sel.cca.49999 sel.cca.49999_adj$anova$Pr(>F) <- p.adjust (sel.cca.49999$anova$Pr(>F), method = 'holm', n = ncol (chem1)) sel.cca.49999_adj$anova
                  R2.adj Df    AIC      F  Pr(>F)
+ Ca            0.082081  1 385.90 7.1738 0.00028 ***
+ conduct       0.095628  1 385.81 2.0268 0.00312 **
+ Si            0.108801  1 385.76 1.9704 0.00312 **
+ NO3           0.117863  1 385.99 1.6612 0.04378 *
+ NH3           0.125336  1 386.27 1.5884 0.11820
+ SO4           0.131177  1 386.73 1.4028 0.30888
+ Mg            0.136262  1 387.19 1.3789 0.30888
+ Corg          0.141158  1 387.65 1.3550 0.30888
<All variables> 0.142456
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1             

You may have noticed that the use of ordiR2step function with CCA takes considerably longer than when applied on RDA (or tb-RDA as above). This is because in the case of CCA, adjusted R2 needs to be calculated using the permutation method introduced by Peres-Neto et al. (2006), while in the case of RDA the adjusted R2 can be calculated analytically by Ezekiel's formula. This also means that in CCA, the resulting adjusted R2 values will slightly change between calculations, because the adjusted R2 value calculation is using the mean of R2 explained by randomized environmental variables (by default 1000 randomizations is used; this number can be increased by including the argument R2permutations in the ordiR2step function with a higher number). These small changes between calculation can eventually lead to selecting slightly different sets of variables or selecting them in a different order (as in the case of this example - when you see the results of the forward selection in CCA, the first one selected variables in the order Ca > conduct > Si > NH3 > NO3 > Corg > SO4 > Mg, while the latter (with 49,999 permutations) selected them in the order Ca > conduct > Si > NO3 > NH3 > SO4 > Mg > Corg (the order changed after the third selected variable). Increasing R2permutations should decrease such fluctuation (but will also increase the calculation time).