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Functions to combine and adjust the outputs of the fourthcorner
and
randtest.rlq
functions created using permutational models 2 and
4 (sequential approach).
combine.randtest.rlq(obj1, obj2, ...)
combine.4thcorner(four1,four2)
p.adjust.4thcorner(x, p.adjust.method.G = p.adjust.methods,
p.adjust.method.D = p.adjust.methods, p.adjust.D = c("global",
"levels"))
an object of the class 4thcorner created with modeltype = 2 (or 4)
an object of the class 4thcorner created with modeltype = 4 (or 2)
an object created with randtest.rlq
and
modeltype = 2 (or 4)
an object created with randtest.rlq
and
modeltype = 4 (or 2)
an object of the class 4thcorner
a string indicating a method for multiple
adjustment used for output tabG, see p.adjust.methods
for possible choices
a string indicating a method for multiple
adjustment used for output tabD/tabD2, see p.adjust.methods
for possible choices
a string indicating if multiple adjustment for tabD/tabD2 should be done globally or only between levels of a factor ("levels", as in the original paper of Legendre et al. 1997)
further arguments passed to or from other methods
The functions return objects of the same class than their argument. They simply create a new object where pvalues are equal to the maximum of pvalues of the two arguments.
The functions combines the outputs of two objects (created by
fourthcorner
and randtest.rlq
functions) as described in
Dray and Legendre (2008) and ter Braak et al (2012).
Dray, S. and Legendre, P. (2008) Testing the species traits-environment relationships: the fourth-corner problem revisited. Ecology, 89, 3400--3412.
ter Braak, C., Cormont, A., and Dray, S. (2012) Improved testing of species traits-environment relationships in the fourth corner problem. Ecology, 93, 1525--1526.
# NOT RUN {
data(aravo)
four2 <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=2)
four4 <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=4)
four.comb <- combine.4thcorner(four2,four4)
## or directly :
## four.comb <- fourthcorner(aravo$env, aravo$spe, aravo$traits, nrepet=99,modeltype=6)
summary(four.comb)
plot(four.comb, stat = "G")
# }
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