Dear list members,
I would like to test a linear model between a predictor variable (e.g.
altitude) and a dependent variable (e.g. number of flowers). For this, I do
have several observations of individuals of 42 species along the predictor
variable, but the number of observations do vary per each species and class
of the independent variable. In the end, I do have 364 observations
unbalanced per each of the 42 species and environment.
My intention is doing a model having the number of flower as dependent
variable, the temperature as fixed effect, using species as a random factor
and add a phylogenetic correction. I could do part of this this using GLS,
but the fact of having multiple observations per species do not allow
running the model and returns the following message:
*“number of observations and number of tips in the tree are not equal.”*
I would much appreciate if you could give me advices on how to overcome
this issue and run such model.
Many thanks,
Nicolay
Grupo de Ecología de la Polinización, INIBIOMA,
CONICET-Universidad Nacional del Comahue,
Quintral 1250, 8400 San Carlos de Bariloche, Rio Negro, Argentina
#**************************************************************************************#
# Below an example code
*library(phytools)tree <- rtree(5, rooted = FALSE, tip.label =
LETTERS[1:5])x <- data.frame( spp = c(sample(c(LETTERS [1:3]),100,
replace = T), sample(c(LETTERS [2:4]),100, replace =
T), sample(c(LETTERS [3:5]),100, replace = T)),
alt = rep(c("1000", "1500", "2000"), each = 100),*
* flower = rnorm(300)) library(nlme)fit <- gls(flower ~ alt,
correlation = corPagel(1,tree, fixed=FALSE), method="ML", data = x) *
#**************************************************************************************#
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