Hello Thierry
Thanks for your answer!
I am interested in the interaction between period and phylogeny, to see
whether the trends differ between groups. If I understood correctly, I
shouldn't put something that I am interested in in the random part, am I
right?
Best
Di
2016-12-20 22:03 GMT+01:00 Thierry Onkelinx <thierry.onkelinx at inbo.be>:
Dear Diego,
The linear trend is required otherwise the optimum of the parabola is
fixed a x = 0.
You could try to fit the phylogeny effect as a random effect instead of a
fixed effect. poly(Period, 2) | Phyloge ny and add species as a nested
random intercept.
If you have the individual latitudes you can try to use those instead of
using only their average.
Best regards,
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
2016-12-20 17:49 GMT+01:00 Diego Pavon <diego.pavonjordan at gmail.com>:
Dear all,
I write you because I need some advice about the model I want to fit to
my
data, which I suspect it is not too 'correct'...
My response variable is continuous (mean weighted latitude) of 24
species.
I have these mean latitudes calculated for 4 periods (95-99, 00-04,
05-09,
10-13), for each species. Therefore I have 96 observations. I want to see
if there is a trend of the mean latitude over time (indicating a shift in
the population... all the species together). In this case, I would use
Period as a continuous varaible and not a factor. I want to fit a GLMM
with
random = SpeciesID. However, data exploration revealed a quadratic
relationship between my response (mean latitude) and Period (my
continuous
covariate for time). But also, I am interested also to see whether there
are differences between functional groups (some species may move faster
than others). Thus I include the variable Phylogeny, which is a factor
with
5 levels. Thus, the model in nlme notation is:
lme(NEnessKM ~ Period_std + I(Period_std^2) + factor(Phylogeny)
+ Period_std : factor(Phylogeny) + I(Period_std^2) :
factor(Phylogeny),
random = ~1| factor(Species),
weights = varIdent(form =~ 1 | factor(Species)),
control = ctrl2,
method = "REML",
data = NEness5y)
My concern is that this model might be too complicated. I have only 96
observation. If I follow the rule of 15 observation per parameter
estimated, this model i way to complex. I understand that in order to
include quadratic terms, one must include also the linear effect... and
that would also apply for the interactions. If the relationship between
my
response (NEness = Mean Latitude) and Period is a parabola, I guess I
should include also the Period^2 in the interaction (Period^2 *
Phylogeny)?
But in that case, I have to include the interaction of Period *
Phylogany?
Is there a way to reduce the complexity? Is it totally wrong if I do not
include the linear effects and keep only Period^2?
Thanks for sharing your knowledge and for the advice.
Best
Diego
--
*Diego Pav?n Jord?n*
*Finnish Museum of Natural History*
*PO BOX 17 *
*Helsinki. Finland*
*0445061210https://www.researchgate.net/profile/Diego_Pavon-jordan
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