Hi R users,
I have some uncertanties regarding comparative method in R. Regarding
especially the lamba and K values. I am using 'ape' and 'nlme' packages and
'corPagel' and 'gls' functions in a regression model. My questions regards
this model. If I wish to calculate signal in multiple regression (say two
variables) I can use formula: Y ~ X + bodymass. This will take account of
body mass. What If I only want to find signal in one variable? Should the
model be: Y ~ bodymass ? Will this give lambda value for Y after controling
for body mass, or should I instead correct for body mass using a ratio (Y /
body mass) and get lambda for this - using perhaps 'fitContinuous' in
'geiger'. It seems better to me to use regression model, but I welcome your
thoughts on the matter.
I also whish to calculate K value. This can be done with 'phylosignal'
function in 'picante' package. This calculates K for one variable (already
scaled for body mass). Is it possible to calculate K in regression model,
like lambda?
One final question - I see people use often logarithm of trait values in
pgls. Is this ok? It seems to me this transformation would reduce distance
between means along the right tail of a sample and would thus distort signal
measurment?
kind regards,
Alberto
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