Hi Sérgio,

`It seems your results make good sense. alpha=0 is too far from the`

`"optimum" value (i.e., the value that maximises the log-lik), so the`

`optimisation fails to maximise the log-likelihood with respect to this`

`parameter. It has been written in the literature that it is difficult to`

`estimate reliably alpha in an OU model, and your results seem to confirm`

`this.`

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`Besides, alpha=0 is equivalent to a Brownian motion model. So if your`

`traits are not really evolving according to this model, the GLS fitting`

`procedure may be in difficulty.`

You may try: correlation = corMartins(value=0.9, phy=tree, fixed=FALSE)

`If this estimates alpha=0.99, then you may probably rely on this result`

`(after checking the estimates of the coefficients of course).`

Best, Emmanuel Le 20/01/2017 à 15:16, Sergio Ferreira Cardoso a écrit :

Dear all, I tried to estimate a value for alpha parameter of corMartins (Ornstein-Uhlenbeck) but the output is a bit puzzling. I do as follows:fitOU<-gls(fcl~mass+loc_dim+activity+feeding+loc_typ+agility,correlation=corMartins(value=0,phy=tree,fixed=FALSE),data=df,method="ML",weights=varFixed(~vf))Error in recalc.corStruct(object[[i]], conLin) :NA/NaN/Inf in foreign function call (arg 4) Enter a frame number, or 0 to exit 1: gls(fcl ~ mass + loc_dim + activity + feeding + loc_typ + agility, correlati 2: nlminb(c(coef(glsSt)), function(glsPars) -logLik(glsSt, glsPars), control = 3: objective(.par, ...) 4: logLik(glsSt, glsPars) 5: logLik.glsStruct(glsSt, glsPars) 6: recalc(object, conLin) 7: recalc.modelStruct(object, conLin) 8: recalc(object[[i]], conLin) 9: recalc.corStruct(object[[i]], conLin) It work when I turn the "value" to 1, but then the estimate for alpha is 0.99, which looks like it isn't actually estimating a value and is instead accepting my input as the alpha value. Do you think there is a reason for this to happen? Thank you in advance. Cheers, Sérgio.

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