Please do not repeatedly post the same thing. This is the same as https://stat.ethz.ch/pipermail/r-help/2006-February/086381.html
(except you remembered to sign that one). You are fitting a weighted not a generalised least squares model: lm() will do that. On Thu, 9 Feb 2006, [EMAIL PROTECTED] wrote: > I am trying to fit a generalised least squares model using gls in the nlme > package. > > The model seems to fit very well when I plot the fitted values against the > original values, and the model parameters have quite narrow confidence > intervals (all are significant at p<5%). > > The problem is that the log likelihood is always given as -Inf. This > doesn't seem to make sense because the model seems to fit my data so well. > I have checked that the residuals are stationary using an adf test. I > can't work out whether > - the model really doesn't fit at all > - there is something in my data that stops the implementation of logLik > working correctly (the -Inf value says the calculation hasn't worked) > > Possible causes are: > - There are lots of NAs in my data (model and response variables) > - There is some autocorrelation in the data that is not accounted for by > the model (most is accounted for). > > But, I've tried recreating the problem using a simpler data set, and have > never found the same problem. Well, how then do you expect us to be able to recreate it? As a pure guess, look at your weights. Are any numob4150 zero? > The command I use to fit the model is... > > > > result2 <- gls(lci4150 ~ propCapInStomachs + > temperature + > as.factor(monthNumber) + > lagLci1 + > lagcap1 + > lagcap2, > data = monthly, > subset = subset1985, > na.action = na.approx, > weights = varFixed( ~ 1/numob4150) > ) > > > > The output I get is... > > > > Generalized least squares fit by REML > Model: lci4150 ~ propCapInStomachs + temperature + > as.factor(monthNumber) + lagLci1 + lagcap1 + lagcap2 > Data: monthly > Subset: subset1985 > AIC BIC logLik > Inf Inf -Inf > > Variance function: > Structure: fixed weights > Formula: ~1/numob4150 > > Coefficients: > Value Std.Error t-value p-value > (Intercept) -0.3282412 0.5795665 -0.566356 0.5717 > propCapInStomachs 0.0093283 0.0039863 2.340107 0.0202 > temperature 0.4342514 0.1526104 2.845490 0.0048 > as.factor(monthNumber)2 0.3990717 0.3869991 1.031195 0.3036 > as.factor(monthNumber)3 1.3788334 0.3675690 3.751223 0.0002 > as.factor(monthNumber)4 1.4037195 0.3857764 3.638686 0.0003 > as.factor(monthNumber)5 0.9903316 0.3436177 2.882074 0.0043 > as.factor(monthNumber)6 0.3453741 0.3043698 1.134719 0.2577 > as.factor(monthNumber)7 0.3948442 0.3035142 1.300909 0.1946 > as.factor(monthNumber)8 0.5021812 0.3532413 1.421638 0.1565 > as.factor(monthNumber)9 -0.0794319 0.3598981 -0.220707 0.8255 > as.factor(monthNumber)10 0.3536805 0.3790538 0.933061 0.3518 > as.factor(monthNumber)11 0.7874834 0.3557116 2.213826 0.0278 > as.factor(monthNumber)12 0.1854279 0.3178320 0.583415 0.5602 > lagLci1 0.5488437 0.0576144 9.526151 0.0000 > lagcap1 0.0110994 0.0043669 2.541714 0.0117 > lagcap2 -0.0088080 0.0041099 -2.143127 0.0332 > > > > Does anyone have any suggestions of how I can get a meaningful value for > logLik? Or some other way that I can compare models. > > Thankyou, > > Lillian. > -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
