Have you tried "anova(fit1, fit2)", where fit1 <- lme(one model...) fit2 <- lme(a submodel ... )
This "anova" does about the best that anyone knows how to do -- or at lest did 7 years ago when it was written. If the "submodel" changes the fixed effects, you should use "method='ML'". If the "submodel" changes the noise model specification, use "method='REML'". See Pinheiro and Bates (2000) Mixed-Effect Models in S and S-Plus (Springer). If you need something more precise than the standard approximations, try "simulate.lme". buena suerte! spencer graves Patricia Balvanera wrote: > Dear R users, > > I am using lme and nlme to account for spatially correlated errors as > random effects. My basic question is about being able to correct F, p, R2 > and parameters of models that do not take into account the nature of such > errors using gls, glm or nlm and replace them for new F, p, R2 and > parameters using lme and nlme as random effects. > > I am studying distribution patterns of 50 tree species along a gradient. > That gradient > was sampled through 27 transects, with 10 plots within each transect. For > each plot I > have data on presence/absence, abundance and basal area of the species. I > also have data > for 4 environmental variables related to water availability (soil water > retention > capacity, slope, insolation, altitude) and X and Y coordinates for each > plot. I explored > wether the relationship between any of the response variables > (presence/absence, > abundance, basal area) and the environmental variables was linear, > polinomial, or > non-linear. > > My main interest in this question is that I proceeded to correct for spatial > autocorrelation (both within transects and overall) following the > procedures suggest by > Crawley 2002 for linear models > e.g. (GUAMAC = a species, CRAS = soil water retention capacity, TRANSECTO = > transect) > > model1<-gls(GUAMAC ~ CRAS) > > model2<-lme(GUAMAC ~ CRAS, random = ~ 1 | TRANSECTO) > > model3<-lme(GUAMAC ~ CRAS, random = GUAMAC ~ CRAS | TRANSECTO) > > model4<-lme(GUAMAC ~ CRAS, random = GUAMAC ~ CRAS -1 | TRANSECTO) > > AIC(model1,model2,model3,model4) > df AIC > model1 3 3730.537 > model2 4 3698.849 > model3 6 3702.408 > model4 4 3704.722 > > plot(Variogram(model2, form = ~ X + Y)) > > model5<-update(model2,corr=corSpher(c(30,0.8), form = ~ X + Y, nugget = T)) > > plot(Variogram(modelo7, resType = "n")) > > summary(model5) > > In this case I obtain new F for the independent variable INSOLACION, new R2 > for the whole model and new parameters for the linear model. > > I have also applied this procedure to polinomial models and to glms with > binomial errors > (presence/absence) with no problem. > > I am nevertheless stuck with non-linear models. I am using the protocols > you suggested > in the 1998 manuals by Pinheiro and Bates, and those suggested by Crawley > 2002. > Please find enclose an example with an > exponential model (which I chose for being simple). In fact the linear > models I am using > are a bit more complicated. > (HELLOT is a species, INSOLACION = INSOLATION, basal = basal area of the > species, TRANSECTO = transect) > > > HELLOT ~ exp(A + (B * INSOLACION)) > > basal.HELLOT <-function(A,B,INSOLACION) exp(A + (B * INSOLACION)) > > HELLOT ~ basal.HELLOT(A,B,INSOLACION) > > basal.HELLOT<- deriv(~ exp(A + (B * INSOLACION)) > + , LETTERS [1:2], function(A, B, INSOLACION){}) > > model1<- nlme(model = HELLOT ~ exp(A + (B * INSOLACION)), fixed = A + B > ~ 1, > random = A + B ~ 1, groups = ~ TRANSECTO, start = list(fixed = c(5.23, > -0.05))) > > It runs perfectly and gives new values for parameters A and B, but would > only give me F for fixed effects of A and B, while what I am really looking > for is F for fixed effects of INSOLACION and the R2 of the new model. > > Thank you so much in advance for your help > > > > Dra. Patricia Balvanera > Centro de Investigaciones en Ecosistemas, UNAM-Campus Morelia > Apdo. Postal 27-3, Xangari > 58090 Morelia, Michoacán, Mexico > Tel. (52-443)3-22-27-07, (52-55) 56-23-27-07 > FAX (52-443) 3-22-27-19, (52-55) 56-23-27-19 > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Spencer Graves, PhD Senior Development Engineer PDF Solutions, Inc. 333 West San Carlos Street Suite 700 San Jose, CA 95110, USA [EMAIL PROTECTED] www.pdf.com <http://www.pdf.com> Tel: 408-938-4420 Fax: 408-280-7915 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html