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