I have autocorrelated data that show a positive linear increase. When I model them using gls, I think the summary shows overdispersion.
When using GLMM etc I'd change error structure, but I don't think I can change family in gls. Is there a way to address overdispersion in a gls model?
library(nlme) nest <- c(4087,2761,3807,4158,2046,4757,2984,3316,3143, 3042,4429,3335,5124,2464,3713,3028,5739,4671,3799,6167,2937,5031) y <- seq(1997,2018,1) m1 <- glm(nest~y) summary(m1) acf(resid(m1),type="p") #residuals show autocorrelation at one year, so I need to use gls m1.gls <- gls(nest~y,correlation=corARMA(p=1), method="ML") summary(m1.gls) acf(resid(m1.gls)) acf(resid(m1.gls),type="p") m0.gls <- gls(nest~y,correlation=NULL,method="ML") AIC(m0.gls,m1.gls) anova(m1.gls,m0.gls) #L.Ratio = chisqu = 7.382355;p = 0.0066 #autocorrelation is significant null.gls <- gls(nest~1,correlation=corARMA(p=1), method="ML") anova(m1.gls,null.gls) #L.Ratio = chisq = 7.956113; p = 0.0048 #trend is significant summary(m1.gls) #residual se = 970 over 22 df
Does this show overdispersion?