Hello, I am new to this mailing list and also to glmms. I am writing concerning a discrepancy I observed between glmmADMB summary output and and glht comparisons which made me want to enquire about the "best" way to proceed for an analysis. As in this thread <https://stats.stackexchange.com/questions/230734/post-hoc-output-and-glmm-output-dont-add-up> I'm observing different results between a negative binomial glmm summary model output and glht post-hoc comparisons. I have pasted reproducible code below. I did a little digging online, and found this <https://www.researchgate.net/post/Multiple_comparisons_in_GLMMs>post <https://www.researchgate.net/post/Multiple_comparisons_in_GLMMs> which lead me to believe this has to do with multiplicity. (When I use an unadjusted test, as the answer in the post suggests, I find the same results as in the model summary). Still, I find myself wondering if in general its better to use the glht function that corrects for multiple comparisons (and doesn't match up with the model outputs), or if I should use the glht function that doesn't correct for multiple comparisons. Based on some posts (example <https://stats.stackexchange.com/questions/204741/which-multiple-comparison-method-to-use-for-a-lmer-model-lsmeans-or-glht>) I've read I suspect that its 'better' to correct for multiple comparisons to avoid Type 1 error, but I am not sure about this given the mismatch with the model output and wanted to ask before moving forward. I have not encountered this problem before as I have only previously used a model selection approach with linear mixed models, and did not have to think about p-values and post-hoc analyses for mixed models. Thank you for your time, I really appreciate any help/clarifications on this topic. Dalal Hanna ###Reproducible example#### #Generate dataframe RecreationalTrails<-c(5, 0, 0, 4, 7, 0, 0, 0, 6, 5, 0, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0,4, 6, 8, 0, 0, 7) LandUse<-c("Protected", "Agricultural", "Forestry", "Unprotected Forest", "Protected", "Agricultural", "Forestry", "Unprotected Forest", "Protected", "Agricultural", "Forestry", "Unprotected Forest","Protected", "Agricultural", "Forestry", "Unprotected Forest", "Protected", "Agricultural", "Forestry", "Unprotected Forest","Protected", "Agricultural", "Forestry", "Unprotected Forest", "Protected", "Agricultural", "Forestry", "Unprotected Forest") Parc<-c("Monts Valin", "Monts Valin", "Monts Valin", "Monts Valin", "Fjords du Saguenay", "Fjords du Saguenay","Fjords du Saguenay", "Fjords du Saguenay", "Hautes Gorges", "Hautes Gorges","Hautes Gorges","Hautes Gorges", "Grands Jardins", "Grands Jardins", "Grands Jardins", "Grands Jardins", "Mont Tremblant", "Mont Tremblant", "Mont Tremblant", "Mont Tremblant", "Mauricie", "Mauricie", "Mauricie", "Mauricie", "Jacques Cartier", "Jacques Cartier", "Jacques Cartier", "Jacques Cartier") ESCombinedDataRE<-data.frame(c("LandUse", "Parc", "RecreationalTrails")) ESCombinedDataRE <- data.frame(LandUse, Parc, RecreationalTrails) names(ESCombinedDataRE) <- c("LandUse", "Parc", "RecreationalTrails") ESCombinedDataRE$LandUse <- factor(ESCombinedDataRE$LandUse, levels=c("Protected", "Unprotected Forest", "Forestry", "Agricultural")) ESCombinedDataRE str(ESCombinedDataRE) #Run model library(glmmADMB) R_glmer <- glmmadmb(RecreationalTrails ~ LandUse+ (1|Parc), data=ESCombinedDataRE, family= "nbinom") #Validate model #homogeneity ResidR<-resid(R_glmer) qqnorm(ResidR) qqline(ResidR, col=2) #Note that I am not certain that this model actually meets its assumptions. I see here that there is #likely a problem of homogeneity, but am not sure what alternative model I could run for this dataset, suggestions most welcome. #Model results summary(R_glmer) #Post-hoc comparisons summary(glht(R_glmer, linfct=mcp(LandUse="Tukey"))) #Alternative post-hoc comparison without adjusted test mc_RT <- glht(R_glmer, linfct=mcp(LandUse="Tukey")) summary(mc_RT, test=adjusted("none"))
Concern/question regarding discrepancy between glmmADBM summary output and glht comparisons output
1 message · Dalal Hanna