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...s: > sv.mc <- glht(model.sv,linfct=mcp(comp="Tukey")) Error in x$terms : $ operator not defined for this S4 class Error in factor_contrasts(model) : no 'model.matrix' method for 'model' found! What I have done before: > sv.growth <- groupedData(length~meas|box_id,outer=~comp,data=sv.growth) > model.sv <- lmer(length~comp+(meas|box_id),data=sv.growth) Warning message: In .local(x, ..., value) : Estimated variance-covariance for factor 'box_id' is singular > summary(model.sv) Linear mixed-effects model fit by REML Formula: length ~ comp + (...

maybe it's in the data? So here it comes. > sv.growth Grouped Data: length ~ meas | box_id meas spec comp water box_id sprouts leaves length long.sprout 1 1 Sv control moist 1 8.800000 37.80 211.2000 60.6 2 1 Sv xfull moist 2 7.000000 8.00 174.8000 62.8 3 1 Sv control moist 3 9.000000 4...

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <- lmer(length~spec*comp*water+(meas|box_id),data=growth) > summary(model) This works fine. But now, I would like to calculate Tukey HSD among certain factor combinations. Formerly (before version 1.0), Tukey contrasts including all levels of...

...ging system for my users but I''m having a hard time designing my db. This is what i have in mind, but I am not sure if its the best approach. user has_one inbox user has_one outbox inbox has_many messages outbox has_many messages inbox table user_id outbox table user_id messages table box_id (refers to either inbox or outbox - how?) from_id to_id msg_status (new, sent, read, deleted, etc.) subject body does this make sense? is there a better way of acheiving this? since both inbox and outbox both have many messages, how do I created the association between them? will the addition of a...

...Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <- lmer(length~spec*comp*water+(meas|box_id),data=growth) > summary(model) This works fine. But now, I would like to calculate Tukey HSD among certain factor combinations. Formerly (before version 1.0), Tukey contrasts including all levels of...