lme4 categorical outcome probability values at each time point
Dear Igor, Am new to the list, so defer by all means to more experienced opinions. My suggestion would be to use the "lsmeans" package and the inverse logit transformation (to get back to the scale of probabilities from the hard-to-interpret logits) If you have time as a factor this will be no problem. So before you run the model, specify vse$Time<-as.factor(vse$Time) #then use lsmeans by R Lenth #see https://cran.r-project.org/web/packages/lsmeans/vignettes/using-lsmeans.pdf #example # lsmeans(oranges.lm1, "day", at = list(price1 = 50, price2 = c(40,60), day = c("2","3","4")) ) Newdf<- summary(lsmeans(workshop, ~Time*Condition)) #or if Time is numeric: Newdf<- summary(lsmeans(workshop, ~Time*Condition, at=list(Time=c(1, 2, 3)) #if you need to graph standard errors, create a new column with raw std errors first, #then transform the mean +/- SE Newdf$sehi<-Newdf$lsmean+ Newdf$SE Newdf$selo<-Newdf$lsmeans - Newdf$SE #now apply inverse logit library(boot) Newdf$meanprop<-inv.logit (Newdf$lsmean) Newdf$prophi<-inv.logit(Newdf$sehi) Newdf$proplo<-inv.logit(Newdf$selo) You should be able to plot the resulting variables using ggplot2 or your package of choice. You could also use inv.logit on the 95% confidence limits, though the error bars tend to get pretty enormous with these reverse transformations and may not seem to reflect any significant differences in your model. Hope this helps! Evan On Fri, Mar 25, 2016 at 12:20 AM, Igor Yakovenko <iyakoven at ucalgary.ca> wrote:
Hello Everyone,
Does anyone know how to graph the results of a lme4 longitudinal multilevel
model as a bar graph of Time (3 points) on x-axis with probability of
getting the 1 value on the outcome after taking into account the predictors
as the y-axis? Alternatively, if there is a way to derive the mean
probability of the outcome being 1 (vs. 0) during an intervention at time
1,
2 and 3 divided by Condition (two levels), I can graph those values
separately. The model is based on data from a two-group RCT evaluating an
intervention over 3 time points. Here is a sample model with a dichotomous
outcome and fixed effects of time (continuous), condition (2 levels) and
whether an additional workshop was completed (2 levels).
workshop<-glmer(CurrentAsst~Time*Condition + Workshop_completion_reordered
+
(1|id), data = vse, family = binomial, nAGQ = 25, control =
glmerControl(optimizer = "bobyqa"))
Thank you,
Igor Yakovenko
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