Example: I have the following model > model <- lmer(response ~ time * trt * bio + (time|id), data = dat)
where time = time of observation trt = treatment group (0-no treatment / 1-treated) bio = biological factor (0-absent / 1-present) and I would like to obtain an estimate (with standard error) of the change in response over time for individuals in the treatment group with the biological factor. The estimate is easy, > sum(fixef(model)[c(2,5,6,8)]) # ie time + time:trt + time:bio + time:trt:bio but the standard error is a hassle to calculate by hand. Is there some better way to do this? In SAS for example there is an `estimate' option (see sample code below) that will calculate the estimate, SE, df, t statistic, etc... Is there some R equivalent? Thanks, Randy proc mixed data=dat; class id; model response = time + trt + bio + time*trt + time*bio + trt*bio + time*trt*bio; random time; estimate "est1" intercept 0 time 1 trt 0 bio 0 time*trt 1 time*bio 1 trt*bio 0 time*trt*bio 1; /* or something like that */ run; ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Randy Johnson Laboratory of Genomic Diversity NCI-Frederick Bldg 560, Rm 11-85 Frederick, MD 21702 (301)846-1304 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ______________________________________________ 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