search for: grpmean

...> ave(tmp$runtime)[1] > [1] 41.8108 I would have guessed you would get an error, but maybe if ave() is given no grouping factor it just returns a grand mean. Try instead one of these: aggregate(data2, data2$function. , FUN=mean) tapply(data2$runtime, data2$function. , FUN=mean) data2$grpmean <- ave( data2$runtime, data2$function. , FUN=mean) The last one adds a column in the dataframe and could be useful for identifying items that are some particular diastance away from thier group mean. -- David Winsemius, MD West Hartford, CT David Winsemius, MD West Hartford, CT

...t to lose the binding to the other factors (FAC2, FAC3). > Or can i only control it via order preserving - it seems unsave. > > Something like that would be nice: > "by(d[1], FAC1) <- by(d[1], FAC1, scale)" Hey, finely this works (adapted from Prof. Ripleys suggestion): grpmeans <- tapply(d$VALUE, d$FAC1, mean, na.rm=T) d$c <- d$VALUE - grpmeans[d$FAC1] grpscale <- tapply(d$c, d$FAC1, sd, na.rm=T) d$cs <- d$c / grpscale[d$FAC1] It was funny to get 3 eligible answers but non of them worked at first go. 1) grpmeans <- tapply(d[1], FAC1, mean) d$s <...

Hey, hopefully there is an easy way to solve my problem. All that i think off is lengthy and clumsy. Given a data.frame d with columns VALUE, FAC1, FAC2, FAC3. Let FAC1 be something like experiment number, so that there are exactly the same number of rows for each level of FAC1 in the data.frame. Now i would like to scale all values according to the center of its experiment. So i can apply s