Uri Eduardo RamÃrez Pasos
2019-Apr-12 16:28 UTC
[R] Syntax differences between aov and lmer for 2-way repeated measures design using a mixed model
Hi everyone, I'm working with the following data frame using R. It consists of measurements obtained from 7 subjects with two independent variables (IV1 and IV2) with two levels each (OFF/ON, ALT/ISO, respectively):>myDataSubject DV IV1 IV2 1 2.567839 OFF ALT 1 58.708027 ON ALT 1 44.504265 OFF ISO 1 109.555701 ON ISO 2 99.043735 OFF ALT 2 75.958737 ON ALT 2 182.727396 OFF ISO 2 364.725795 ON ISO 3 45.788988 OFF ALT 3 52.941263 ON ALT 3 54.719013 OFF ISO 3 41.909909 ON ISO 4 116.145279 OFF ALT 4 162.927971 ON ALT 4 34.162077 OFF ISO 4 74.029748 ON ISO 5 114.412913 OFF ALT 5 121.127983 ON ALT 5 192.379708 OFF ISO 5 229.192453 ON ISO 6 213.421076 OFF ALT 6 526.739206 ON ALT 6 150.596812 OFF ISO 6 217.931951 ON ISO 7 117.931273 OFF ALT 7 102.467813 ON ALT 7 57.823062 OFF ISO 7 85.181033 ON ISO (1) Is this a repeated measures (RM) design? Some folks have mentioned that it is not since it isn't a longitudinal study, but I thought that as long as there are measurements from each experimental unit for every single level of a factor, one can say this as a RM design. What is correct? Also, is an RM design synonymous with having a within-subject factor? (2) I'm interested in both the main and the interaction effects of IV1 and IV2, but due to having measurements from each subject for all level combinations, I think I have to include Subject as a random effect. I have looked at aov and lmer but I'm confused about the difference in syntax: This cheat sheet recommends: m1<-aov(DV ~ IV1*IV2 + Error(Subject/(IV1*IV2)), myData) However it's not clear to me whether Error(x/(y*z)) means x is a random effect and y and z are nested in x. Is this interpretation correct? If so, would m1 be inappropriate for my data since my data isn't nested, but fully crossed? And if so, would m2<-aov(DV ~ IV1*IV2 + Error(Subject), myData) be the correct syntax? I have also been told that in m2 the Error term should be dropped - is this correct? (3) In a previous question I was told the linear mixed effects model m3<-lmer(DV ~ IV1*IV2 + (1|Subject), myData) was appropriate more my data. Just to better understand lmer syntax: if I had n subjects and for each subject measurements were obtained for both levels of IV2 but half of the subjects were OFF and the other half ON, would the model be m4<-lmer(DV ~ IV1*IV2 +(1|Subject/IV1), data=myData) ? And if there was only one measurement per IV1*IV2 combination, would that mean this is no longer a repeated-measures design and therefore the model is just m5<-lmer(DV ~ IV1*IV2, data=myData) ? In which case lm would probably suffice. Any help would be greatly appreciated, Uri Ramirez [[alternative HTML version deleted]]
Bert Gunter
2019-Apr-12 19:50 UTC
[R] Syntax differences between aov and lmer for 2-way repeated measures design using a mixed model
You should talk with your professor. This list is about R programming. Essentially statistical issues, which this appears mostly to be, are generally off topic. Questions about mixed effects models -- RM and longitudinal designs are typically analysed as such -- and especially using the nlme and/or lme4 packages are usually better posted on the r-sig-mixed-models list. ... and if this is homework, this list has a no homework poilicy. Cheers, Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Fri, Apr 12, 2019 at 11:08 AM Uri Eduardo Ram?rez Pasos < urieduardo at gmail.com> wrote:> Hi everyone, > > I'm working with the following data frame using R. It consists of > measurements obtained from 7 subjects with two independent variables (IV1 > and IV2) with two levels each (OFF/ON, ALT/ISO, respectively): > > >myData > Subject DV IV1 IV2 > 1 2.567839 OFF ALT > 1 58.708027 ON ALT > 1 44.504265 OFF ISO > 1 109.555701 ON ISO > 2 99.043735 OFF ALT > 2 75.958737 ON ALT > 2 182.727396 OFF ISO > 2 364.725795 ON ISO > 3 45.788988 OFF ALT > 3 52.941263 ON ALT > 3 54.719013 OFF ISO > 3 41.909909 ON ISO > 4 116.145279 OFF ALT > 4 162.927971 ON ALT > 4 34.162077 OFF ISO > 4 74.029748 ON ISO > 5 114.412913 OFF ALT > 5 121.127983 ON ALT > 5 192.379708 OFF ISO > 5 229.192453 ON ISO > 6 213.421076 OFF ALT > 6 526.739206 ON ALT > 6 150.596812 OFF ISO > 6 217.931951 ON ISO > 7 117.931273 OFF ALT > 7 102.467813 ON ALT > 7 57.823062 OFF ISO > 7 85.181033 ON ISO > (1) Is this a repeated measures (RM) design? Some folks have mentioned that > it is not since it isn't a longitudinal study, but I thought that as long > as there are measurements from each experimental unit for every single > level of a factor, one can say this as a RM design. What is correct? Also, > is an RM design synonymous with having a within-subject factor? > > (2) I'm interested in both the main and the interaction effects of IV1 and > IV2, but due to having measurements from each subject for all level > combinations, I think I have to include Subject as a random effect. I have > looked at aov and lmer but I'm confused about the difference in syntax: > This cheat sheet recommends: > > m1<-aov(DV ~ IV1*IV2 + Error(Subject/(IV1*IV2)), myData) > > However it's not clear to me whether Error(x/(y*z)) means x is a random > effect and y and z are nested in x. Is this interpretation correct? If so, > would m1 be inappropriate for my data since my data isn't nested, but fully > crossed? And if so, would > > m2<-aov(DV ~ IV1*IV2 + Error(Subject), myData) > > be the correct syntax? I have also been told that in m2 the Error term > should be dropped - is this correct? > > (3) In a previous question I was told the linear mixed effects model > > m3<-lmer(DV ~ IV1*IV2 + (1|Subject), myData) > was appropriate more my data. Just to better understand lmer syntax: if I > had n subjects and for each subject measurements were obtained for both > levels of IV2 but half of the subjects were OFF and the other half ON, > would the model be > > m4<-lmer(DV ~ IV1*IV2 +(1|Subject/IV1), data=myData) ? > > And if there was only one measurement per IV1*IV2 combination, would that > mean this is no longer a repeated-measures design and therefore the model > is just > > m5<-lmer(DV ~ IV1*IV2, data=myData) ? In which case lm would probably > suffice. > > Any help would be greatly appreciated, > Uri Ramirez > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]