> On 11 Feb 2015, at 16:57 , anord <andreas.n...@biol.lu.se> wrote: > > Dear R users, > We are working on a data set in which we have measured repeatedly a > physiological response variable (y) > every 20 min for 12 h (time variable; 'x') in subjects ('id') beloning to > one of five groups ('group'; 'A' to 'E'). Data are located at: > https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0 > > We are interested to model if the response in y differences with time (i.e. > 'x') for the two groups. Thus: > require(nlme) > m1<-lme(y~group*x+group*I(x^2),random=~x|id,data=data.df,na.action=na.omit) > > But because data are collected repeatedly over short time intervals for each > subject, it seemed prudent to consider an autoregressive covariance > structure. Thus: > m2<-update(m1,~.,corr=corCAR1(form=~x|id)) > > AIC values indicate the latter (i.e. m2) as more appropriate: > anova(m1,m2) > # Model df AIC BIC logLik Test L.Ratio > p-value > #m1 1 19 2155.996 2260.767 -1058.9981 > #m2 2 20 2021.944 2132.229 -990.9718 1 vs 2 136.0525 <.0001 > > Fixed effects and test statistics differ between models. A look at marginal > ANOVA tables suggest inference might differ somewhat between models: > > anova.lme(m1,type="m") > # numDF denDF F-value p-value > #(Intercept) 1 1789 63384.80 <.0001 > #group 4 45 1.29 0.2893 > #x 1 1789 0.05 0.8226 > #I(x^2) 1 1789 4.02 0.0451 > #group:x 4 1789 2.61 0.0341 > #group:I(x^2) 4 1789 4.37 0.0016 > > anova.lme(m2,type="m") > # numDF denDF F-value p-value > #(Intercept) 1 1789 59395.79 <.0001 > #group 4 45 1.33 0.2725 > #x 1 1789 0.04 0.8379 > #I(x^2) 1 1789 2.28 0.1312 > #group:x 4 1789 2.09 0.0802 > #group:I(x^2) 4 1789 2.81 0.0244 > > Now, this is all well. But: my colleagues have been running the same data > set using PROC MIXED in SAS and come up with substantially different results > when comparing SAS default covariance structure (variance components) and > AR1. Specifically, there is virtually no change in either test statistics or > fitted values when using AR1 instead of Variance Components in SAS, which > fits the observation that AIC values (in SAS) indicate both covariance > structures fit data equally well. > > This is not very satisfactory to me, and I would be interesting to know what > is happening here. Realizing > this might not be the correct forum for this question, I would like to ask > you all if anyone would have any > input as to what is going on here, e.g. am I setting up my model > erroneously, etc.? > > N.b. I have no desire to replicate SAS results, but I would most certainly > be interested to know what could possibly explain such a large discrepancy > between the two platforms. Any suggestions greatly welcomed. > > (Data are located at: > https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0) >
Hmm, does SAS include a nugget effect perchance? At any rate, showing the SAS output (or some of it) might make it easier for someone to help. Also, R-sig-ME is a better choice for discussions of mixed effects models. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ R-help@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.