Hi Johan, it's not clear to me that we have enough information to answer your question reliably, but the following thoughts might be useful to you.
1) in your lme() model, all the coefficients are closer to zero than in your lm() model. 2) the change in Z is not very large relative to its standard error under either model. 3) one possible explanation for the change is that the contributions of individual observations to the parameter estimates are weighted differently under lme() than under lm(), and this can be noticeable when the data are unbalanced. Specifically, there might be a large group of individuals who are influencing the parameter estimates strongly in lm(), but who are all from the same family. As individuals, their contribution will lessen in lme(). If your data are unbalanced, then I would look here for the effect. 4) there might be other explanations for the change. I hope that this helps, Andrew On Sat, Nov 10, 2007 at 12:58:21AM -0700, Johan Jackson wrote: > Any help would be most appreciated. (Don't make me get down on my > hands and knees and beg for help, cause I'll do it!!) My boss has me > learning R and doing nested regression with the report due Mon (Friday > night statistics...fun. ). Anyway, here's my problem: > > In a regression equation not accounting for the fact that people are > nested in families, the result for Z variable is VERY strong (beta = > -4511), but this result is much weaker when I use lme and account for > people nested in families (beta = -2613). I'm struggling with a verbal > interpretation of this result. Is it because the effect of Z within > families is not very strong, but between family variation is high? > > > > > ----------------------------------------------------------------- > > If my R syntax would be helpful, it's below: > > BIG EFFECT OF Z (using lm) > summary(lm(Y ~ X + Z + age ,data=pharma.data ,na.action='na.omit')) > > Estimate Std. Error t value Pr(>|t|) > (Intercept) 102.27 24.98 4.09 7e-05 *** > X -629.76 267.87 -2.35 0.020 * > Z -4511.96 2032.39 -2.22 0.028 * > age 1.88 1.42 1.32 0.188 > > > SMALLER EFFECT OF Z (using lme) > summary(lme(Y ~ X + Z + age, random = ~1|Family.ID, > data=pharma.data, method="ML", na.action='na.omit')) > > Value Std.Error DF t-value p-value > (Intercept) 103.9 20.0 85 5.200 0.0000 > X. -417.3 179.3 85 -2.327 0.0223 > Z -2613.0 1845.1 85 -1.416 0.1604 > age 1.3 1.2 85 1.126 0.2632 > > ______________________________________________ > R-help@r-project.org mailing list > 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. -- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-9763 University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 http://www.ms.unimelb.edu.au/~andrewpr http://blogs.mbs.edu/fishing-in-the-bay/ ______________________________________________ R-help@r-project.org mailing list 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.
