longitudinal analysis using lmer?
Andrew Gelman at least seems to glory in the ability of random effects models to cope with cases having meagre data. See the discussion in the Gelman/Hell book on the Radon data and Lac Qui Parle County. Murray Jorgensen
Emmanuel Charpentier wrote:
Le lundi 31 ao?t 2009 ? 12:53 -0400, Robert Terwilliger a ?crit :
Thanks everyone for all the advice. One question I have (maybe there will be more...... :-P ): Should I exclude subjects that have only 1 or 2 data points?
The question you shuld try to answer is "*Why* do they have only 1 or 2 points ?". HTH, Emmanuel Charpentier
On Fri, Aug 28, 2009 at 7:07 PM, Ken Beath<ken-PJqznCQlsrTvnOemgxGiVw at public.gmane.org> wrote:
On 29/08/2009, at 6:07 AM, Robert Terwilliger wrote:
One more thing........ What i sent was only a small sample of the data, just for the purpose of showing what kind of set we have. We have about 150 subjects, with starting ages between 8 and 21, with 3-5 data points (yearly visits) per subject.
This will be fine, although it isn't as good as having a smaller number of complete series. One point is that they don't look completely linear, so a polynomial (maybe quadratic) or regression spline may be a better option. Judging by the scatter the random effect variance will probably be close to zero. Ken
Thanks, -- Robert Terwilliger Physicist Laboratory of Neurocognitive Development Western Psychiatric Institute and Clinic University of Pittsburgh Medical Center Loeffler Building 121 Meyran Avenue #114 Pittsburgh, PA 15213 412.383.8174 - Office 412.383.8179 - Fax em: raterwil at gmail.com http://www.wpic.pitt.edu/research/lncd/ ******************************************************* Dear R mixed effects gurus, I have the following data below. Attached is a png graphic representing the data. I would like to run the following analysis: signal ~ age | subject. For your information (not statically relevant), the "signal" variable is from a functional MRI experiment. At issue is whether this analysis is valid using "lme". From the graph (and the table below), one can see that there are five subjects. However, each subject begins at a different age. Subject 1 begins at 8 and goes to 12, while subject 5 begins at 14. From my study of longitudinal analysis, usually each subject begins at the same starting point, while these data have subjects beginning at different starting points (different ages). Any insight is appreciated. subject age signal 1 8 0.108 1 9 0.139 1 10 NA 1 11 0.151 1 12 0.148 2 10 0.127 2 11 NA 2 12 0.135 2 13 0.146 3 9 0.105 3 10 0.123 3 11 0.134 3 12 0.151 3 13 0.145 4 12 0.130 4 13 0.169 4 14 0.146 4 15 0.174 5 14 0.158 5 15 0.141 5 16 0.178 5 17 NA 5 18 0.172
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Dr Murray Jorgensen http://www.stats.waikato.ac.nz/Staff/maj.html Department of Statistics, University of Waikato, Hamilton, New Zealand Email: maj at waikato.ac.nz Fax 7 838 4155 Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 0200 8350