With lme of the nlme package you can specify a correlation argument to account for longitudinal correlation. Try a CAR1 correlation. Tim Timothy G. Gregoire J. P. Weyerhaeuser Professor of Forest Management School of Forestry & Environmental Studies Yale University 360 Prospect St, New Haven, CT, U.S.A. 06511 Ph: 1.203.432.9398 mob: 1.203.508.4014? fax:1.203.432.3809 -----Original Message----- From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of r-sig-mixed-models-request at r-project.org Sent: Sunday, July 31, 2016 6:00 AM To: r-sig-mixed-models at r-project.org Subject: R-sig-mixed-models Digest, Vol 115, Issue 39 Send R-sig-mixed-models mailing list submissions to r-sig-mixed-models at r-project.org To subscribe or unsubscribe via the World Wide Web, visit https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_r-2Dsig-2Dmixed-2Dmodels&d=CwICAg&c=-dg2m7zWuuDZ0MUcV7Sdqw&r=atRKEKX5W2zm-GsIgYzLo4oYM9D-Qn-eMFObHZtnEnI&m=0bsxPbgQhfwtIO1cgoQfJt24FzLV6A7sHDNIpLCPdZY&s=1hHtoI5s_g99seFW7TN4ouNfHYBmeuLcge-Zu332o24&e= or, via email, send a message with subject or body 'help' to r-sig-mixed-models-request at r-project.org You can reach the person managing the list at r-sig-mixed-models-owner at r-project.org When replying, please edit your Subject line so it is more specific than "Re: Contents of R-sig-mixed-models digest..." Today's Topics: 1. Re: lme4 random effects for repeated measures, unbalanced data (Ben Bolker) ---------------------------------------------------------------------- Message: 1 Date: Sat, 30 Jul 2016 11:16:07 -0400 From: Ben Bolker <bbolker at gmail.com> To: r-sig-mixed-models at r-project.org Subject: Re: [R-sig-ME] lme4 random effects for repeated measures, unbalanced data Message-ID: <de3be868-c64c-052c-00db-436e3abd5708 at gmail.com> Content-Type: text/plain; charset=windows-1252
On 16-07-19 07:28 PM, Ann Marie Raymondi wrote:
Hello Listers, I have a vegetation data set that consists of 150 plots that were sampled 1-3 times over a three year period. Plots are my unit of observation and they are unbalanced (since plots were sampled either once, twice, or three times). I would like to use mixed-effects models in order to account for variation in both plots and sampling year and to keep my sample size large (instead of conducting my analysis within individual years). Here is an example of the grouping of my data: Plot_ID Plot Year 2012_101 101 2012 2013_101 101 2013 2014_101 101 2014 2012_201 201 2012 2013_201 201 2013 2013_301 301 2013 My response variables are cover of vegetation functional groups and predictors include variables related to fire and treatment history. Additionally, I am not interested in how plots change over time per se, but rather, in aggregating sampling from all three years to increase my sample size and to account for the spatial/temporal correlation that arises from doing so. It is my assumption that treating plot as a random effect (intercept only) accounts for variation that arises from potential spatial autocorrelation, but my main question is how to account for the repeated measures and if I need to account for the grouping of cells within sampling years: Potential model: model<-lmer(response~covariates + (1|Plot) + (1|Year). However, I know that is not appropriate to use a random effect with only three levels, year in this case. I'm hoping for recommendations on how to incorporate year as a random effect. Is including (Year | Plot) recommended? And if so, how might I interpret that effect, i.e., is it accounting for variation introduced by different sampling year or variation in plots over sampling year?
Thank you in advance for any help/suggestions!
Include Year as a fixed effect. You could try to include (Year|Plot), but it will overlap with the residual error (since each plot is measured once per year), so it probably won't work (without some more fussing around). You'll probably get most of the signal by including Year as a fixed effect. ------------------------------ Subject: Digest Footer _______________________________________________ R-sig-mixed-models mailing list R-sig-mixed-models at r-project.org https://urldefense.proofpoint.com/v2/url?u=https-3A__stat.ethz.ch_mailman_listinfo_r-2Dsig-2Dmixed-2Dmodels&d=CwICAg&c=-dg2m7zWuuDZ0MUcV7Sdqw&r=atRKEKX5W2zm-GsIgYzLo4oYM9D-Qn-eMFObHZtnEnI&m=0bsxPbgQhfwtIO1cgoQfJt24FzLV6A7sHDNIpLCPdZY&s=1hHtoI5s_g99seFW7TN4ouNfHYBmeuLcge-Zu332o24&e= ------------------------------ End of R-sig-mixed-models Digest, Vol 115, Issue 39