If I remember correctly, coef(m1) would do it ... but it has been a while since I last used lmer, and I am working only from memory.
Cheers David Cross d.cr...@tcu.edu www.davidcross.us On May 18, 2011, at 6:29 PM, Stephen Peterson wrote: > Hello, > I am looking for some help on how I may be able to view estimated > values for 3 response variables with 1 fixed and 1 random effect using > lmer. > My data is proportional cover of three habitat variables (bare ground, > grass cover, shrub cover) that was collected during 3 years (1976, > 2000, 2010) on 5 study plots, each plot divided into 50 m square > cells. > Portion of dataset (proportions were log transformed) > year plot cell_id bare_trans grass_trans shrub_trans > 0 wh whi1 -0.678240631 -0.892213913 -0.158328393 > 0 wh whi2 -0.774640426 -0.745665597 -0.164722747 > 0 wh whi3 -0.600670894 -0.545056465 -0.30835479 > 0 wh whi4 -0.461018617 -0.704273962 -0.315083353 > 0 wh whi5 -0.518221954 -0.643432282 -0.303575808 > 0 wh whi6 -0.598043065 -0.588487184 -0.286051968 > 0 wh whi7 -0.581336622 -0.356760604 -0.4880035 > 0 wh whj1 -0.650114241 -0.706560469 -0.215255255 > > I am treating the group of response variables (bare_trans, > grass_trans, shrub_trans) as one multivariate response. > The year (0, 1, 2) is my fixed effect and cell_id (whi1 . . .) is my > random effect. > > My model is: > m1 <- lmer(cbind(bare_trans,grass_trans,shrub_trans) ~ year + > (1|cell_id),data=whdata) > > Summary output is: > Linear mixed model fit by REML > Formula: cbind(bare_trans, grass_trans, shrub_trans) ~ year + (1 | cell_id) > Data: whdata > AIC BIC logLik deviance REMLdev > -97.86 -88.14 52.93 -119.1 -105.9 > Random effects: > Groups Name Variance Std.Dev. > cell_id (Intercept) 0.000000 0.00000 > Residual 0.014523 0.12051 > Number of obs: 84, groups: cell_id, 28 > > Fixed effects: > Estimate Std. Error t value > (Intercept) -0.53781 0.02079 -25.87 > year 0.24182 0.01610 15.02 > > Correlation of Fixed Effects: > (Intr) > year -0.775 > > What is missing from this output that I need are estimated > coefficients of the 3 response variables (bare_trans, grass_trans, > shrub_trans) for each year (0, 1, 2), standard errors and p-values. > > Any idea if lmer even generates these estimates? And if so, is there a > way of digging them out of the R blackbox? > If not, if anyone has suggestions on a more appropriate package to use > that would be great. > I essentially want to perform a MANOVA on my 3 response variables > while accounting for fixed and random effects. > > Any help would be appreciated. > > Thank you, > Stephen L. Peterson > Utah State University > > ______________________________________________ > 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. ______________________________________________ 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.
