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.cross at 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 at 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.