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
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