Hi R Users,
When I use package lme4 for mixed model analysis, I can't distinguish
the significant and insignificant variables from all random independent
variables.
Here is my data and result:
Data:
Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9),
Variety=rep(rep(c("A1","A2","A3"),each=3),3),
Stand=rep(c("B1","B2","B3"),9),
Block=rep(1:3,each=9))
Rice.lmer<-lmer(Yield ~ (1|Variety) + (1|Stand) + (1|Block) +
(1|Variety:Stand), data = Rice)
Result:
Linear mixed model fit by REML
Formula: Yield ~ (1 | Variety) + (1 | Stand) + (1 | Block) + (1 |
Variety:Stand)
Data: Rice
AIC BIC logLik deviance REMLdev
96.25 104.0 -42.12 85.33 84.25
Random effects:
Groups Name Variance Std.Dev.
Variety:Stand (Intercept) 1.345679 1.16003
Block (Intercept) 0.000000 0.00000
Stand (Intercept) 0.888889 0.94281
Variety (Intercept) 0.024691 0.15714
Residual 0.666667 0.81650
Number of obs: 27, groups: Variety:Stand, 9; Block, 3; Stand, 3; Variety, 3
Fixed effects:
Estimate Std. Error t value
(Intercept) 7.1852 0.6919 10.38
Can you give me some advice for recognizing the significant variables among
random effects above without other calculating.
Any suggestions will be appreciated.
Wenjun
[[alternative HTML version deleted]]
______________________________________________
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.
