linear mixed model using lmer
I think the best analysis of this data is: library(lattice) dotplot(yield ~ batch, daty) bwplot(yield ~ batch, daty) There is no detectable difference between batches. But, if you insist, try removing the overall intercept. m1 <- lmer(yield~0+(1|batch),daty) coef(m1) summary(m1) VarCorr(m1) On Fri, Mar 4, 2022 at 6:44 PM array chip via R-help
<r-help at r-project.org> wrote:
Dear all, I have this simple dataset to measure the yeild of a crop collected in 2 batches (attached). when I ran a simple inear mixed model using lmer to estimate within-batch and between-batch variability, the between-batch variability is 0. The run showed that data is singular. Does anyone know why the data is singular and what's the reason for 0 variability? is it because the dataset only has 2 batches?
daty<-read.table("datx.txt",sep='\t',header=T,row.names=NULL)
library(lme4)> lmer(yield~1+(1|batch),daty)
boundary (singular) fit: see ?isSingular
Linear mixed model fit by REML ['lmerMod']
Formula: yield ~ 1 + (1 | batch)
Data: daty
REML criterion at convergence: 115.6358
Random effects:
Groups Name Std.Dev.
batch (Intercept) 0.000
Residual 2.789
Number of obs: 24, groups: batch, 2
Fixed Effects:
(Intercept)
5.788
Thanks!
John______________________________________________
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Kevin Wright