linear mixed model using lmer
Well, it is true that when there are only two levels, t-test and F-tests should give identical inferences, I don?t think that?s the problem in this case. Instead, remember that we can compute variance components from an anova table, if we write out the expected mean squares and solve algebraically. When I run aov on these data, I get
summary(aov(yield ~ batch,data=daty))
Df Sum Sq Mean Sq F value Pr(>F) batch 1 1.03 1.035 0.128 0.724 Residuals 22 177.91 8.087 This would imply a negative variance component estimate for batch, since batch MS is smaller than residual MS. Most mixed model packages constrain variance estimates to be non-negative, so you get 0 from lmer. It doesn?t help that you only have two levels for batch. I find negative variance estimates happen more frequently when there are few levels of the effect to be estimated. Cheers, Peter C
On Mar 15, 2022, at 12:12 PM, Jixiang Wu <jixiangwu05 at gmail.com> wrote: There is no difference when running anova or t-test. So you shouldn't expect positive variance between batches. On Fri, Mar 4, 2022 at 7:06 PM array chip via R-help <r-help at r-project.org> wrote:
Thanks Jeff for reminding me that the attachment is removed. I put it in my google drive if anyone wants to test the data ( https://drive.google.com/file/d/1lgVZVLHeecp9a_sFxEPeg6353O-qXZhM/view?usp=sharing ) I'll try the mixed model mailing list as well. John On Friday, March 4, 2022, 04:56:20 PM PST, Jeff Newmiller < jdnewmil at dcn.davis.ca.us> wrote: a) There is a mailing list for that:
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models b) Read the Posting Guide, as most attachment types are removed to avoid propagating worms/viruses. (None seen upon receipt of this email.) On March 4, 2022 4:41:57 PM PST, 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 -- Sent from my phone. Please excuse my brevity. [[alternative HTML version deleted]] ______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
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______________________________________________ R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.