Why am I getting a Variance of 0 for my random effect
On Wed, Aug 11, 2010 at 1:13 PM, Kevin E. Thorpe
<kevin.thorpe at utoronto.ca> wrote:
Hello.
I'm getting a variance of 0 on a random effect and I don't know why.
It's not a bug - it's a feature. ML estimates or REML estimates of variance components can be zero. This simply indicates that the variability in the response associated with the factor, RN in your case, is not sufficient to warrant the additional complexity in the model.
I suspect I've not set the model up correctly. ?My transcript is below with my own comments sprinkled in for time to time. A little bit about the data (which I will provide off-list if requested). ?We have nurses managing an aspect of patient care according to different algorithms. ?Interest focuses on of the algorithms result in different outcomes. ?I have restricted this to only nurses who did each algorithm twice (in case my problem was being caused by some nurses doing only one algorithm, possibly only one time). I figured that since I have multiple observations per nurse, I should treat nurse as a random effect, but maybe I confused myself again.
You are quite correct that it is appropriate to allow for the possibility of RN having an effect on the response and that it should be incorporated as a random effect if it were in the model but the results indicate that RN does not have sufficient effect on the response.
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library(lattice) library(lme4)
str(data1)
'data.frame': ? 72 obs. of ?3 variables: ?$ RN ? ? ? ?: int ?1 1 2 3 7 7 9 9 15 15 ... ?$ Assignment: Factor w/ 2 levels "E","N": 1 1 1 1 1 1 1 1 1 1 ... ?$ AUChr ? ? : num ?12.26 7.23 9.26 4.04 10.31 ...
tmp1 <- with(data1,aggregate(AUChr,list(RN=RN,Assigment=Assignment),mean)) names(tmp1)[3] <- "Mean" tmp2 <- with(data1,aggregate(AUChr,list(RN=RN,Assignment=Assignment),var)) names(tmp2)[3] <- "Variance" meanvar <- merge(tmp1,tmp2)
The point of this is to show that the means are not all the same, nor are the variances.
meanvar
? RN Assignment ? Mean ?Variance 1 ? 1 ? ? ? ? ?E ?9.745 ?12.65045 2 ? 1 ? ? ? ? ?N ?7.185 ? 1.36125 3 ?15 ? ? ? ? ?E 10.605 ?15.07005 4 ?15 ? ? ? ? ?N 10.385 ? 4.41045 5 ?16 ? ? ? ? ?E ?8.175 ? 0.00845 6 ?16 ? ? ? ? ?N ?8.420 ? 1.03680 7 ? 2 ? ? ? ? ?E ?7.300 ? 7.68320 8 ? 2 ? ? ? ? ?N ?6.950 ? 1.00820 9 ?21 ? ? ? ? ?E ?9.670 ? 9.41780 10 21 ? ? ? ? ?N 10.535 ? 2.44205 11 22 ? ? ? ? ?E ?7.720 ? 2.04020 12 22 ? ? ? ? ?N ?7.930 ? 1.21680 13 24 ? ? ? ? ?E ?9.555 ?10.35125 14 24 ? ? ? ? ?N ?9.330 ? 0.38720 15 25 ? ? ? ? ?E ?8.240 ? 0.92480 16 25 ? ? ? ? ?N ?9.485 ? 0.00125 17 27 ? ? ? ? ?E ?8.635 ? 0.08405 18 27 ? ? ? ? ?N ?7.745 ? 3.72645 19 28 ? ? ? ? ?E ?9.635 ? 8.61125 20 28 ? ? ? ? ?N ?8.315 ?10.35125 21 ?3 ? ? ? ? ?E ?6.005 ? 7.72245 22 ?3 ? ? ? ? ?N 11.435 ?55.44045 23 31 ? ? ? ? ?E ?9.590 ? 9.94580 24 31 ? ? ? ? ?N 10.570 ?16.70420 25 35 ? ? ? ? ?E ?9.055 ? 0.32805 26 35 ? ? ? ? ?N ?9.925 ?14.41845 27 36 ? ? ? ? ?E ?9.040 ? 2.08080 28 36 ? ? ? ? ?N ?7.395 ? 1.14005 29 ?5 ? ? ? ? ?E ?8.430 ? 3.38000 30 ?5 ? ? ? ? ?N 17.385 139.94645 31 ?6 ? ? ? ? ?E ?6.930 ? 0.24500 32 ?6 ? ? ? ? ?N ?8.330 ? 1.72980 33 ?7 ? ? ? ? ?E 10.650 ? 0.23120 34 ?7 ? ? ? ? ?N ?7.375 ? 0.09245 35 ?9 ? ? ? ? ?E ?8.885 ? 7.56605 36 ?9 ? ? ? ? ?N ?8.405 ? 0.73205 Model with "Assignment" (algorithm).
lmer(AUChr~Assignment+(1|RN),data=data1,REML=FALSE)
Linear mixed model fit by maximum likelihood Formula: AUChr ~ Assignment + (1 | RN) ? Data: data1 ? AIC ? BIC logLik deviance REMLdev ?365.7 374.8 -178.8 ? ?357.7 ? 356.9 Random effects: ?Groups ? Name ? ? ? ?Variance Std.Dev. ?RN ? ? ? (Intercept) 0.0000 ? 0.0000 ?Residual ? ? ? ? ? ? 8.4152 ? 2.9009 Number of obs: 72, groups: RN, 18 Fixed effects: ? ? ? ? ? ?Estimate Std. Error t value (Intercept) ? 8.7703 ? ? 0.4835 ? 18.14 AssignmentN ? 0.5131 ? ? 0.6837 ? ?0.75 Correlation of Fixed Effects: ? ? ? ? ? ?(Intr) AssignmentN -0.707 Model without the algorithm variable.
lmer(AUChr~(1|RN),data=data1,REML=FALSE)
Linear mixed model fit by maximum likelihood Formula: AUChr ~ (1 | RN) ? Data: data1 ? AIC ? BIC logLik deviance REMLdev ?364.3 371.1 -179.1 ? ?358.3 ? 358.5 Random effects: ?Groups ? Name ? ? ? ?Variance Std.Dev. ?RN ? ? ? (Intercept) 0.000 ? ?0.0000 ?Residual ? ? ? ? ? ? 8.481 ? ?2.9122 Number of obs: 72, groups: RN, 18 Fixed effects: ? ? ? ? ? ?Estimate Std. Error t value (Intercept) ? 9.0268 ? ? 0.3432 ? ?26.3
sessionInfo()
R version 2.11.1 Patched (2010-07-21 r52598) Platform: i686-pc-linux-gnu (32-bit) locale: ?[1] LC_CTYPE=en_US ? ? ? LC_NUMERIC=C ? ? ? ? LC_TIME=en_US ?[4] LC_COLLATE=C ? ? ? ? LC_MONETARY=C ? ? ? ?LC_MESSAGES=en_US ?[7] LC_PAPER=en_US ? ? ? LC_NAME=C ? ? ? ? ? ?LC_ADDRESS=C [10] LC_TELEPHONE=C ? ? ? LC_MEASUREMENT=en_US LC_IDENTIFICATION=C attached base packages: [1] stats ? ? graphics ?grDevices utils ? ? datasets ?methods ? base other attached packages: [1] lme4_0.999375-34 ? Matrix_0.999375-42 lattice_0.18-8 loaded via a namespace (and not attached): [1] grid_2.11.1 ? nlme_3.1-96 ? stats4_2.11.1
proc.time()
? user ?system elapsed ?3.488 ? 0.056 ? 3.536 -- Kevin E. Thorpe Biostatistician/Trialist, Knowledge Translation Program Assistant Professor, Dalla Lana School of Public Health University of Toronto email: kevin.thorpe at utoronto.ca ?Tel: 416.864.5776 ?Fax: 416.864.3016
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