Also, plot time 2 versus time 1, broken down by assignment. John Maindonald email: john.maindonald at anu.edu.au phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre for Mathematics & Its Applications, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200. http://www.maths.anu.edu.au/~johnm
On 12/08/2010, at 9:29 AM, John Maindonald wrote:
Surely you do want to treat nurses as a random effect, by analysing summary data at the nurse level, if not in a multi-level model. The zero variance may (if it really would prefer to be negative) be telling you that there is a systematic difference between the two times, for which your model needs to account. Maybe there is a learning effect -- 2nd time is systematically different from the first. Did your model account for such an effect? Or (requires more thought to model), those who do badly the first time may learn rather more from their experience than those who did moderately well, doing better than average next time? It appears that the data have the information needed to get insight on these questions. The most insightful approach might well be separate regressions for 2-1 differences and 2+1 averages. I'd do those analyses whatever else you do. John Maindonald email: john.maindonald at anu.edu.au phone : +61 2 (6125)3473 fax : +61 2(6125)5549 Centre for Mathematics & Its Applications, Room 1194, John Dedman Mathematical Sciences Building (Building 27) Australian National University, Canberra ACT 0200. http://www.maths.anu.edu.au/~johnm On 12/08/2010, at 4:24 AM, Kevin E. Thorpe wrote:
On 08/11/2010 02:16 PM, Daniel Ezra Johnson wrote:
Try data1$RN<- as.factor(data1$RN).
Thanks, but that has no effect. That is I get the same results.
On Wed, Aug 11, 2010 at 2:13 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. 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. R version 2.11.1 Patched (2010-07-21 r52598) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R.
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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