[R-sig-ME] Why am I getting a Variance of 0 for my random effect
John Maindonald
john.maindonald at anu.edu.au
Thu Aug 12 01:55:33 CEST 2010
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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