[R-sig-ME] lmer(): Higher weight in case of more measurements

[Ken Beath]

lmer will sort out by itself the fact that some groups are larger than the
others. weights is used when an individual point is the summary of a number
of observations.

When generating data sets to test things 2 groups isn't enough, use a
similar number that would be used in the analysis, and generate the data
randomly, that way the results can be compared to what is expected.

On 24 June 2015 at 23:10, Susanne Susanne <susanne.stat at gmx.de> wrote:

>
> Hello everyone,
>
> I just saw now, that my last emails were empty. I had this question
> regarding the lme4 package.
> I want to  regress a simple
>
>   lmer(y ~ x + (x | group),
>
> but the number of datapoints vary among groups.
>
> So what I want is to weight groups, which have more measurements than
> others, slightly higher (as they provide more information).
>
>
> My question is: Is this done automatically in the lmer() function or
> should I do it manually by myself?
>
>
> It seems an easy question, but I tried a small example which I attached
> and it's very contradictory.
>
> The upper two pictures seem to proove that lmer doesn't weight groups
> higher which report more datapoints. I get the same regression line,
> independent of whether the blue group has more or less datapoints.
>
> In the lower two pictures I used the "weights=" argument to weight the
> blue group higher. And then suddenly it seems to matter how many datapoints
> a group has. I used the same weights in both pictures, but now I get
> different regression lines, dependent of how many datapoints the blue group
> has.
>
>
> I really need to know this for my thesis and would be very thankful if
> someone could help me!
>
> Susanne
>
>
>
>
> I add my code:
>
> # Data
> # many Values
> x <- c(2, 4, 1, 1.25, 1.5,1.75, 2,2.25, 2.5, 2.75, 3, 3.25,
> 3.5,3.75,4,4.25, 4.5, 4.75, 5)
> y <- c(0.2, 0.4, 0.5, 0.525, 0.57, 0.575, 0.62, 0.625, 0.65, 0.677,
> 0.7,0.726,0.75,0.775,0.8,0.827,0.85,0.873, 0.9)
> group <- c(1,1,rep(2,17))
>
> # Less values
> x <- c(2, 4, 1, 3.25, 5) y <- c(0.2, 0.4,0.5,0.726, 0.9)
> group <- c(1,1,rep(2,3))
>
> # Weights
> if(weights == "Default"){ w <- NULL }
>
> if(weights == "Disequilibrated"){
>    w <- c(1,1, rep(5000,17))
>   # or for less values
>   w <- c(1,1, rep(5000,3))
>   # scale weights to sum weights=number of values
>   w <- length(w)*w/sum(w) }
>
> # Lme Model
> lmeModel <- lmer(y ~ x + (x|group), weights=w)
> s <- summary(lmeModel)
>
> # Plot
> plot(x,y, pch=16, col=c(1,1,rep(2,17)),
> xlim=c(min(x),max(x)),ylim=c(min(y),max(y)), ylab="y",xlab="x",main="Random
> Intercept + Slope") abline(s$coefficients[1,1],s$coefficients[2,1], lty=2,
> col = 1)
>
>
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>


-- 

*Ken Beath*
Lecturer
Statistics Department
MACQUARIE UNIVERSITY NSW 2109, Australia

Phone: +61 (0)2 9850 8516

Level 2, AHH
http://stat.mq.edu.au/our_staff/staff_-_alphabetical/staff/beath,_ken/

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