Re: [R] lme model specification
If you are now presenting subjects with pairs, have you considered
multidimensional scaling and possibly "Bradley-Terry" models &
extensions? RSiteSearch("multidimensional scaling") and
RSiteSearch("Bradley-Terry") both seemed to contain potentially useful
information.
best wishes,
spencer graves
#
Bill Simpson wrote:
> Thanks very much Spencer for your helpful reply.
>
> On Fri, 2006-01-20 at 07:38 -0800, Spencer Graves wrote:
>
>> Does each subject get only one LED per session or all 4 LEDs?
>
> I simplified a bit. Each subject gets pairs of LEDs and needs to
> estimate the distance between them. LED1 can be Red or Blue, and LED2
> can be R or B -- so there are 4 combos. These are presented in random
> order. So in one session yes each subject sees all combos of LEDs.
>
>
>> This
>>should be important regarding which models are estimaable. In either
>>case, might the following help you?
>>
>>nSubj <- 8
>>nSess <- 4
>>nObsPerSess <- 3
>>
>>library(nlme)
>>library(e1071)
>>P4 <- permutations(4)
>>
>>LED <- letters[t(P4[permSubj,])]
>>
>>set.seed(1)
>>permSubj <- sample(24, nSubj)
>>N <- nSubj*nSess*nObsPerSess
>>DF <- data.frame(
>> Subject=rep(1:nSubj, each=nSess*nObsPerSess),
>> illum=rep(c("star", "moon"), each=N/2),
>> feedback=rep(c("yes", "no"), each=N/4, length=N),
>> session=rep(1:nSess, each=nObsPerSess, nSubj),
>> LED=rep(LED, each=nObsPerSess),
>> Rep=rep(1:nObsPerSess, nSess*nSubj),
>> logdistance=rep(1:nObsPerSess, nSess*nSubj),
>> logestimate=rnorm(nSubj*nSess*nObsPerSess) )
>>
>>fit <- lme(logestimate~logdistance*illum*feedback+LED,
>> random=~1|Subject,
>> correlation=corAR1(form=~Rep|Subject/session),
>> data=DF)
>
> Thanks very much, I didn't know about the corAR1 statement.
>
> Best wishes
> Bill
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] lme model specification
Does each subject get only one LED per session or all 4 LEDs? This
should be important regarding which models are estimaable. In either
case, might the following help you?
nSubj <- 8
nSess <- 4
nObsPerSess <- 3
library(nlme)
library(e1071)
P4 <- permutations(4)
LED <- letters[t(P4[permSubj,])]
set.seed(1)
permSubj <- sample(24, nSubj)
N <- nSubj*nSess*nObsPerSess
DF <- data.frame(
Subject=rep(1:nSubj, each=nSess*nObsPerSess),
illum=rep(c("star", "moon"), each=N/2),
feedback=rep(c("yes", "no"), each=N/4, length=N),
session=rep(1:nSess, each=nObsPerSess, nSubj),
LED=rep(LED, each=nObsPerSess),
Rep=rep(1:nObsPerSess, nSess*nSubj),
logdistance=rep(1:nObsPerSess, nSess*nSubj),
logestimate=rnorm(nSubj*nSess*nObsPerSess) )
fit <- lme(logestimate~logdistance*illum*feedback+LED,
random=~1|Subject,
correlation=corAR1(form=~Rep|Subject/session),
data=DF)
spencer graves
Bill Simpson wrote:
> I have been asked to analyse the results of (what is to me) a very
> complicated experiment.
>
> The dependent measure is the estimated distance, which is measured as a
> function of the actual distance. There are also several other IVs.
>
> The plot of log estimated distance as a function of log distance is
> linear. So in the rest of the analysis I will use logestimate and
> logdistance.
>
> My plan is to see how the other IVs affect the slope and intercept of
> this linear relationship between logestimate and log distance.
>
> What complicates everything is that each datum point is not independent.
> Rather, many data points come from each subject.
>
> So:
> * Each subject gets many objects at many distances which he has to
> estimate.
> * Each subject repeats this experiment using 4 colours of LEDs.
> * Each subject repeats this experiment on 4 different sessions.
> * Half the subjects do this under starlight, half under moonlight.
> * Half the subjects do it with feedback and half without.
>
> So some of these variables are within subjects and some between. I think
> lme is a good way to proceed. But I am hung up on how to specify the
> model
>
> fit<-lme(fixed=logestimate~logdistance*session*illum*feedback,
> random=???|subject???, data=df1)
>
> I am familiar with the steps of model building using lm(), exploring
> different models etc, so I think I will be OK once I get the idea of
> specifying the basic lme model.
>
> I have Pinheiro and Bates (2000) here.
>
> Thanks very much for any help
>
> Bill Simpson
>
> __
> R-help@stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
RE: [R] lme model specification
Thanks for the response. It is actually a repeated measures study, I just mention the fixed effects specification because I think I know the random effect specification, i.e.: Random = ~ 1|subject And thanks for the tip about the nonlinear model and the S-plus list. I will check out nlme and the other list. Eric On 6/9/05, Eric Hack <[EMAIL PROTECTED]> wrote: > Dear All, > > > > I am trying to specify the following fixed effects model for lme: If you have a linear fixed-effects model you should use lm, not lme. > > y ~ constant1 - beta1*(x - beta2) > > where y is the response, x is the independent variable, and the > operators above are real arithmetic operations of addition, subtraction, > and multiplication. I realize that this model is just a > reparameterization of y=beta0+beta1*x, but I am using this > parameterization because I am specifically interested in confidence > bounds for beta2. You would need to fit that as a nonlinear model. In reference to such models "linear" means "linear in the parameters" and that model isn't. > I have looked at the help, but the closest hint I find is the I() > function, and that does not seem to work this way. > > > > I confess that I am actually using S-plus, but there does not seem to be > a resource like this list for S-plus. Look for the S-news email list (http://www.biostat.wustl.edu/s-news/) __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] lme model specification
On 6/9/05, Eric Hack <[EMAIL PROTECTED]> wrote: > Dear All, > > > > I am trying to specify the following fixed effects model for lme: If you have a linear fixed-effects model you should use lm, not lme. > > y ~ constant1 - beta1*(x - beta2) > > where y is the response, x is the independent variable, and the > operators above are real arithmetic operations of addition, subtraction, > and multiplication. I realize that this model is just a > reparameterization of y=beta0+beta1*x, but I am using this > parameterization because I am specifically interested in confidence > bounds for beta2. You would need to fit that as a nonlinear model. In reference to such models "linear" means "linear in the parameters" and that model isn't. > I have looked at the help, but the closest hint I find is the I() > function, and that does not seem to work this way. > > > > I confess that I am actually using S-plus, but there does not seem to be > a resource like this list for S-plus. Look for the S-news email list (http://www.biostat.wustl.edu/s-news/) __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
