From: Ben Bolker <bbolker_at_gmail.com>

Date: Mon, 28 Feb 2011 16:23:55 -0700

*>> Brant Inman <brant.inman <at> mac.com <http://mac.com>> writes:
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*>>
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*>> >
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>> > R-helpers:

*>> >
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*>> > I would like to measure the correlation coefficient between the repeated
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*>> measures of a single variable
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*>> > that is measured over time and is unbalanced. As an example,
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*>> consider the
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*>> Orthodont dataset from package
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*>> > nlme, where the model is:
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*>> >
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*>> > fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont)
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*>> >
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*>> > I would like to measure the correlation b/t the variable "distance" at
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*>> different ages such that I would have
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*>> > a matrix of correlation coefficients like the following:
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*>> >
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*>> > age08 age09 age10 age11 age12 age13 age14
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*>> > age08 1
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*>> > age09 1
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*>> > age10 1
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*>> > age11 1
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*>> > age12 1
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*>> > age13 1
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*>> > age14 1
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*>> >
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*>> > The idea would be to demonstrate that the correlations b/t
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*>> > repeated measures of the variable "distance"
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*>> > decrease as the time b/t measures increases For example,
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*>> > one might expect the correlation
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*>> > coefficient b/t age08 and age09 to be higher than that
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*>> > between age08 and age14.
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*>> >
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*>>
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*>> This stuff is not currently possible in lmer/lme4 but is
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*>> easy in nlme:
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*>>
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*>> library(nlme)
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*>> Orthodont$age0 <- Orthodont$age/2-3
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*>> ## later code requires a time index of consecutive integers
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*>> ## (which apparently must also start at 1, although not stated)
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*>>
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*>> fit <- lme(distance~age,random=~1|Subject,data=Orthodont)
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*>>
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*>> ## compute autocorrelation on the basis of lag only, plot
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*>> a <- ACF(fit)
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*>> plot(a,alpha=0.05)
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*>>
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*>>
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*>> fit2 <- update(fit, correlation=corSymm(form=~age0|Subject))
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*>> fit3 <- update(fit, correlation=corAR1(form=~age0|Subject))
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*>>
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*>> AIC(fit,fit2,fit3)
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*>> ## at least on the basis of AIC, this extra complexity is
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*>> ## not warranted
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*>>
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*>> anova(fit,fit2) ## likelihood ratio test
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*>>
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*>> ______________________________________________
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*>> R-help_at_r-project.org <mailto:R-help_at_r-project.org> mailing list
*

*>> https://stat.ethz.ch/mailman/listinfo/r-help
*

*>> PLEASE do read the posting guide
*

*>> http://www.R-project.org/posting-guide.html
*

*>> and provide commented, minimal, self-contained, reproducible code.
*

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 28 Feb 2011 - 23:26:35 GMT

Date: Mon, 28 Feb 2011 16:23:55 -0700

On 11-02-28 11:59 AM, Brant Inman wrote:

> Ben, > > Thanks for the response. Your method generates an answer that is > slightly different than what I was looking for. In the Orthodont > dataset there are 4 age groups (8, 10, 12, 14). I would like to > calculate the correlation of "distance" for all combinations of the > categorical variable "age". The anticipated output would therefore be a > matrix with 4 columns and 4 rows and a diagonal of ones. > > For example, in such a table I would be able to look at the mean within > individual correlation coefficient for distance b/t ages 8 and 10 or, > alternatively, ages 10 and 14. Is there a function in nlme or lme4 that > does this?

Given the model below,

fit2$modelStruct$corStruct

produces

Correlation structure of class corSymm representing
Correlation:

1 2 3

2 -0.099 3 0.021 -0.242 4 -0.298 0.184 0.262

(this is also shown at the end of summary(fit2))

This is the lower triangle of the (symmetric) correlation matrix; the diagonal is 1 by definition.

Isn't that what you're looking for? (Sorry if I'm misunderstanding.)

Ben

> > Brant > > On Feb 28, 2011, at 02:24 AM, Ben Bolker <bbolker_at_gmail.com> wrote: >

>> > R-helpers:

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Mon 28 Feb 2011 - 23:26:35 GMT

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