# [R] Repeated Measures design using lme

Martin Henry H. Stevens HStevens at MUOhio.edu
Tue Apr 24 22:37:52 CEST 2007

```Have you seen Pinheiro and Bates (2000) that lays out the nlme
package? It is very helpful.
Hank
On Apr 14, 2007, at 8:02 PM, Kyle. wrote:

> You probably can do this with lme function, but I don't know that for
> sure.  "aov" (included in the "stats" package), with a call to the
> "Error" function how I generally analyze data obtained from a
> repeated measure design.  For a very good description of how Error
> and aov work together, you should read Baron's “Notes on the use of R
> for psychology experiments and questionnaires” (follow the link
> below)  The section beginning on page 36 addresses your question.
> Generally speaking, the aov function expects you to describe your
> model formula similarly to how you would with a call to the "lm"
> function.  For example, Y~X1*X2 describes a two way ANOVA--your
> dependent variable Y modeled as explanatory variables X1 and X2,
> including the interaction term:
> 		model.aov<-aov(Y~X1*X2, data=DataFile)
>
> If X1 and X2 are within-subject variables, then the above formula
> would be written as follows:
>
> 		model.aov<-aov(Y~X1*X2+Error(Subject/X1*X2),data=DataFile)
>
> The call "summary(model.aov)" will display a summary of the model.
> Depending on your experience working with model formulae in R, there
> are several steps in the repeated-measure ANOVA procedure that can
> come back to haunt you, if you're not careful (e.g., checking the
> assumption of sphericity, normally distributed errors, homogeneity of
> variance, etc., etc.), so make sure you're covered on these before
> you believe your results.
>
> Here's a link to the document I referred to: http://cran.r-
> project.org/doc/contrib/Baron-rpsych.pdf
>
>
>
>
> Kyle H. Ambert
> Graduate Student, Dept. Behavioral Neuroscience
> Oregon Health & Science University
> ambertk at ohsu.edu
>
>
>
>
>
>
> On Apr 8, 2007, at 7:55 PM, Scott Norton wrote:
>
>> Hi,
>> I have what I believe is a repeated-measures dataset that I'm
>> trying to analyze using lme().  This is *not* homework, but an
>> exercise in my trying to self-teach myself repeated-measure ANOVA
>> for other *real* datasets that I have and that are extremely
>> similar to the following design.
>>
>> I'm fairly sure the dataset described below would work with lme()
>> -- but it'd be great if anybody can confirm that after I describe
>> the dataset below)
>>
>> The study involves measuring the effect of a drug on blood
>> pressure. There were 16 patients in all and 6 replicate measures
>> per patient of their blood pressure on one week (one measure per
>> day). Two weeks later, a drug was introduced to 8 randomly selected
>> patients in such a way that I had equal representation of the 4 age
>> groups among the two treatment groups. Then, another two weeks
>> later, 6 replicate measures per patient (per day) of blood pressure
>> was retaken.  So each patient had 12 total measures whether they
>> were in the treatment group or in the control group (6 reads (R1-
>> R6) in the baseline-week and another 6 reads (R1-R6) in the post-
>> treatment week).
>>
>> So,
>> Background: 16 patients
>> Response measure: Blood pressure
>> Fixed Factor: 4 Age groups
>> Fixed Factor: Drug vs. NoDrug
>> Random factor: Day of the read (i.e. 6 replicate reads (R1-R6) at
>> the baseline time, and 6 replicate reads (R1-R6) after the drug has
>> had time to take effect)
>> Random Factor: Subjects 1-16
>>
>> Patient    AgeGroup    BP(Blood Pressure)    Read (replicate
>> reads)    Pre/PostTreatmentWeek    Group
>> 1                20-29            83
>> R1
>> pre                                    Treat
>> 2                20-29            81
>> R1
>> pre                                    Control
>> 3                20-29            74
>> R1
>> pre                                    Treat
>> 4                20-29            85
>> R1
>> pre                                    Control
>> 5                30-39            82
>> R1
>> pre                                    Treat
>>
>
>
>
>
>
>
>> 3                20-29            74
>> R2
>> pre                                    Treat
>>
>
>
>
>
>
>
>> 1                20-29            83
>> R1
>> post                                   Treat
>> 2                20-29            82
>> R1
>> post                                   Control
>> 3                20-29            86
>> R1
>> post                                   Treat
>> 4                20-29            84
>> R1
>> post                                   Control
>>
>
>
>
>
>
>
>>
>> I'm trying to do an analysis of variance to decide whether there is
>> a measurable change in blood pressure between the Treat and Control
>> groups.
>>
>> Another issue is that some of the 16 patients didn't get all 6
>> replicate reads in their pre/post treatment weeks, so I need to
>> include the na.omit function.
>>
>> What I think I'm having the most trouble with is the repeated reads
>> (R1 through R6) in the pre/post treatment weeks.  I'm fairly sure
>> this is a random variable -- their order or identify (R1 in pre-
>> treatment week has no relation to R1 in the post-treatment week,
>> etc).  By placing Read as a random variable, am I covering myself
>> there?
>> If I execute:
>>
>>> summary(lme(BP ~ Group, random = ~ 1 | Patient, data = bloodpress,
>>> na.action=na.omit))
>>
>> I get a result, but I'm not sure it's correct -- do I need to tell
>> the model about the Read factor (R1-R6 in pre/post weeks)?
>>
>> I'm really trying to set the right form of the lme() function call
>> to decide
>> 1) if there is a statistical difference between the Treat/Control
>> groups and,
>> 2) if one takes into account AgeGroup, is there a statistical
>> difference between Treat/Control Groups, and finally
>> 3) if I don't see a statistical difference, can someone recommend
>> an R function that might solve the supplemental question, "given
>> the noise in day-to-day blood pressure reads, and given that I
>> wanted to have enough statistical power to observe a say, a 5%
>> benefit in blood pressure, how many additional reads or patients I
>> would need."
>>
>> Basically, is lme() the proper function, and can someone offer any
>> pointers on what my call to this function should look like to make
>> the above to determinations?
>>
>> Thanks!
>> -Scott
>>
>> ______________________________________________
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>> PLEASE do read the posting guide http://www.R-project.org/posting-
>> guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-
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Dr. Hank Stevens, Assistant Professor
338 Pearson Hall
Botany Department
Miami University
Oxford, OH 45056

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