[R-sig-ME] longitudinal analysis when one group switched from control to treatment

Thierry Onkelinx th|erry@onke||nx @end|ng |rom |nbo@be
Mon May 18 19:13:25 CEST 2020


Dear Simon,

You are wondering if the model captures the change in treatment for a
school. Therefore you need to plot the residuals vs every combination of
school and year. A boxplot for every combination would be useful. If the
change in treatment triggers a shift in residuals, then the current model
fails.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey
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<https://www.inbo.be>


Op ma 18 mei 2020 om 17:45 schreef Simon Harmel <sim.harmel using gmail.com>:

> Dear Thierry,
>
> By "Have a look at the residuals" you mean something like the following
> (below)? So no other adjustment is required for the switching that occurred?
>
> plot(m1, type = c("p","smooth"), col.line = 2)
>
> plot(m1, sqrt(abs(resid(.)))~fitted(.), type = c("p","smooth"), col.line =
> 2)
>
> On Mon, May 18, 2020 at 2:01 AM Thierry Onkelinx <thierry.onkelinx using inbo.be>
> wrote:
>
>> Dear Simon,
>>
>> The question is rather if the model is able to capture this change. Have
>> a look at the residuals. If they look OK, then the model handles the change
>> in treatment.
>>
>> Best regards,
>>
>> ir. Thierry Onkelinx
>> Statisticus / Statistician
>>
>> Vlaamse Overheid / Government of Flanders
>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
>> AND FOREST
>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>> thierry.onkelinx using inbo.be
>> Havenlaan 88 bus 73, 1000 Brussel
>> www.inbo.be
>>
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>> To call in the statistician after the experiment is done may be no more
>> than asking him to perform a post-mortem examination: he may be able to say
>> what the experiment died of. ~ Sir Ronald Aylmer Fisher
>> The plural of anecdote is not data. ~ Roger Brinner
>> The combination of some data and an aching desire for an answer does not
>> ensure that a reasonable answer can be extracted from a given body of data.
>> ~ John Tukey
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>> <https://www.inbo.be>
>>
>>
>> Op zo 17 mei 2020 om 01:09 schreef Simon Harmel <sim.harmel using gmail.com>:
>>
>>> Hello All,
>>>
>>> I have a 3-year longitudinal dataset (*see link below the table*). Up to
>>> year 2 (coded "1"), 8 schools (4 in Treatment, 4 in Control) cooperated
>>> with the study. But in year 3 (coded "2"), one of the Treatment schools
>>> (named "good") dropped out.
>>>
>>> Also in year 3 (coded "2"), we were made to move one of the *Control
>>> *schools
>>> (named "*orange*") to the *Treatment *group. The full design of the study
>>> is shown in the Table below.
>>>
>>> I want to regress "year" and "group" on "y" (a continuous response) in
>>> lme4
>>> package in R. But is there a way to capture the switch of one of the
>>> control schools to the treatment group?
>>>
>>> Thank you very much, Simon
>>>
>>> ·       *Switched from control to treatment*
>>>
>>> ·       *Out as of year coded 2*
>>>
>>> *SCHOOL NAMES*
>>>
>>> *Year*
>>>
>>> *Codes*
>>>
>>> *Control*
>>>
>>> *Treatment*
>>>
>>> 0
>>>
>>> har
>>>
>>> john
>>>
>>> bus
>>>
>>> orange
>>>
>>> caro
>>>
>>> good
>>>
>>> bla
>>>
>>> carm
>>>
>>> 1
>>>
>>> har
>>>
>>> john
>>>
>>> bus
>>>
>>> *orange*
>>>
>>> caro
>>>
>>> good
>>>
>>> bla
>>>
>>> carm
>>>
>>> 2
>>>
>>> har
>>>
>>> john
>>>
>>> bus
>>>
>>> X
>>>
>>> caro
>>>
>>> *orange*
>>>
>>> bla
>>>
>>> carm
>>>
>>> *library(lme4)*
>>> *dat <- read.csv('https://raw.githubusercontent.com/hkil/m/master/z.csv
>>> <https://raw.githubusercontent.com/hkil/m/master/z.csv>')*
>>>
>>> *m1 <- lmer(y~ year*group + (1|stid), data = dat)      #### 'stid' =
>>> student id                m2 <- lmer(y~ year*group + (1|scid/stid), data
>>> =
>>> dat) #### 'scid' = school id*
>>>
>>>         [[alternative HTML version deleted]]
>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>

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