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*
longitudinal analysis when one group switched from control to treatment
4 messages · Simon Harmel, Thierry Onkelinx
1 day later
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 at 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 at 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]]
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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 at 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 at 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 at 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 at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
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 at 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 ma 18 mei 2020 om 17:45 schreef Simon Harmel <sim.harmel at 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 at 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 at 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 at 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]]
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