My mental model for such a simulation is that you create data from a known
distribution, then use your model to check that you can recover the known
parameters from the data. Thus how the marks are created depends on what
influences them. Here is a toy model to illustrate this - expanding on my code
sample:
# a function to generate marks from parameters
rMarks <- function(n, m, s) {
# a normal distribution limited to between 1 and 6, in 0.5 intervals, with
# mean m and standard deviation s
marks <- rnorm(n, m, s)
marks <- round(marks * 2) / 2
marks[marks < 1] <- 1
marks[marks > 6] <- 6
return(marks)
}
# Teachers in two categories: 70% of teachers (tNormal) grade everyone
according to
# a marks distribution with m = 3.5 and sd = 1 ; the others grade girls with a
# m = 4.5 and sd = 0.7 and boys with m = 3.0 and sd = 1.2
# define who are the "normal teachers"
x <- paste0("t", 1:(nS * nTpS))
tNormal <- sample(x, round(nS * nTpS * 0.7), replace = FALSE)
# this is rather pedestrian code, but as explicit as I can make it ...
for (i in 1:nrow(mySim)) {
if (mySim$Teacher[i] %in% tNormal) {
m <- 3.5
s <- 1.0
} else {
if (mySim$Gender[i] == "girl") {
m <- 4.5
s <- 0.7
} else {
m <- 3.0
s <- 1.2
}
}
mySim$Mark[i] <- rMarks(1, m, s)
}
# Validate
table(mySim$Mark)
hist(mySim$Mark[mySim$Teacher %in% tNormal],
col = "#0000BB44")
hist(mySim$Mark[ ! mySim$Teacher %in% tNormal],
add = TRUE,
col = "#BB000044")
Then the challenge is to recover the parameters from your analysis.
Cheers,
Boris
> On 2019-05-19, at 11:14, varin sacha <varinsa...@yahoo.fr> wrote:
>
> Dear Boris,
>
> Great !!!! But what about Mark in your R code ? Don't we have to precise in
> the R code that mark ranges between 1 to 6 (1 ; 1.5 ; 2 ; 2.5 ; 3 ; 3.5 ; 4 ;
> 4.5 ; 5 ; 5.5 ; 6) ?
>
> By the way, to fit a linear mixed model, I use lme4 package and then the lmer
> function works with the variables like in this example here below :
>
> library(lme4)
> mm=lmer(Mark ~Gender + (1 | School / Class), data=Dataset)
>
> With your R code, how can I write the lmer function to make it work ?
>
> Best,
> S.
>
>
>
>
>
>
>
> Le dimanche 19 mai 2019 à 15:26:39 UTC+2, Boris Steipe
> <boris.ste...@utoronto.ca> a écrit :
>
>
>
>
>
> Fair enough - there are additional assumptions needed, which I make as
> follows:
> - each class has the same size
> - each teacher teaches the same number of classes
> - the number of boys and girls is random within a class
> - there are 60% girls (just for illustration that it does not have to be
> equal)
>
>
> To make the dependencies explicit, I define them so, and in a way that they
> can't be inconsistent.
>
> nS <- 10 # Schools
> nTpS <- 5 # Teachers per School
> nCpT <- 2 # Classes per teacher
> nPpC <- 20 # Pupils per class
> nS * nTpS * nCpT * nPpC == 2000 # Validate
>
>
> mySim <- data.frame(School = paste0("s", rep(1:nS, each = nTpS*nCpT*nPpC)),
> Teacher = paste0("t", rep(1:(nTpS*nS), each = nCpT*nPpC)),
> Class = paste0("c", rep(1:(nCpT*nTpS*nS), each = nPpC)),
> Gender = sample(c("boy", "girl"),
> (nS*nTpS*nCpT*nPpC),
> prob = c(0.4, 0.6),
> replace = TRUE),
> Mark = numeric(nS*nTpS*nCpT*nPpC),
> stringsAsFactors = FALSE)
>
>
> Then you fill mySim$Mark with values from your linear mixed model ...
>
> mySim$Mark[i] <- simMarks(mySim[i]) # ... or something equivalent.
>
>
> All good?
>
> Cheers,
> Boris
>
>
>
>> On 2019-05-19, at 08:05, varin sacha <varinsa...@yahoo.fr> wrote:
>>
>> Many thanks to all of you for your responses.
>>
>> So, I will try to be clearer with a larger example. Te end of my mail is the
>> more important to understand what I am trying to do. I am trying to simulate
>> data to fit a linear mixed model (nested not crossed). More precisely, I
>> would love to get at the end of the process, a table (.txt) with columns and
>> rows. Column 1 and Rows will be the 2000 pupils and the columns the
>> different variables : Column 2 = classes ; Column 3 = teachers, Column 4 =
>> schools ; Column 5 = gender (boy or girl) ; Column 6 = mark in Frecnh
>>
>> Pupils are nested in classes, classes are nested in schools. The teacher
>> are part of the process.
>>
>> I want to simulate a dataset with n=2000 pupils, 100 classes, 50 teachers
>> and 10 schools.
>> - Pupils n°1 to pupils n°2000 (p1, p2, p3, p4, ..., p2000)
>> - Classes n°1 to classes n°100 (c1, c2, c3, c4,..., c100)
>> - Teachers n°1 to teacher n°50 ( t1, t2, t3, t4, ..., t50)
>> - Schools n°1 to chool n°10 (s1, s2, s3, s4, ..., s10)
>>
>> The nested structure is as followed :
>>
>> -- School 1 with teacher 1 to teacher 5 (t1, t2, t3, t4 and t5) with classes
>> 1 to classes 10 (c1, c2, c3, c4, c5, c6, c7, c8,c9,c10), pupils n°1 to
>> pupils n°200 (p1, p2, p3, p4,..., p200).
>>
>> -- School 2 with teacher 6 to teacher 10, with classes 11 to classes 20,
>> pupils n°201 to pupils n°400
>>
>> -- and so on
>>
>> The table (.txt) I would love to get at the end is the following :
>>
>> Class Teacher School gender Mark
>> 1 c1 t1 s1 boy 5
>> 2 c1 t1 s1 boy 5.5
>> 3 c1 t1 s1 girl 4.5
>> 4 c1 t1 s1 girl 6
>> 5 c1 t1 s1 boy 3.5
>> 6 ... .... .... ..... .....
>>
>>
>> The first 20 rows with c1, with t1, with s1, gender (randomly slected) and
>> mark (andomly selected) from 1 to 6
>> The rows 21 to 40 with c2 with t1 with s1
>> The rows 41 to 60 with c3 with t2 with s1
>> The rows 61 to 80 with c4 with t2 with s1
>> The rows 81 to 100 with c5 with t3 with s1
>> The rows 101 to 120 with c6 with t3 with s1
>> The rows 121 to 140 with c7 with t4 with s1
>> The rows 141 to 160 with c8 with t4 with s1
>> The rows 161 to 180 with c9 with t5 with s1
>> The rows 181 to 200 with c10 with t5 with s1
>>
>> The rows 201 to 220 with c11 with t6 with s2
>> The rows 221 to 240 with c12 with t6 with s2
>>
>> And so on...
>>
>> Is it possible to do that ? Or am I dreaming ?
>>
>>
>> Le dimanche 19 mai 2019 à 10:45:43 UTC+2, Linus Chen
>> <linus.l.c...@gmail.com> a écrit :
>>
>>
>>
>>
>>
>> Dear varin sacha,
>>
>> I think it will help us help you, if you give a clearer description of
>> what exactly you want.
>>
>> I assume the situation is that you know what a data structure you
>> want, but do not know
>> how to conveniently create such structure.
>> And that is where others can help you.
>> So, please, describe the wanted data structure more thoroughly,
>> ideally with example.
>>
>> Thanks,
>> Lei
>>
>> On Sat, May 18, 2019 at 10:04 PM varin sacha via R-help
>> <r-help@r-project.org> wrote:
>>>
>>> Dear Boris,
>>>
>>> Yes, top-down, no problem. Many thanks, but in your code did you not forget
>>> "teacher" ? As a reminder teacher has to be nested with classes. I mean the
>>> 50 pupils belonging to C1 must be with (teacher 1) T1, the 50 pupils
>>> belonging to C2 with T2, the 50 pupils belonging to C3 with T3 and so on.
>>>
>>> Best,
>>>
>>>
>>> Le samedi 18 mai 2019 à 16:52:48 UTC+2, Boris Steipe
>>> <boris.ste...@utoronto.ca> a écrit :
>>>
>>>
>>>
>>>
>>>
>>> Can you build your data top-down?
>>>
>>>
>>>
>>> schools <- paste("s", 1:6, sep="")
>>>
>>> classes <- character()
>>> for (school in schools) {
>>> classes <- c(classes, paste(school, paste("c", 1:5, sep=""), sep = "."))
>>> }
>>>
>>> pupils <- character()
>>> for (class in classes) {
>>> pupils <- c(pupils, paste(class, paste("p", 1:10, sep=""), sep = "."))
>>> }
>>>
>>>
>>>
>>> B.
>>>
>>>
>>>
>>>> On 2019-05-18, at 09:57, varin sacha via R-help <r-help@r-project.org>
>>>> wrote:
>>>>
>>>> Dear R-Experts,
>>>>
>>>> In a data simulation, I would like a balanced distribution with a nested
>>>> structure for classroom and teacher (not for school). I mean 50 pupils
>>>> belonging to C1, 50 other pupils belonging to C2, 50 other pupils
>>>> belonging to C3 and so on. Then I want the 50 pupils belonging to C1 with
>>>> T1, the 50 pupils belonging to C2 with T2, the 50 pupils belonging to C3
>>>> with T3 and so on. The school don’t have to be nested, I just want a
>>>> balanced distribution, I mean 60 pupils in S1, 60 other pupils in S2 and
>>>> so on.
>>>> Here below the reproducible example.
>>>> Many thanks for your help.
>>>>
>>>> ##############
>>>> set.seed(123)
>>>> # Génération aléatoire des colonnes
>>>> pupils<-1:300
>>>> classroom<-sample(c("C1","C2","C3","C4","C5","C6"),300,replace=T)
>>>> teacher<-sample(c("T1","T2","T3","T4","T5","T6"),300,replace=T)
>>>> school<-sample(c("S1","S2","S3","S4","S5"),300,replace=T)
>>>
>>>> ##############
>>>>
>>>> ______________________________________________
>>>> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>>> 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@r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>> 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.
>
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