[R] bootstrap CI of the difference between 2 Cramer's V

[Daniel Nordlund]

There are a few problems with the "rewrite" of the code, both 
syntactically and conceptually.
1. Goodman-Kruskal gamma is for ordinal data.  You should create your 
"shopping" and "statut" variables as factors, ordered from lowest to 
highest using the levels= parameter in  the function, factor
2. In your function, G, you use "data[index,][1,2]"  where you should 
have used either "g1[,c(1,2)]", or "g2[,c(1,2)]".  You should read up on 
Indexing using [] on data frames, to make sure you understand what the 
original code was doing.
3.  The base cor function does not calculate a Goodman-Kruskal gamma 
(unless somebody has written a new version).  So you need to find an 
appropriate function and you may need to structure your data differently 
for calculating gamma, depending on what parameters the function 
demands.  Google is your friend here, search for    "R Goodman Kruskal 
gamma"

Since this is looking like homework to me, I suggest you ask your 
instructor about some of this.

Best of luck,

Dan


On 6/5/2022 9:21 AM, varin sacha wrote:
> Dear Daniel,
> Dear R-experts,
>
> I really thank you a lot Daniel. Nobody had answered to me offline. So, thanks.
> I have tried in the same vein for the Goodman-Kruskal gamma for ordinal data. There is an error message at the end of the code. Thanks for your help.
>
>
> ##############################
> library(ryouready)
> library(boot)
>
> shopping1<-c("très important","important","pas important","pas important","important","très important","important","pas important","très important","très important","important","pas important","pas important","important","très important","très important","important","pas important","pas important","important","très important","très important","important","pas important","pas important","important","très important","très important","important","pas important","pas important","important","très important","très important","important","pas important","pas important","important","très important","important")
>
> statut1<-c("riche","pas riche","moyennement riche","moyennement riche","riche","pas riche","moyennement riche","moyennement riche","riche","pas riche","moyennement riche","riche","pas riche","pas riche","riche","moyennement riche","riche","pas riche","pas riche","pas riche","riche","riche","moyennement riche","riche","riche","moyennement riche","moyennement riche","moyennement riche","pas riche","pas riche","riche","pas riche","riche","pas riche","riche","moyennement riche","riche","pas riche","moyennement riche","riche")
>
> shopping2<-c("important","pas important","très important","très important","important","très important","pas important","important","pas important","très important","important","important","important","important","pas important","très important","très important","important","pas important","très important","pas important","très important","pas important","très important","important","très important","important","pas important","pas important","important","pas important","très important","pas important","pas important","important","important","très important","très important","pas important","pas important")
>
> statut2<-c("moyennement riche","pas riche","riche","moyennement riche","moyennement riche","moyennement riche","pas riche","riche","riche","pas riche","moyennement riche","riche","riche","riche","riche","riche","pas riche","moyennement riche","moyennement riche","pas riche","moyennement riche","pas riche","pas riche","pas riche","moyennement riche","riche","moyennement riche","riche","pas riche","riche","moyennement riche","blue","moyennement riche","pas riche","pas riche","riche","riche","pas riche","pas riche","pas riche")
>
> f1 <- data.frame(shopping=shopping1,statut=statut1,group='grp1')
> f2 <- data.frame(shopping=shopping2,statut=statut2,group='grp2')
> f3 <- rbind(f1,f2)
>
> G <- function(x, index) {
>     
> # calculate goodman for group 1 bootstrap sample
>     g1 <-x[index,][x[,3]=='grp1',]
>     goodman_g1 <- cor(data[index,][1,2])
>    
>   # calculate goodman for group 2 bootstrap sample
>     g2 <-x[index,][x[,3]=='grp2',]
>     goodman_g2 <- cor(data[index,][3,4])
>    
>   # calculate difference
>     goodman_g1-goodman_g2
>     }
>   
>
> # use strata parameter in function boot to resample within each group
> results <- boot(data=f3,statistic=G, strata=as.factor(f3$group),R=2000)
>
> results
> boot.ci(results)
> ##############################
>
>
>
> Le samedi 4 juin 2022 à 09:31:36 UTC+2, Daniel Nordlund <djnordlund using gmail.com> a écrit :
>
>
>
>
>
> On 5/28/2022 11:21 AM, varin sacha via R-help wrote:
>> Dear R-experts,
>>
>> While comparing groups, it is better to assess confidence intervals of those differences rather than comparing confidence intervals for each group.
>> I am trying to calculate the CIs of the difference between the two Cramer's V and not the CI to the estimate of each group’s Cramer's V.
>>
>> Here below my toy R example. There are error messages. Any help would be highly appreciated.
>>
>> ##############################
>> library(questionr)
>> library(boot)
>>
>> gender1<-c("M","F","F","F","M","M","F","F","F","M","M","F","M","M","F","M","M","F","M","F","F","F","M","M","M","F","F","M","M","M","F","M","F","F","F","M","M","F","M","F")
>> color1<-c("blue","green","black","black","green","green","blue","blue","green","black","blue","green","blue","black","black","blue","green","blue","green","black","blue","blue","black","black","green","green","blue","green","black","green","blue","black","black","blue","green","green","green","blue","blue","black")
>>
>> gender2<-c("F","F","F","M","M","F","M","M","M","F","F","M","F","M","F","F","M","M","M","F","M","M","M","F","F","F","M","M","M","F","M","M","M","F","F","F","M","F","F","F")
>> color2<-c("green","blue","black","blue","blue","blue","green","blue","green","black","blue","black","blue","blue","black","blue","blue","green","blue","black","blue","blue","black","black","green","blue","black","green","blue","green","black","blue","black","blue","green","blue","green","green","blue","black")
>>
>> f1=data.frame(gender1,color1)
>> tab1<-table(gender1,color1)
>> e1<-cramer.v(tab1)
>>
>> f2=data.frame(gender2,color2)
>> tab2<-table(gender2,color2)
>> e2<-cramer.v(tab2)
>>
>> f3<-data.frame(e1-e2)
>>
>> cramerdiff=function(x,w){
>> y<-tapply(x[w,1], x[w,2],cramer.v)
>> y[1]-y[2]
>> }
>>
>> results<-boot(data=f3,statistic=cramerdiff,R=2000)
>> results
>>
>> boot.ci(results,type="all")
>> ##############################
>>
>>    
>>
>> ______________________________________________
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>> and provide commented, minimal, self-contained, reproducible code.
> I don't know if someone responded offline, but if not, there are a
> couple of problems with your code.   First, the f3 dataframe is not what
> you think it is.  Second, your cramerdiff function isn't going to
> produce the results that you want.
>
> I would put your data into a single dataframe with a variable
> designating which group data came from.  Then use that variable as the
> strata variable in the boot function to resample within groups.  So
> something like this:
>
> f1 <- data.frame(gender=gender1,color=color1,group='grp1')
> f2 <- data.frame(gender=gender2,color=color2,group='grp2')
> f3 <- rbind(f1,f2)
>
> cramerdiff <- function(x, ndx) {
>     # calculate cramer.v for group 1 bootstrap sample
>     g1 <-x[ndx,][x[,3]=='grp1',]
>     cramer_g1 <- cramer.v(table(g1[,1:2]))
>     # calculate cramer.v for group 2 bootstrap sample
>     g2 <-x[ndx,][x[,3]=='grp2',]
>     cramer_g2 <- cramer.v(table(g2[,1:2]))
>     # calculate difference
>     cramer_g1-cramer_g2
>     }
> # use strata parameter in function boot to resample within each group
> results <- boot(data=f3,statistic=cramerdiff,
> strata=as.factor(f3$group),R=2000)
>
> results
> boot.ci(results)
>
>
>
> Hope this is helpful,
>
> Dan
>

-- 
Daniel Nordlund
Port Townsend, WA  USA


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