Re: [R] bootstrap CI of the difference between 2 Cramer's V
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
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)
Re: [R] bootstrap CI of the difference between 2 Cramer's V
I would calculate the difference and the CI about that difference. You would
not get the same thing by comparing the bootstrap CI of the group means.
One use for this is to determine if the confidence interval for the difference
in means includes zero. An alternative would be to use a more conventional test
(rather than calculate a difference) and then find a mean p-value and a
confidence interval about the p-value. This gives a better assessment of the
p-value but is harder to decide if the test outcome is "significant."
You might also consider whether you want a permutation test, a randomization
test, or a bootstrap. A permutation test will look at all possible combinations
of the data once. Use this approach when computationally reasonable. A
randomization test will look at a random subset of all possible combinations,
but may include repeats of some combinations. Both of these do not replace
values. The bootstrap replaces values and will therefore tend to minimize the
effects of outliers in the data. With small datasets a risk is that there are
few permutations and performing a randomization test with 1,000,000
randomizations on data with 4000 permutations is not good.
Tim
-Original Message-
From: R-help On Behalf Of Daniel Nordlund
Sent: Saturday, June 4, 2022 3:31 AM
To: varin sacha ; r-help@r-project.org
Subject: Re: [R] bootstrap CI of the difference between 2 Cramer's V
[External Email]
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","g
> reen","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","gre
> en","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
[R] bootstrap CI of the difference between 2 Cramer's V
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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