How can this boxplot be transformed appropriately?
up vote
-1
down vote
favorite
In R,I have produced a boxplot for two different groups, with discrete y-values between 1 and 20. My goal from this work is to investigate whether the average count differs between A and B:
unscaled = ggplot(data3, aes(x = A_or_B, y = Count))+geom_boxplot()
unscaled boxplot
The problem with this is the significant skewness towards one side, which I believe should be solved with an appropriate transformation. The difficulty is that the mode 'count' = 1, and decreases exponentially as 'count' increases.
I have tried and considered several transformations, including a logarithmic transformation, which I believe had little effect because the
I have tried a log transformation, which I believe failed because the value the plot is weighted around it 1:
data3[33]=log(data3["Count"])
logTransformed = ggplot(data3, aes(x = A_or_B, y = logCount))+geom_boxplot()
Log Transformed
I also tried 1/e as a transformation:
data3[34]=(1/exp(data3["Count"])
One_ovr_e_Transformed = ggplot(data3, aes(x = A_or_B, y = One_over_e_Count))+geom_boxplot()
1/e transformed
None of these look as I would expect/want them to look, I'm struggling to find other appropriate transformations that could be applied?
transformation hypothesis-testing
asked Nov 25 at 10:37
thatsnotmyname71
1
add a comment |
up vote
-1
down vote
favorite
In R,I have produced a boxplot for two different groups, with discrete y-values between 1 and 20. My goal from this work is to investigate whether the average count differs between A and B:
unscaled = ggplot(data3, aes(x = A_or_B, y = Count))+geom_boxplot()
unscaled boxplot
The problem with this is the significant skewness towards one side, which I believe should be solved with an appropriate transformation. The difficulty is that the mode 'count' = 1, and decreases exponentially as 'count' increases.
I have tried and considered several transformations, including a logarithmic transformation, which I believe had little effect because the
I have tried a log transformation, which I believe failed because the value the plot is weighted around it 1:
data3[33]=log(data3["Count"])
logTransformed = ggplot(data3, aes(x = A_or_B, y = logCount))+geom_boxplot()
Log Transformed
I also tried 1/e as a transformation:
data3[34]=(1/exp(data3["Count"])
One_ovr_e_Transformed = ggplot(data3, aes(x = A_or_B, y = One_over_e_Count))+geom_boxplot()
1/e transformed
None of these look as I would expect/want them to look, I'm struggling to find other appropriate transformations that could be applied?
transformation hypothesis-testing
asked Nov 25 at 10:37
thatsnotmyname71
1
add a comment |
up vote
-1
down vote
favorite
up vote
-1
down vote
favorite
In R,I have produced a boxplot for two different groups, with discrete y-values between 1 and 20. My goal from this work is to investigate whether the average count differs between A and B:
unscaled = ggplot(data3, aes(x = A_or_B, y = Count))+geom_boxplot()
unscaled boxplot
The problem with this is the significant skewness towards one side, which I believe should be solved with an appropriate transformation. The difficulty is that the mode 'count' = 1, and decreases exponentially as 'count' increases.
I have tried and considered several transformations, including a logarithmic transformation, which I believe had little effect because the
I have tried a log transformation, which I believe failed because the value the plot is weighted around it 1:
data3[33]=log(data3["Count"])
logTransformed = ggplot(data3, aes(x = A_or_B, y = logCount))+geom_boxplot()
Log Transformed
I also tried 1/e as a transformation:
data3[34]=(1/exp(data3["Count"])
One_ovr_e_Transformed = ggplot(data3, aes(x = A_or_B, y = One_over_e_Count))+geom_boxplot()
1/e transformed
None of these look as I would expect/want them to look, I'm struggling to find other appropriate transformations that could be applied?
transformation hypothesis-testing
asked Nov 25 at 10:37
thatsnotmyname71
1
In R,I have produced a boxplot for two different groups, with discrete y-values between 1 and 20. My goal from this work is to investigate whether the average count differs between A and B:
unscaled = ggplot(data3, aes(x = A_or_B, y = Count))+geom_boxplot()
unscaled boxplot
The problem with this is the significant skewness towards one side, which I believe should be solved with an appropriate transformation. The difficulty is that the mode 'count' = 1, and decreases exponentially as 'count' increases.
I have tried and considered several transformations, including a logarithmic transformation, which I believe had little effect because the
I have tried a log transformation, which I believe failed because the value the plot is weighted around it 1:
data3[33]=log(data3["Count"])
logTransformed = ggplot(data3, aes(x = A_or_B, y = logCount))+geom_boxplot()
Log Transformed
I also tried 1/e as a transformation:
data3[34]=(1/exp(data3["Count"])
One_ovr_e_Transformed = ggplot(data3, aes(x = A_or_B, y = One_over_e_Count))+geom_boxplot()
1/e transformed
None of these look as I would expect/want them to look, I'm struggling to find other appropriate transformations that could be applied?
transformation hypothesis-testing
transformation hypothesis-testing
asked Nov 25 at 10:37
thatsnotmyname71
1
asked Nov 25 at 10:37
thatsnotmyname71
1
asked Nov 25 at 10:37
thatsnotmyname71
1
asked Nov 25 at 10:37
thatsnotmyname71
1
asked Nov 25 at 10:37
thatsnotmyname71
1
1
add a comment |
add a comment |
active
oldest
votes
active
oldest
votes
active
oldest
votes
active
oldest
votes
active
oldest
votes
Thanks for contributing an answer to Mathematics Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Some of your past answers have not been well-received, and you're in danger of being blocked from answering.
Please pay close attention to the following guidance:
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f3012672%2fhow-can-this-boxplot-be-transformed-appropriately%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
