blog/how-to-do-a-t-test-or-anova-for-many-variables-at-once-in-r-and-communicate-the-results-in-a-better-way/

[utterances-bot]

How to do a t-test or ANOVA for more than one variable at once in R - Stats and R

Learn how to compare samples for multiple variables at once in R thanks to a Student t-test or ANOVA and communicate the results in a better way

https://statsandr.com/blog/how-to-do-a-t-test-or-anova-for-many-variables-at-once-in-r-and-communicate-the-results-in-a-better-way/

[AntoineSoetewey]

Comment written by CARLOS ORTEGA on March 19, 2020 19:59:08:

Thanks for your entry, very elaborated and clear.
For further reference, and I used it very recently, I strongly recommend you to take a look to "compareGroups" package.

It will make the first analysis (not the graphical one) automatically even if the dataset includes categoricals.
https://cloud.r-project.org/web/packages/compareGroups/index.html

Thanks again,
Carlos.

[AntoineSoetewey]

Comment written by Duleep Samuel on March 20, 2020 08:02:54:

thanks for sharing a very useful site

[AntoineSoetewey]

Comment written by Duleep Samuel on March 20, 2020 08:02:54:

thanks for sharing a very useful site

Comment written by Antoine Soetewey on March 20, 2020 08:05:25:

Glad you like it!

[AntoineSoetewey]

Comment written by CARLOS ORTEGA on March 19, 2020 19:59:08:

Thanks for your entry, very elaborated and clear. For further reference, and I used it very recently, I strongly recommend you to take a look to "compareGroups" package.

It will make the first analysis (not the graphical one) automatically even if the dataset includes categoricals. https://cloud.r-project.org/web/packages/compareGroups/index.html

Thanks again, Carlos.

Comment written by Antoine Soetewey on March 20, 2020 09:38:54:

Thanks Carlos for your comment.

I added a reference to the package you mentioned, but I'll definitely discuss it in more details in an article about descriptive statistics and/or about presenting odds ratio.

Regards, Antoine

[AntoineSoetewey]

Comment written by Chuck Powell on March 20, 2020 12:18:19:

Take a look at ggstatsplot (https://github.com/IndrajeetPatil/ggstatsplot) [full disclosure I contributed a small amount to the package in the past]

[AntoineSoetewey]

Comment written by Chuck Powell on March 20, 2020 12:18:19:

Take a look at ggstatsplot (https://github.com/IndrajeetPatil/ggstatsplot) [full disclosure I contributed a small amount to the package in the past]

Comment written by Antoine Soetewey on March 20, 2020 14:58:17:

The package is really complete, thanks for your input Chuck!

I have mentioned it in the article and I'll definitely discuss about it again in a future article.

[AntoineSoetewey]

Comment written by Shodan on April 02, 2020 17:16:00:

Thank you for this I have found it very helpful. However, I have some questions.

Questions

• In the first graph, is the top value shown "1.9e-07" a p-value?
• If so, why isn't it written out as such?
• If son, is it considered significant?
• If so, why aren't asterisks shown?
• The second and third numbers are p-values 
• Are they significant? 
• If so, why aren't asterisks shown? 
• What level of significance do the 4 asterisks shown  represent?
• Could a graph have comparisons with p-values at different levels of significance (e.g., 0.05, 0.01, 0.001 etc.)?

Any help interpreting these graphs would be very helpful.

Thank you. Jason, the #rstatsnewbie

[AntoineSoetewey]

Comment written by Shodan on April 02, 2020 17:16:00:

Thank you for this I have found it very helpful. However, I have some questions.

Questions

• In the first graph, is the top value shown "1.9e-07" a p-value?
• If so, why isn't it written out as such?
• If son, is it considered significant?
• If so, why aren't asterisks shown?
• The second and third numbers are p-values
• Are they significant?
• If so, why aren't asterisks shown?
• What level of significance do the 4 asterisks shown  represent?
• Could a graph have comparisons with p-values at different levels of significance (e.g., 0.05, 0.01, 0.001 etc.)?

Any help interpreting these graphs would be very helpful.

Thank you. Jason, the #rstatsnewbie

Comment written by Antoine Soetewey on April 03, 2020 08:00:14:

Dear Shodan,

• In the first graph, is the top value shown "1.9e-07" a p-value? Yes it is a p-value. 1.9e-07 is the scientific notation for 0.00000019, so the p-value is very small. 
• If so, why isn't it written out as such? Simply because writing the scientific notation is shorter than to write 0.00000019 (so it is more concise, especially if the p-value is even smaller). 
• If so, is it considered significant? The p-value is so small, we reject the null hypothesis (even for a small alpha) so the 2 species versicolor and virginica are significantly different from each other in terms of Sepal.Length. 
• If so, why aren't asterisks shown? The package doesn't allow to write both the p-values AND the asterisks, it is either the p-values or the asterisks. As I find p-values more informative, I chose to display them and not the asterisks. Furthermore, due to multiple testing, the p-values should be adjusted accordingly so the asterisks may not be correct if you have multiple tests. For instance, usually a p-value of 0.04 is significant at 5% level, so an asterisk is usually displayed. However, if you performed 2 t-tests, the significance level is alpha = 0.05 / 2 = 0.025, so your p-value of 0.04 is not significant anymore, so displaying an asterisk is not correct in that case.
• The second and third numbers are p-values? All numeric values in the plots are p-values. 
• Are they significant? It depends on the significance level you choose. Usually, we choose a significance level of 5%. So if p-values are below 0.05, they are significant and we reject the null hypothesis (H0), if p-values >= 0.05, they are not significant and we fail to reject H0. 
• If so, why aren't asterisks shown? Same reasons than above. 
• What level of significance do the 4 asterisks shown represent? I don't see any asterisk in this article. In R, if a p-value is less than 0.05, it is flagged with one star (*), if a p-value is less than 0.01, it is flagged with two stars (**), and if a p-value is less than 0.001, it is flagged with three stars (***), so I assume a p-value with 4 stars means it is smaller than 0.0001.
• Could a graph have comparisons with p-values at different levels of significance (e.g., 0.05, 0.01, 0.001 etc.)? Not sure I understand your question here but I'll try. Of course a p-value shown in a plot allows you to compare it to any significance levels of your choice. It's the point of the p-value, so you can conclude (reject or do not reject H0) at different significance levels.

Hope this helps.

Regards, Antoine

[AntoineSoetewey]

Comment written by Pat on May 29, 2020 21:49:01:

I have a question on your ANOVA section for this article. I was under the impression that you couldn't use a t-test multiple times without making a correction to your p value, but the values shown in the graphs are non-adjusted is that right? - EDIT - It looks like you have that addressed in the introductory note on 'multiplicity'.  I'm assuming that since your t-test values are so small, they qualify as fitting the Bonferroni correction for multiple t-tests.

[grahamreidpsychology]

Thank you so much for sharing your code. It is excellent! I have 6 ANOVAS that I want to run on my depedent variable and plot as boxplots. I have written a loop using lapply(), which allows me to run the 6 ANOVAS and plot the boxplots no problem. However, I have no idea how to loop so that I can do the pairwise comparisons and add the significant ones to the boxplots. Would you have any idea how to do this or how to ammend your code (which is similar to mine) to add these comparisons in one loop ?

[AntoineSoetewey]

Thank you so much for sharing your code. It is excellent! I have 6 ANOVAS that I want to run on my depedent variable and plot as boxplots. I have written a loop using lapply(), which allows me to run the 6 ANOVAS and plot the boxplots no problem. However, I have no idea how to loop so that I can do the pairwise comparisons and add the significant ones to the boxplots. Would you have any idea how to do this or how to ammend your code (which is similar to mine) to add these comparisons in one loop ?

Hello,

Hard for me to reply without seeing your code. My code can be used to run ANOVAs and do multiple comparisons, but it will display all comparisons and not just the significant ones.

I believe editing my code to display only the significant comparisons would be too much of a struggle (and not very efficient). However, here are two resources that may be of interest to you:

• Grouped analysis with grouped_ggbetweenstats
• Repeating function execution across multiple columns in a dataframe

Hope this helps.

Regards, Antoine