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To interpret correctly the Hartigans' dip test I constructed some distributions (the original code is from here) and I increased the value of exp(mu2) (called 'Intensity of bimodularity' from now on - Edit : I should have called it 'Intensity of bimodality') to get bimodality. In the first graph, you can see some example of distributions. Then I estimated the diptest index (second graph) and the p value (third graphe) associated (package diptest) to those different simulated distributions. The R code used is at the end of my post.

To interpret correctly the Hartigans' dip test I constructed some distributions (the original code is from here) and I increased the value of exp(mu2) (called 'Intensity of bimodularity' from now on - Edit : I should have called it 'Intensity of bimodality') to get bimodality. In the first graph, you can see some example of distributions. Then I estimated the diptest index (second graph) and the p value (third graphe) associated (package diptest) to those different simulated distributions. The R code used is at the end of my post.

I would like to find a way to quantify the intensity of bimodality of some distributions I got empirically. From what I read, there is still some debate about the way to quantify bimodality. I chose to use Hartigans dip test which seems to be the only one available on R (original paper : http://www.stat.washington.edu/wxs/Stat593-s03/Literature/hartigan85a.pdf). Hartigans dip test is defined as : "The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference".

To interpret correctly the Hartigan's dip test I constructed some distributions (the original code is from here) and I increased the value of exp(mu2) (called 'Intensity of bimodularity' from now on - Edit : I whould have called it 'Intensity of bimodality') to get bimodality. In the first graph, you can see some example of distributions. Then I estimated the diptest index (second graph) and the p value (third graphe) associated (package diptest) to those different simulated distributions. The R code used is at the end of my post.

What I show here is that the dip test index is high and the Pvalue is low when the distributions are bimodal. Which is contrary to what you can read on the internet.

I am no expert in statistics, so that I barely understood Hartigans' paper. I would like to get some comments about the right way we should interpret Hartigans dip test. Am I wrong somewhere ?

I would like to find a way to quantify the intensity of bimodality of some distributions I got empirically. From what I read, there is still some debate about the way to quantify bimodality. I chose to use Hartigans' dip test which seems to be the only one available on R (original paper : http://www.stat.washington.edu/wxs/Stat593-s03/Literature/hartigan85a.pdf). Hartigans' dip test is defined as : "The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference".

To interpret correctly the Hartigans' dip test I constructed some distributions (the original code is from here) and I increased the value of exp(mu2) (called 'Intensity of bimodularity' from now on - Edit : I should have called it 'Intensity of bimodality') to get bimodality. In the first graph, you can see some example of distributions. Then I estimated the diptest index (second graph) and the p value (third graphe) associated (package diptest) to those different simulated distributions. The R code used is at the end of my post.

What I show here is that the dip test index is high and the Pvalue is low when the distibutions are bimodal. Which is contrary to what you can read on the internet.

I am no expert in statistics, so that I barely understood Hartigans' paper. I would like to get some comments about the right way we should interpret Hartigans' dip test. Am I wrong somewhere ?

I would like to find a way to quantify the intensity of bimodularity of some distributions I got empirically. From what I read, there is still some debate about the way to quantify bimodularity. I chose to use Hartigan's dip test which seems to be the only one available on R (original paper : http://www.stat.washington.edu/wxs/Stat593-s03/Literature/hartigan85a.pdf). Hartigan's dip test is defined as : "The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference".

To interpret correctly the Hartigan's dip test I constructed some distributions (the original code is from here) and I increased the value of exp(mu2) (called 'Intensity of bimodularity' from now on) to get bimodularity. In the first graph, you can see some example of distributions. Then I estimated the diptest index (second graph) and the p value (third graphe) associated (package diptest) to those different simulated distributions. The R code used is at the end of my post.

What I show here is that the dip test index is high and the Pvalue is low when the distibutions are bimodal. Which is contrary to what you can read on the internet.

I am no expert in statistics, so that I barely understood Hartigan's paper. I would like to get some comments about the right way we should interpret Hartigan's dip test. Am I wrong somewhere ?

I would like to find a way to quantify the intensity of bimodality of some distributions I got empirically. From what I read, there is still some debate about the way to quantify bimodality. I chose to use Hartigans dip test which seems to be the only one available on R (original paper : http://www.stat.washington.edu/wxs/Stat593-s03/Literature/hartigan85a.pdf). Hartigans dip test is defined as : "The dip test measures multimodality in a sample by the maximum difference, over all sample points, between the empirical distribution function, and the unimodal distribution function that minimizes that maximum difference".

To interpret correctly the Hartigan's dip test I constructed some distributions (the original code is from here) and I increased the value of exp(mu2) (called 'Intensity of bimodularity' from now on - Edit : I whould have called it 'Intensity of bimodality') to get bimodality. In the first graph, you can see some example of distributions. Then I estimated the diptest index (second graph) and the p value (third graphe) associated (package diptest) to those different simulated distributions. The R code used is at the end of my post.

What I show here is that the dip test index is high and the Pvalue is low when the distributions are bimodal. Which is contrary to what you can read on the internet.

I am no expert in statistics, so that I barely understood Hartigans' paper. I would like to get some comments about the right way we should interpret Hartigans dip test. Am I wrong somewhere ?