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Re: st: testing for bimodality in survey data


From   Joerg Luedicke <joerg.luedicke@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: testing for bimodality in survey data
Date   Thu, 10 Nov 2011 11:14:53 -0500

I agree with Nick's general remarks on bimodality.

Dana, we do not have any background information on your project, but -
generally speaking - only "testing" whether a distribution is bimodal
or not looks like an empty excercise to me. Important is to understand
the nature of your distribution, i.e. how is it shaped and what are
possible explanations for it being shaped in a certain way. To
illustrate this, consider the following example for the measurement of
height, sampled from a population that contained male and female
humans.

When using the height measurement from the nhanes data and plot a
kernel density, the distribution does not look exactly bimodal at
first sight. That is because the difference between the means of the
two distributions (for men and women) is somewhat too small in
relation to their variances. So, for the sake of
argument/illustration, let's just make the sampled men a bit taller
(b). We can then estimate the density (c) and clearly find evidence
for the distribution of height being bimodal, indicating a mixture of
two normal distributions (as we assume at this point, one for male and
one for female heights). We can now go ahead and fit a mixture model
for two components to estimate the parameters for the mixed
distributions (d). In this case the results look very reasonable and
we if we crosstab the actual gender with the classification (d), we
see that around 95% of men and women are correctly classified. In
addition, we could also go ahead and plot the probability density
function for the bimodal distribution, using the parameters that we
estimated with the mixture model (e).

So all this seems to make a lot of sense and we can conclude that the
distribution at hand is bimodal and that the bimodality is caused by a
mixture of two Gaussian distributions, revolving around males and
females. Now, in reality, solutions are not always as clear and there
is often not as much previous knowledge available. However, given this
example, I wonder what it would help to formally "test" whether the
distribution is bimodal or not (or whatever the null-hypothesis would
be in such a "test", I do not know)? Given our example, I guess any
such test would give you a green light regarding bimodality because of
the clear-cut solution. However, we already knew it was bimodal and we
also could explain it reasonably, so the "test" would not add a lot of
information. Likewise, what if the "test" gave a red light? Clearly,
the test result would not make a lot of sense. Something similar
applies to small differences of the modes/means. If there is not much
of a bimodal distribution to detect and neither is there a theory nor
previous knowledge that would lead to a reasonable expectation of
bimodality, and now the test says "green", what would that mean? On
the other hand, if you have a strong theory/knowledge upon which you
expect a bimodal distribution and you could also detect it in the
data, but the test says "red", well, I would still go with the
theoretically guided solution. Often it is just a good idea to do more
data exploration and checking, than "testing".


/*Example*/

//a) data
webuse nhanes2, clear

//(b) let's make men a bit taller, for the sake of argument
clonevar height2=height
replace height2=height2+10 if sex==1

//(c) inspecting the distribution using an adaptive kernel estimate
//and saving 1000 grid point at which the density is evaluated
kdens height2, adapt n(1000) g(den grid)

//(d) now we can fit a mixture model, assuming bimodality
fmm height2, comp(2) mix(normal)
fmmlc, savec
tab sex _class_1, row

//(e) and can plot a probability density function using the parameters
//and mixing probabilities as estimated from the ML fit (using the
//grid that we saved earlier)
mat par=e(b)
gen mlmix=(e(pi1_est)*normalden(grid, par[1,1], e(sigma1_est)))+ ///
(e(pi2_est)*normalden(grid, par[1,2], e(sigma2_est)))
line mlmix grid, title("ML fit") ytitle("Density")

/*End*/

Joerg



On Thu, Nov 10, 2011 at 3:37 AM, Nick Cox <n.j.cox@durham.ac.uk> wrote:
> Joerg has in effect already answered your question. Bimodality implies some generating process that is bimodal, so should you want to investigate it formally, it is arguably best to think up a model with that kind of behaviour as one possibility and then estimate its parameters.
>
> For the most part, I have found that bimodality is convincing if and only if (a) it shows up consistently on density estimates with a range of kernels and a range of kernel widths and (b) there is some substantive expectation of a mix of two kinds (males and females, whatever).
>
> Nick
> n.j.cox@durham.ac.uk
>
> Dana Shills
>
> Thank you Joerg. That was very helpful. If I understand this correctly, once you have the kdens plot you can visually see if there are two modes. So there is no statistical test thatconfirms the number of modes in the distribution?
>
>> From: joerg.luedicke@gmail.com
>
>> I am not sure what "testing" is supposed to mean in this context, but
>> if you want to explore the possibility of a multimodal distribution
>> you could indeed go for a non-parametric density estimation. I
>> recommend using Ben Jann's -kdens- (available from SSC, -findit
>> kdens-), which is a quite powerful package and supports probability
>> weights. I would also recommend using an adaptive kernel estimate, as
>> this is usually the best kernel estimate when dealing with multimodal
>> data (at least in my experience). What you could do in addition is
>> checking whether the multimodality is due to distributional mixtures
>> (which is often the case when you find more than one mode). For
>> example, say you find your distribution being bimodal, you could fit a
>> 2-component mixture model to estimate the underlying parameters of the
>> mixed distributions via maximum likelihood (to do this you could use
>> -fmm- which is also available from SSC; if the model does not converge
>> make sure you provide starting values; for Gaussian mixtures you could
>> use the modes from the kernel estimate and guess the variance). You
>> could also check how well the (in this case) 2 distributions can be
>> separated with using an entropy measure which you could calculate with
>> -fmmlc-, also available from SSC.
>
>>
>> On Wed, Nov 9, 2011 at 1:50 PM, Dana Shills <shills52@hotmail.com> wrote:
>
>> > I am using survey data on firms in Ghana. The survey methodology uses stratified random sampling and I have the probability weights. I want to be able to plot a distribution of firm sizes (incorporating the weights) and test for bimodality in the firm size distribution. I looked at the "adgakern" program but I don't think it allows for survey weights. Could someone please point me to what commands I should be looking at?
>
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