Testing continuous zero-inflated response

That said,

wilcox_test(x ~ factor(y), distribution = "exact")

or the same with oneway_test, i.e would be ok?

2013/1/27 Achim Zeileis <Achim.Zeileis at uibk.ac.at>

On Sun, 27 Jan 2013, Kay Cichini wrote:

 Thanks for the reply!

Still, aren't there issues with 2-sample test vs y and excess zeroes
(->many ties), like for Mann-Whitney-U tests?

If you use the (approximate) exact distribution, that is no problem.

The problem with the Wilcoxon/Mann-Whitney test and ties is only that the
simple recursion formula for computing the exact distribution only works
without ties. Thus, it's not the exact distribution that is wrong but only
the standard algorithm for evaluating it.

Best,
Z

 Kind regards,

Kay


2013/1/26 Achim Zeileis <Achim.Zeileis at uibk.ac.at>

 On Fri, 25 Jan 2013, Kay Cichini wrote:

 Hello,

I'm searching for a test that applies to a dataset (N=36) with a
continuous zero-inflated dependent variable

In a regression setup, one can use a regression model with a response
censored at zero. survreg() in survival fits such models, tobit() in AER
is
a convenience interface for this special case.

If the effects of a regressor can be different for the probability of a
zero and the mean of the non-zero observations, then a two-part model can
be used. E.g. a probit fit (via glm) plus a truncated regression (via
truncreg in the package of the same name).

However:


 and only one nominal grouping variable with 2 levels (balanced).

In that case I would probably use no regression model but two-sample
permutation tests, e.g. via the "coin" package.


 In fact there are 4 response variables of this kind which I plan to test

seperately - the amount of zeroes ranges from 75 to 97%..

That means you have between one (!) and nine non-zero observations. In
the
former case, it will be hard to model anything. And even in the latter
case
it will be hard to investigate the probability of zero and the mean of
the
non-zero observations separately.

I would start out with a simple two-way table of (y > 0) vs group and
conduct Fisher's exact test.

And then you might try also your favorite two sample test of y vs group,
preferably using the approximate exact distribution.

Hope that helps,
Z

 I searched the web and found several modelling approaches but have the

feeling that they are overly complex for my very simple dataset.

Thanks in advance for any help!
Kay

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Kay Cichini, MSc Biol

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Tel.: 0650 9359101

E-Mail: kay.cichini at gmail.com

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Grubenweg 22, 6071 Aldrans

Tel.: 0650 9359101

E-Mail: kay.cichini at gmail.com

Web: www.theBioBucket.blogspot.co.at<http://www.thebiobucket.blogspot.co.at/><http://www.theBioBucket.blogspot.co.at>
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