[R] Testing continuous zero-inflated response

[Achim Zeileis]

On Sun, 27 Jan 2013, Kay Cichini wrote:

> That said,
>
>> wilcox_test(x ~ factor(y), distribution = "exact")
>
> or the same with oneway_test, i.e would be ok?

Yep, exactly.

And you could also look at chisq_test(factor(x > 0) ~ factor(y), 
distribtuion = approximate(9999)) or something like that. Or of course 
Fisher's exact test.

Best,
Z

>
> 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
>>>>>
>>>>> --
>>>>>
>>>>> Kay Cichini, MSc Biol
>>>>>
>>>>> Grubenweg 22, 6071 Aldrans
>>>>>
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>>>>>
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>>>
>>> Kay Cichini, MSc Biol
>>>
>>> Grubenweg 22, 6071 Aldrans
>>>
>>> Tel.: 0650 9359101
>>>
>>> E-Mail: kay.cichini at gmail.com
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>
>
> -- 
>
> Kay Cichini, MSc Biol
>
> 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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