Many thanks - this was very helpful!

Regards, Kay
Am 28.01.2013 13:19 schrieb "Achim Zeileis" <Achim.Zeileis@uibk.ac.at>:

> 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@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@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
>>>>>>
>>>>>> Tel.: 0650 9359101
>>>>>>
>>>>>> E-Mail: kay.cichini@gmail.com
>>>>>>
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>>>> --
>>>>
>>>> Kay Cichini, MSc Biol
>>>>
>>>> Grubenweg 22, 6071 Aldrans
>>>>
>>>> Tel.: 0650 9359101
>>>>
>>>> E-Mail: kay.cichini@gmail.com
>>>>
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>>>>
>>>>
>>>>
>>
>> --
>>
>> Kay Cichini, MSc Biol
>>
>> Grubenweg 22, 6071 Aldrans
>>
>> Tel.: 0650 9359101
>>
>> E-Mail: kay.cichini@gmail.com
>>
>> Web: www.theBioBucket.blogspot.co.**at<http://www.theBioBucket.blogspot.co.at>
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