Testing continuous zero-inflated response
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 at gmail.com Web: www.theBioBucket.blogspot.co.at<http://www.thebiobucket.blogspot.co.at/><http://www.theBioBucket.blogspot.co.at> -- [[alternative HTML version deleted]]
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