[R] Non-parametric four-way interactions?
Frank E Harrell Jr
f.harrell at vanderbilt.edu
Thu Jul 27 14:29:26 CEST 2006
Paul Smith wrote:
> On 7/27/06, Frank E Harrell Jr <f.harrell at vanderbilt.edu> wrote:
>>> I am trying to study four-way interactions in an ANOVA problem.
>>> However, qqnorm+qqline result
>>> (at http://phhs80.googlepages.com/qqnorm.png)
>>> is not promising regarding the normality of data (960 observations).
>>> The result of Shapiro-Wilk test is also not encouraging:
>>> W = 0.9174, p-value < 2.2e-16
>>> (I am aware of the fact that normality tests tend to reject normality
>>> for large samples.)
>>> By the way, the histogram is at:
>>> To circumvent the problem, I looked for non-parametric tests, but I
>>> found nothing, but the article:
>>> Finally, my question is: has R got implemented functions to use
>>> non-parametric tests to avoid the fulfillment of the normality
>>> assumption required to study four-way interactions?
>> Yes, although I seldom want to look at 4th order interactions. You can
>> fit a proportional odds model for an ordinal response which is a
>> generalization of the Wilcoxon/Kruskal-Wallis approach, and allows one
>> to have N-1 intercepts in the model when there are N data points (i.e.,
>> it works even with no ties in the data). However if N is large the
>> matrix operations will be prohibitive and you might reduce Y to 100-tile
>> groups. The PO model uses only the ranks of Y so is monotonic
>> transformation invariant.
>> library(Design) # also requires library(Hmisc)
>> f <- lrm(y ~ a*b*c*d)
>> Also see the polr function in VR
> Thanks, Frank. It is very encouraging to learn that, even without
> normality, I can still study my four-way interactions. I am also aware
> of transformations that may work in some non-normal cases, and I have
> tried some of them, but with no success.
> I am not familiar with the solutions that you suggest, and I would
> like to learn how they work theoretically, in some book or on the
> Internet. In particular, I would like to see, regarding power, how the
> non-parametric suggested approach compares with the classical ANOVA
> approach. Could you please indicate some references to help me with
> Again, thanks in advance.
Tony Lachenbruch has written some about this, also see my book (it's web
page is biostat.mc.vanderbilt.edu/rms). I don't know about the power of
interaction tests, but for main effect tests in the absence of
interaction, the Wilcoxon test (a special case of PO model) has
efficiency of 3/pi compared to the t-test if normality holds.
Other approaches: Cox PH model, avas, ace. My book covers these too.
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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