🚀 go-pugleaf

In article <1992Oct30.135542.482@hnrc.tufts.edu>, jerry@hnrc.tufts.edu (Jerry Dallal) writes:

|> For those who have not yet been exposed to the problem, consider fitting
|> a quasi-independence model to an r*c table, i.e., a model that specifies
|> row/column independence over a subset of the table (example, type of crime
|> against campus.  If one of the crimes were auto theft, we would not expect 
|> a homogeneity of proportions model to extend to those campuses where no cars 
|> were allowed. For those campuses, structural 0's would be fitted to the 
|> auto-theft cells and we might asks whether a model of homogeneity of 
|> proportions fit the rest of the table.)

I have a related (proably naive) question:

I am doing spatial trend removal with a median polish. A grid is overlayed
on a 2-dimensional data set. Points are first linked to their nearest grid 
point (some grid points may have more than one point, others may have 
none--0's).  Then, medians are systematically removed from the rows and 
then columns of grid points. This is repeated until, within some tolerance 
level, the polish converges.

The reference that I am using suggests that, before I used the residuals
for anything, I should test for any "significant interation" (I think that's
the term he used). Can someone expound upon this (in private email if you
feel this is too trivial for sci.math.stat)? How do I test for it? What 
happens if I conclude that there is interaction (if this can be answered
generally---I assume that it depends on what I'm using the residuals for)?

The work that I allude to is appended.
-- 
James Darrell McCauley                Dept of Ag Engineering, Purdue Univ
internet: mccauley@ecn.purdue.edu     West Lafayette, Indiana 47907-1146, USA
bitnet: mccauley%ecn@purccvm          UUCP: pur-ee!mccauley

@Book{ cressie91,
  author =       "Noel A. C. Cressie",
  title =        "Statistics for Spatial Data",
  publisher =    "John Wiley \& Sons",
  year =         "1991",
  series =       "Wiley Series in Probability and Mathematical Statistics",
  address =      "New York, NY"
}