On Aug 20, 2011, at 12:57 PM, peter dalgaard wrote:
On Aug 20, 2011, at 18:04 , Stephen Davies wrote:
As for "$stdres," that would be wonderful, but
as you can see from the above list of attributes, it's not one of
the 8
returned. What am I missing?
An upgrade, most likely.
Whoosh. Sometimes I am simply clueless. I didn't notice that 'stdres'
was missing from the names in Stephen's output. Laura Thompson has a
very nice R/S accompaniment to Agresti's "Categorical Data Analysis"
text and she shows how to adjust the Pearson residuals to make them
"standardized". What follows is directly from pages 37-38 of her work:
#--------------------------------------------------#
resid.pear <- residuals(fit.glm, type = "pearson")
Note that the sum of the squared Pearson residuals equals the Pearson
chi-squared statistic:
sum(resid.pear^2)
[1] 69.11429
To get the standardized residuals, just modify resid.pear according to
the formula on p. 81 of Agresti.
ni<-rowSums(table.3.2.array) # row sums
nj<-colSums(table.3.2.array) # column sums
n<-sum(table.3.2.array) # total sample size
resid.pear.mat<-matrix(resid.pear, nc=3, byrow=T,
dimnames=list(c("<HS","HS or JH",
"Bachelor or Grad"),c("Fund", "Mod", "Lib")))
n*resid.pear.mat/sqrt(outer(n-ni,n-nj,"*") ) # standardized Pearson
residuals
Fund Mod Lib
<HS 4.534062 -2.5520482 -1.941537
HS or JH 2.552988 1.2859745 -3.994669
Bachelor or Grad -6.806638 0.7007539 6.250329
#--------------------------------------------------#
You can also look at the code (once you upgrade) and the method in R
is quite similar, although the R codes calcualtes the stdres values
separately rather than adjusting the Pearson residuals
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
David Winsemius, MD
West Hartford, CT
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