On 13.12.2011 04:36, wim nursal wrote:
Dear Uwe and David,
Yes, definitely i was wrong. The expression in R should be:
glm(cbind(FD, 12 - FD) ~ Fsize, family=binomial, data=subFS)
Call: glm(formula = cbind(FD, 12 - FD) ~ Fsize, family = binomial,
data = subFS)
Coefficients:
Dear statistician experts,
Sorry if this is a trivial question, or the old same question (i don't know
what is the efficient key word for this issue).
In order to understand the calculation of parameter of logistic regression,
I did an exercise through spreadsheet following the procedural
1. The formula you used is not for a logistic but an ordinal regression
(since you are using the default gaussian family rather than
family=binomial or whatever.
2. R (nor any other software) can deal with perfect separation (nor
quasi-separation) of classes, since the problem is not well
On Dec 12, 2011, at 3:51 PM, Uwe Ligges wrote:
1. The formula you used is not for a logistic but an ordinal
regression (since you are using the default gaussian family rather
than family=binomial or whatever.
this this then produce one version of the Armitage linear test of
trend?
Dear Uwe and David,
Yes, definitely i was wrong. The expression in R should be:
glm(cbind(FD, 12 - FD) ~ Fsize, family=binomial, data=subFS)
Call: glm(formula = cbind(FD, 12 - FD) ~ Fsize, family = binomial,
data = subFS)
Coefficients:
(Intercept)Fsize
0.6381
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