On 29.04.2011 18:29, "Biedermann, Jürgen" wrote:
Hi there,
I have the problem, that I'm not able to reproduce the SPSS residual
statistics (dfbeta and cook's distance) with a simple binary logistic
regression model obtained in R via the glm-function.
I tried the following:
fit <- glm(y ~ x1 + x2 + x3, data, family=binomial)
cooks.distance(fit)#
Just type stats::cooks.distance.glm and see the definition in R yourself:
function (model, infl = influence(model, do.coef = FALSE), res =
infl$pear.res, dispersion = summary(model)$dispersion, hat = infl$hat, ...)
{
p <- model$rank
res <- (res/(1 - hat))^2 * hat/(dispersion * p)
res[is.infinite(res)] <- NaN
res
}
<environment: namespace:stats>
Now you can digg yourself further on. I do not know how to find the
actually used algorithm from SPSS, hence I cannot tell what is different.
Uwe Ligges
dfbetas(fit)
When i compare the returned values with the values that I get in SPSS,
they are different, although the same model is calculated (the
coefficients are the same etc.)
It seems that different calculation-formulas are used for cooks.distance
and dfbetas in SPSS compared to R.
Unfortunately I didn't find out, what's the difference in the
calculation and how I could get R to calculate me the same statistics
that SPSS uses.
Or is this an unknown SPSS bug?
Greetings
Jürgen
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and provide commented, minimal, self-contained, reproducible code.
