On May 12, 2009, at 3:50 AM, utkarshsinghal wrote:
Hi All,
Can anybody explain why the following three ways of extracting
residuals from a glm object are giving me different outputs:
> idv = runif(1000,0,1)
> dv = rbinom(1000,1,0.5)
> d = data.frame(idv,dv)
> fit = glm(dv~idv, data=d, family=binomial)
> head(residuals(fit))
1 2 3 4 5 6
1.216862 -1.161059 -1.156795 1.204759 -1.141068 1.201437
> head(fit$residuals)
1 2 3 4 5 6
2.096724 -1.962126 -1.952454 2.066224 -1.917492 2.057981
> head(d$dv-fit$fitted.values)
1 2 3 4 5 6
0.5230655 -0.4903489 -0.4878241 0.5160253 -0.4784855 0.5140869
set.seed(1)
idv <- runif(1000, 0, 1)
d <- data.frame(idv, dv)
fit <- glm(dv ~ idv, data = d, family = binomial)
> head(fit$residuals)
1 2 3 4 5 6
-1.957016 -1.960477 -1.967029 -1.978074 -1.954949 -1.977749
> head(residuals(fit, type = "working"))
1 2 3 4 5 6
-1.957016 -1.960477 -1.967029 -1.978074 -1.954949 -1.977749
> head(d$dv - fit$fitted.values)
1 2 3 4 5 6
-0.4890179 -0.4899201 -0.4916190 -0.4944577 -0.4884778 -0.4943746
> head(residuals(fit, type = "response"))
1 2 3 4 5 6
-0.4890179 -0.4899201 -0.4916190 -0.4944577 -0.4884778 -0.4943746
See ?glm and ?residuals.glm and read the information there regarding
the type of residuals stored in the glm model object as opposed to the
multiple types of residuals that can be returned by residuals.glm().
See the references in ?residuals.glm for more information as per the
Details section therein.
HTH,
Marc Schwartz
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