Your model must be singular. Consider the following example:
> dat0 <- data.frame(x1=1:3, x2=1:3, y=rnorm(3))
> coef(lm(y~x1+x2, dat0))
(Intercept) x1 x2
-3.714515 1.487876 NA
Since x1 = x2, ordinarly least squares cannot produce separate estimates
for coefficients for x1 and x2. In such situations, "lm" drops terms
out of the model until it gets a model that is estimable.
In S-Plus, "lm" will give an error message and no answer to this
problem, unless you add ""singular.ok=TRUE". I prefer the R
default.
Spencer Graves
Ernesto Jardim wrote:> Hi
>
> I'm doing a regression analysis with dummy variables and I'm
getting NA
> for some coefficients. I've inspected residuals, leverage effects and
> Cook's distance and it seems ok.
>
> Can someone explains what can cause this problem ?
>
> Thanks
>
> EJ
>
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