search for: unpooled

...subgroups (divided according to a factor). I was wondering if anyone can help me making waldtest recognize the nesting. Here's the lines I run: (BTW, I try to use robust standard errors because what I normally use (glm.binomial.disp) to correct for overdispersion does not converge for the unpooled model. But this is another story....) # poolability for leva.fin03.d # pooled model inv.log.leva.base = glm(mix.au.bin ~ cat.gap.tot + leva.fin03.d + ... + sud0nord1, data = inv.sub.au, family = binomial, maxit = 1000) # I deleted almost all variables to make the line more readable # overdisper...

Is there a formal way to prove the need of a mixed model, apart from e.g. comparing the intervals estimated by lmList fit? For example, should I compare (with AIC ML?) a model with seperately (unpooled) estimated fixed slopes (i.e.using an index for each group) with a model that treats this parameter as a random effect (both models treat the remaining parameters as random)? Thank you!

...question, so please bear with me i'm currently applying the chow test of poolability in fact i'm working with panel N=17 T=5 , and my model looks like this : Yit= a0+B1X1+B2X2+B3X3+B4X4+eit My question is the following when i'm Testing for the equality of the coefficients of the unpooled data (the last stage) many of my constraints get dropped, this indeed impact the degree of freedom of my F statistic , and would like to understand the reason? is this because the time dimension of my panel is too small? or because the number of my constraints is too high? Any hint will be highly...

...also split data frames. This is a useful function, often used in combination with lapply to avoid for() loops. See help(split) and help(lapply) for further details.) -------------------------------------------------- Section 10.1 The current twosam is for a pooled-variance t-statistic. Give the unpooled version instead (for consistency with t.test, and because it is better statistical practice). Give initial comments in functions twosam: # compute a two-sample t-statistic for the difference in means and bslash: # Compute least-squares regression coefficients (X'X)^{-}(X'y) Add initial...