Problems:
1. A gentle reminder that the default has not been changed for saving
.RData in batch mode (as was promised).
2. The degrees of freedom for the null deviance in glm are wrong when
some observations are weighted out. This can give silly answers, for
example when applying anova. The number of weighted out observations
should be subtracted, as in other df calculations.
3. The null deviance itself is wrong in glm when an offset is used. It
can be smaller than that when variables are added to the model!
4. R gave a segmentation fault when I tried to fit a model with 49
factor levels in glm (using R -v4). All these glm problems were with
poisson.
5. R still does not read my environmental variables to set memory
size.
Suggestions:
1. d, p, q, and r functions for inverse Gauss and Laplace
distributions.
2. Add a fifth function for continuous distributions, the hazard
function, h. For example, ht <- function(...) dt(...)/(1-pt(...))
is the Student t hazard function.
For writing likelihood functions, these would be much faster in C than
R and some such as Weibull can be simplified.
3. Add the five functions for three parameter distributions such as
generalized F, extreme value, etc., Box-Cox,... (I have the densities,
cumulative, and hazard as R functions.)
4. Philippe Lambert and I have d and p functions working in R for the
four-parameter stable family by inverting the characteristic function
with a Fourier transform (requires C code). S-plus only has the r
function for stables.
Jim
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