[Rd] Type: Frontend ignored in 'Bundle' packages
A few weeks ago I started a thread expressing the desire to expand 'R CMD INSTALL' to also provide a hook for a post-build install step. A Gmane-base view is here http://thread.gmane.org/gmane.comp.lang.r.devel/16827 but it misses at least one further 'in support' message by Greg Warnes. There has be no follow-up from R Core. Now, I tried to code around this limitation by - switching to 'Bundle'-style packaging - using the 'Type: Frontend' in one part that the other part would depend upon - and using a normal package structure in another But 'R CMD check' fails to recognise 'Type: Frontend' in bundle packages, and tries to treat it as a normal package, and fails. So I cannot successfully run 'R CMD check' on the whole, and hence could not upload to CRAN. Is this something that should get fixed in the R tools? Shall I look into a patch? That said, I would still like to see a 'make install'-alike functionality in the R CMD INSTALL process. Could this be revisited? Regards, Dirk -- Three out of two people have difficulties with fractions. __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
[Rd] matching predictors and dummies
I am trying to make a little web interface for the lm() function. It calculates both anova F-tests and parameters and returns it in a nice table. However, I have a problem with matching the Anova predictors with the regression coefficients: For numeric predictors there is no problem: the coefficients have the same names as the predictors. However, when a factor IV is specified, lm() automatically converts this factor to dummy variables, which (of course) have different names than the orriginal predictor. The lm model that is returned contains a seperate parameter for every dummy variable. Then when you use anova(lm.model) the function seems to know which of the parameters are dummies of one and the same factor, and takes these together in the anova-test. The anova() function returns the variance explained by the orriginal factor, which are all dummies. It does not show the seperate dummy variables anymore. Of course, this is exactly what you want in an analysis of variance. My question is: where in the lm or glm object is stored which of the parameters are dummies of the same factor? The only thing i could think of was using lm.model$xlevels, however manipulating these names in the lm-model did not confuse anova() at all, so I guess there is a better way. An additional question is if it is possible to specify the names of the dummy variables that lm/glm creates when a factor is specified as IV? -- View this message in context: http://www.nabble.com/matching-predictors-and-dummies-tp18405023p18405023.html Sent from the R devel mailing list archive at Nabble.com. __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
Re: [Rd] Suggestion: 20% speed up of which() with two-character mod
On Thu, 10 Jul 2008, Henrik Bengtsson wrote:
Hi,
by replacing 'll' with 'wh' in the source code for base::which() one
gets ~20% speed up for *named logical vectors*.
The amount of speedup depends on how sparse the TRUE values are.
When the proportion of TRUEs gets small the speedup is more than twofold
on my macbook. For high proportions of TRUE, the speedup is more like the
20% you cite.
HTH,
Chuck
CURRENT CODE:
which <- function(x, arr.ind = FALSE)
{
if(!is.logical(x))
stop("argument to 'which' is not logical")
wh <- seq_along(x)[ll <- x & !is.na(x)]
m <- length(wh)
dl <- dim(x)
if (is.null(dl) || !arr.ind) {
names(wh) <- names(x)[ll]
}
...
wh;
}
SUGGESTED CODE: (Remove 'll' and use 'wh')
which2 <- function(x, arr.ind = FALSE)
{
if(!is.logical(x))
stop("argument to 'which' is not logical")
wh <- seq_along(x)[x & !is.na(x)]
m <- length(wh)
dl <- dim(x)
if (is.null(dl) || !arr.ind) {
names(wh) <- names(x)[wh]
}
...
wh;
}
That's all.
BENCHMARKING:
# To measure both in same environment
which1 <- base::which;
environment(which1) <- globalenv(); # Needed?
N <- 1e6;
set.seed(0xbeef);
x <- sample(c(TRUE, FALSE), size=N, replace=TRUE);
names(x) <- seq_along(x);
B <- 10;
t1 <- system.time({ for (bb in 1:B) idxs1 <- which1(x); });
t2 <- system.time({ for (bb in 1:B) idxs2 <- which2(x); });
stopifnot(identical(idxs1, idxs2));
print(t1/t2);
# Fair benchmarking
t2 <- system.time({ for (bb in 1:B) idxs2 <- which2(x); });
t1 <- system.time({ for (bb in 1:B) idxs1 <- which1(x); });
print(t1/t2);
## usersystem elapsed
## 1.283186 1.052632 1.25
You get similar results if you put for loop outside the system.time()
call (and sum up the timings).
Cheers
Henrik
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Charles C. Berry(858) 534-2098
Dept of Family/Preventive Medicine
E mailto:[EMAIL PROTECTED] UC San Diego
http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901
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Re: [Rd] matching predictors and dummies
On Fri, 11 Jul 2008, Jeroen Ooms wrote: I am trying to make a little web interface for the lm() function. It calculates both anova F-tests and parameters and returns it in a nice table. However, I have a problem with matching the Anova predictors with the regression coefficients: For numeric predictors there is no problem: the coefficients have the same names as the predictors. However, when a factor IV is specified, lm() automatically converts this factor to dummy variables, which (of course) have different names than the orriginal predictor. The lm model that is returned contains a seperate parameter for every dummy variable. Then when you use anova(lm.model) the function seems to know which of the parameters are dummies of one and the same factor, and takes these together in the anova-test. The anova() function returns the variance explained by the orriginal factor, which are all dummies. It does not show the seperate dummy variables anymore. Of course, this is exactly what you want in an analysis of variance. My question is: where in the lm or glm object is stored which of the parameters are dummies of the same factor? The only thing i could think of was using lm.model$xlevels, however manipulating these names in the lm-model did not confuse anova() at all, so I guess there is a better way. See ?terms ?terms.object and run example( terms.object ) or something like terms( lm( Ozone ~ Temp + factor(Month), airquality ) ) An additional question is if it is possible to specify the names of the dummy variables that lm/glm creates when a factor is specified as IV? -- I'm guessing a custom contrast function would do this. Have a look at ?contrasts page( contr.treatment, 'print' ) Or just hack the names attribute of the relevant pieces in the object returned by lm/glm. You do know to use str(), right? HTH, Chuck View this message in context: http://www.nabble.com/matching-predictors-and-dummies-tp18405023p18405023.html Sent from the R devel mailing list archive at Nabble.com. __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel Charles C. Berry(858) 534-2098 Dept of Family/Preventive Medicine E mailto:[EMAIL PROTECTED] UC San Diego http://famprevmed.ucsd.edu/faculty/cberry/ La Jolla, San Diego 92093-0901 __ R-devel@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
