similar to: binequality - Problems with fitFunc()

my understanding of `eval' behaviour is obviously incomplete. my question: usually `eval(expr)' and `eval(expr, envir=parent.frame())' should be identical. why does the last `eval' (yielding `r3') in this code _not_ evaluate in the local environment of function `f' but rather in the global environment (if `f' is called from there)?

dear list, I stumbled over the follwing strange behaviour/error when using `nls' which I'm tempted (despite the implied "dangers") to call a bug: I've written a driver for `nls' which allows specifying the model and the data vectors using arbitrary symbols. these are internally mapped to consistent names, which poses a slight complication when using `deriv' to

I initially thought, this should better be posted to r-devel but alas! no response. so I try it here. sory for the lengthy explanation but it seems unavoidable. to quickly see the problem simply copy the litte example below and execute f(n=5) which crashes. called with n != 5 (and of course n>3 since there are 3 parameters in the model...) everything is as it should be. in detail:

Dear List, I'm using glmulti for model averaging in R. There are ~10 variables in my model, making exhaustive screening impractical - I therefore need to use the genetic algorithm (GA) (call: method = "g"). I need to include random effects so I'm using glmulti as a wrapper for lme4. Methods for doing this are available here

Dear All, wonder if you have some thoughts on running the with() function (and perhaps including the pool() function to get the results?) in glmulti? In other words, how to run glmulti with a data set that is produced by mice()? publicly available code: data <- airquality data[4:10,3] <- rep(NA,7) data[1:5,4] <- NA data <- data[-c(5,6)] library(mice) library(glmulti) the following

Dear all, I have a question about the glmulti package. I want to include some variables in all models. To that end I applied the wrapper function as shown in the examples (http://www.inside-r.org/packages/cran/glmulti/docs/glmulti). To include the variable "Geslacht" in all models: > glm.redefined = function(formula, data, always="", ...)

Dear List, I am using both the clm() and clmm() functions from the R package 'ordinal'. I am fitting an ordinal dependent variable with 5 categories to 9 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and the

To all, I'm working with code below to produce a map with station data plotted in points, but right now I'm having trouble with the mapping portion of this code states <- data.frame(map("state", plot=FALSE,xlim= c(-85,-75),ylim=c(33,37))[c("x","y")]) usamap<- ggplot(states)+geom_path(aes(x,y)) usamap When I plot this the problem is that the bounds of

Hey everyone, Sorry for yet another simple question but hopefully it makes whoever comes up with the answer feel good about helping others. I would like to simply plot the following two sets of data in a line graph. The one set is an observed set of points and the latter is the predicted. I have looked through the documentation (which makes any graphing very complicated to me) but i havent

On 7/31/13 4:47 PM, Shuxin Yang wrote: > > On 7/31/13 4:30 PM, Tobias Grosser wrote: >> On 07/30/2013 09:44 PM, Chris Lattner wrote: >>> >>> On Jul 30, 2013, at 10:19 AM, Shuxin Yang <shuxin.llvm at gmail.com> >>> wrote: >>> >>>> The pro for running LICM early is that it may move big redundant >>>> stuff out of loop

On 7/31/13 4:30 PM, Tobias Grosser wrote: > On 07/30/2013 09:44 PM, Chris Lattner wrote: >> >> On Jul 30, 2013, at 10:19 AM, Shuxin Yang <shuxin.llvm at gmail.com> wrote: >> >>> The pro for running LICM early is that it may move big redundant >>> stuff out of loop nest. You never know >>> how big it is. In case you are lucky , you can move

On 07/30/2013 09:44 PM, Chris Lattner wrote: > > On Jul 30, 2013, at 10:19 AM, Shuxin Yang <shuxin.llvm at gmail.com> wrote: > >> The pro for running LICM early is that it may move big redundant stuff out of loop nest. You never know >> how big it is. In case you are lucky , you can move lot of stuff out of >> loop, the loop may become much smaller and hence

>> I'm talking about perfect loop nests, as in the classical fortran loop >> transformation sense. > > Most nest optimizations only apply to perfect nests. Each such > optimization could try to "fix" the nest for its own purposes, but it > would be a lot of duplicated effort. If each L.N.O pass have to fix by itself, I would say this LNO component is

Hi, I'm playing around with gamlss and don't entirely understand the sigma result from an attempted lognormal fit. In the example below, I've created lognormal data with mu=10 and sigma=2. When I try a gamlss fit, I get an estimated mu=9.947 and sigma=0.69 The mu estimate seems in the ballpark, but sigma is very low. I get similar results on repeated trials and with Normal and

Hi I have an asterisk box with digium hardware connected to a Siemens EWSD version 15 using a crossed E1 cable. The asterisk is serving as a h323 media gateway. When i start asterisk, the Siemens gives me this alarm: REPORTES GENERADOS EN LA EWSD AES/V15SBOL/BOLCBK1V51327079/013 05-06-06 11:25:38 7773 3062/03728 HF.ARCHIVE-80040

On 7/31/2013 6:30 PM, Tobias Grosser wrote: > > I can see us following Andrews > suggestion to disable LICM in case a LNO is run and having the LNO > schedule an additional set of cleanup passes later on. The way I was thinking about it is that LICM could be optionally added to the preparation steps, something like requiring loop-closed SSA form for certain transformations. -K --

On 7/30/13 7:35 AM, Krzysztof Parzyszek wrote: > On 7/29/2013 6:28 PM, Andrew Trick wrote: >> >> You mean that LICM and Unswitching should be left for later? For the >> purpose of exposing scalar optimizations, I'm not sure I agree with >> that but I'd be interested in examples. > > Optimizations like LICM, and unswitching can potentially damage >

Hi, I'm a bit puzzled by the gamlss fitting of exponential and lognormal data. Gamlss seems to think that exponentially distributed data fits better with a lognormal distribution, and vice versa. For example, X <- rexp(1000) Gexp <- gamlss(X~1,family=EXP) # X~1 is X tilde 1 GAMLSS-RS iteration 1: Global Deviance = 2037.825 GAMLSS-RS iteration 2: Global Deviance = 2037.825 Glno

On 04/17/2013 09:04 PM, Sebastian Pop wrote: > Tobias Grosser wrote: >> As said before, we could probably add it in between those two passes: >> >> MPM.add(createReassociatePass()); // Reassociate expressions >> + addExtensionsToPM(EP_LoopOptimizerStart, MPM); >> MPM.add(createLoopRotatePass()); // Rotate Loop > > As this is in the middle of other

On 04/17/2013 08:37 PM, Sebastian Pop wrote: > Tobias Grosser wrote: >> On 04/17/2013 05:53 PM, Sebastian Pop wrote: >>> Hi, >>> >>> polly is run very early and schedules the following passes before it runs: >>> >>> /// @brief Schedule a set of canonicalization passes to prepare for Polly >>> /// >>> /// The set of