similar to: MASS Book Exercise

Hi, I was just trying out an example on Page 247 of the MASS (Modern Applied Statistics with S-plus) book and saw the function "negexp.ival". It says it is supplied in the MASS library, however when I load the library in R and typed: negexp.ival it says: Error: Object "negexp.ival" not found Does it mean it only appears in the MASS library for S-plus?

All the usual MASS data sets are nicely sitting in D:\Program Files\rw1022\library\MASS\data on my laptop and yet: > library(MASS) > data(wtloss) Warning message: Data set `wtloss' not found in: data(wtloss) what could be wrong? I'm running 1.2.2 on Win98. Murray Jorgensen -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list --

The following warning message occurs when running the nls on some data: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Warning message: In is.na(wts) : is.na() applied to non-(list or vector) of type 'NULL' ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ and I would like to know what is causing it and how I can fix it. As an example, the following, from Venables and Ripley, which

I'm running R 1.2.2 under windows 98 on a Pentium 133 laptop. I can't seem to retrieve the package MASS data sets: > library(MASS) > data(wtloss) Warning message: Data set `wtloss' not found in: data(wtloss) > data(abbey) Warning message: Data set `abbey' not found in: data(abbey) And yet all the .rda files for the MASS datasets are in D:\Program

I'm trying to use nls() to fit an exponential decay with an unknown offset in the time (independent variable). (Perhaps this is inherently very difficult?). > decay.pl <- nls (amp ~ expn(b0,b1,tau,t0,t), data = decay, + start = c(b0=1, b1=7.5, tau=3.5, t0=0.1), trace=T) Error in nlsModel(formula, mf, start) : singular gradient matrix at initial parameter estimates

I've been working on some basic library call optimizations, the SimplifyLibCalls pass (lib/Transforms/IPO/SimplifyLibCalls.cpp). Tonight I conjured up a list of the potential libcall simplifications that could be done. There's a lot of them. I could use some help if anyone wants to pitch in. The individual optimizations are self-contained and fairly straight forward to write. They range

After I spend some time working with the function attribute* deduction pass** [1,3], I would like to propose a "proper" organization***. Why? Because we do not derive nearly as many attributes as we could****, while we do maintain various (separate and diffently organized) "data-flow-like analyses" to do so. What else? I propose a single optimistic data-flow

Stop using extra underscores on asm and volatiles, that is just silly. Also, make lgdt/lidt/sgdt/sldt explicitly "l". Patch-base: 2.6.13-rc5-mm1 Patch-keys: i386 desc cleanup Signed-off-by: Zachary Amsden <zach@vmware.com> Index: linux-2.6.13/include/asm-i386/mach-default/mach_desc.h =================================================================== ---

Stop using extra underscores on asm and volatiles, that is just silly. Also, make lgdt/lidt/sgdt/sldt explicitly "l". Patch-base: 2.6.13-rc5-mm1 Patch-keys: i386 desc cleanup Signed-off-by: Zachary Amsden <zach@vmware.com> Index: linux-2.6.13/include/asm-i386/mach-default/mach_desc.h =================================================================== ---

Move I/O instruction building to the sub-arch layer. Some very crafty but esoteric macros are used here to get optimized native instructions for port I/O in Linux be writing raw instruction strings. Adding a wrapper layer here is fairly easy, and makes the full range of I/O instructions available to the VMI interface. Also, slowing down I/O is not a useful operation in a VM, so there is a VMI

Move I/O instruction building to the sub-arch layer. Some very crafty but esoteric macros are used here to get optimized native instructions for port I/O in Linux be writing raw instruction strings. Adding a wrapper layer here is fairly easy, and makes the full range of I/O instructions available to the VMI interface. Also, slowing down I/O is not a useful operation in a VM, so there is a VMI

plain text document attachment (xx-paravirt-desc-header.patch) Update the descriptors for an interface with paravirt ops Signed-off-by: Steven Rostedt srostedt@redhat.com Signed-off-by: Glauber de Oliveira Costa <gcosta@redhat.com> Index: clean-start/include/asm-x86_64/desc.h =================================================================== ---

plain text document attachment (xx-paravirt-desc-header.patch) Update the descriptors for an interface with paravirt ops Signed-off-by: Steven Rostedt srostedt@redhat.com Signed-off-by: Glauber de Oliveira Costa <gcosta@redhat.com> Index: clean-start/include/asm-x86_64/desc.h =================================================================== ---

> I''m not mad keen on propagating these sorts of asm atomic operations throughout > our code base. Other options would be: gcc has atomic builtins to do this kind of work. I don''t know about arm, but they do the job in x86 http://gcc.gnu.org/onlinedocs/gcc-4.1.1/gcc/Atomic-Builtins.html so atomic_inc(val) -> __sync_fetch_and_add(val, 1) and likewise for dec/sub Andres

I have made a minor hack to "legend" (in R 1.0.0, but I didn't notice any changes to legend in the 1.0.1 NEWS) to allow the legend to be formatted in multiple columns, or horizontally (number of columns <- number of legend items). (I find this helpful when I have lots of legend items and not a lot of vertical space to squeeze the legend into.) (Another hack I've considered

All, If we can speculatively execute a load instruction, why isn’t it safe to hoist it out by promoting it to a scalar in LICM pass? There is a comment in LICM pass that if a load/store is conditional then it is not safe because it would break the LLVM concurrency model (See commit 73bfa4a). It has an IR test for checking this in test/Transforms/LICM/scalar-promote-memmodel.ll However, I have

How to use R to solve the optimisaton problem Minimize: ?*w^T*omega*w+mu^T*w+c^T(w-w0) for w>w0 long position ?*w^T*omega*w+mu^T*w-c^T(w-w0) for w<w0 short position W: is the update weight of portfolio Wo is the initial weight of portfolio Omega is the variance covariance matrix mu is the vector of return rate of stocks in the portfolio C is the vector coefficient of transaction cost

Dear all, I am trying to fit a non linear regression model to time series data. If I do this: reg.logis = nls(myVar~SSlogis(myTime,Asym,xmid,scal)) I get this error message (translated to English from French): Erreur in nls(y ~ 1/(1 + exp((xmid - x)/scal)), data = xy, start = list(xmid = aux[1], : le pas 0.000488281 became inferior to 'minFactor' of 0.000976562 I then tried to set

I  am trying to learn nls using a simple simulation. I assumed that the binomial prob varies linearly as 0.2 + 0.3*x in  x {0,1}, and the objective is to recover the known parameters a=0.2, b=0.3 ..data frame d has 1000 rows... d$x<-runif(0,1)               d$y<-rbinom(1000,1,0.2+0.3*d$x)  table(d$y,cut(d$x,breaks=5));   (-0.000585,0.199] (0.199,0.399] (0.399,0.599] (0.599,0.799]

i386 Transparent paravirtualization subarch patch #5 This change encapsulates descriptor and task register management. Diffs against: 2.6.13-rc4-mm1 Signed-off-by: Zachary Amsden <zach@vmware.com> Index: linux-2.6.13/include/asm-i386/desc.h =================================================================== --- linux-2.6.13.orig/include/asm-i386/desc.h 2005-08-08 17:31:59.000000000 -0700