similar to: Splitting a Vector

Hi! I'm having some issues on both conceptual and technical levels for selecting the right combination of variables for this model I'm working on. The basic, all inclusive form looks like lm(mic ~ B * D * S * U * V * ICU) Where mic, U, V, and ICU are numeric values and B D and S are factors with about 16, 16 and 2 levels respectively. In short, there's a ton of actual explanatory

Hi, I have some values in a list format generated by the following: Path_Number <- 0010 ID.Path <- formatC(0001:Path_Number, width=4, flag=0) # Make vector of ID's. No_of_Effectors <- sample(1:550, length(ID.Path), replace=TRUE) # Define Number of Effectors each individual gets. Effectors <- split(sample(1:10000, sum(No_of_Effectors), replace=TRUE), rep(ID.Path, No_of_Effectors))

>Wasn't our (lld/ELF's) position on debug info size that we should focus on providing a great split-dwarf workflow and not try go too far out of our way to deduplicate >or otherwise reduce debug info size inside LLD? I recall there being some patches that made linking of large debug binaries like 1.5GB+ clang faster, but we decided to >reject those changes because split-dwarf was

Hi R experts, I'm attempting to run Linear Discriminant Analysis using the lda function in the MASS package. I've got around 50 predictor variables and one response variable. My response variable has 5 numeric categories that represent different clusters of fish abundance data (clusters were developed using Bray-Curtis and NMDS), and my predictor variables are environmental variables that

Dear all: I try to analyse a dataset which contain one binary response variable and serveral predict variables, but multiple colinear problem exists in my dataset, some paper suggest that logistic regression for principle components is suit for these noise data, but i only find R can done principle component regression using "pls" package, is there any package that can do the task i

?But could not we for example do split dwarf, but for example do dedup of types ? I do not mean right now, but in a theory ? Best regards, George | Developer | Access Softek, Inc ________________________________ От: David Blaikie <dblaikie at gmail.com> Отправлено: 16 декабря 2017 г. 22:25 Кому: George Rimar Копия: Sean Silva; llvm-dev at lists.llvm.org; Rui Ueyama; Rafael Espindola Тема:

Hi, Can anybody tell me what the message below means and how to overcome it. Thanks, Neil Warning message: X matrix deemed to be singular; variable 2 in: coxph(Surv(age_at_death, death) ~ project$pluralgp + project$yrborn + ......... >

Is there a colinearty function implemented in R? I have tried help.search("colinearity") and help.search("collinearity") and have searched for "colinearity" and "collinearity" on http://www.rpad.org/Rpad/Rpad-refcard.pdf but with no success. Many thanks in advance, Peter Lauren.

I'm a little unclear about something. I want my Linux box to be the Local Browse Master -- so that the machine that's "on" all the time is the one that other computers look to. Is it correct that I want in my Global Settings: wins support = yes os level = 65 (or some higher number) And should my Windows XP workstations have the Linux box as the Wins Server? Or should I

Hi All, I have some questions on using library rpart. Given my data below, the plotcp gives me increasing 'xerrors' across different cp's with huge xstd (plot attached). What causes the problem or it's not a problem at all? I am thinking 'xerror's should be decreasing when 'cp' gets smaller. Also what the 'xstd' really tells us? If the error bars for

Hi, I have a data frame with 6563 observations. I can run a regression with loess using four explanatory variables. If I add a fifth, R crashes. There are no missings in the data, and if I run a regression with any four of the five explanatory variables, it works. Its only when I go from four to five that it crashes. This leads me to believe that it is not an obvious problem with the data,

I'm sorry to all those who are tired of seeing my email appear in need of help. But, I've never coded in any program before, so this has been a difficult process for me. Is there a simple function to test for colinearity in R? I'm running a logistic regression and a linear regression. Thanks for the help! [[alternative HTML version deleted]]

>Not quite sure what you mean by "on linker side" - but I guess you mean using linker features like comdats etc, rather than DWARF parsing/reassembly/etc. I mean that it probably not a good idea for external library. I feel it is much more convinent to do such proccessing in a linker. Linker do and knows much more about things like sections that are ICFed, eliminated, about COMDATs

On Sat, Dec 16, 2017 at 11:40 AM George Rimar <grimar at accesssoftek.com> wrote: > Or following workflow: > > Split dwarf is used to make linker to proccess less, like relocations, > right ? > Partly, though the main motivation as far as I know, was to have to provide fewer bytes to the linker at all. That's why something like Apple's scheme (leave the debug info in

Hi group: I have a data set, which has severe colinearity problem. While running linear regression in R and SPSS, I got different models. I am wondering if somebody knows how to make the two software output the same results. (I guess the way R and SPSS handling singularity is different, which leads to different models.) Thanks. [[alternative HTML version deleted]]

Dear R users, When fitting a lme() object (from the nlme library), is it possible to test interactions *before* main effects? As I understand, R conventionally re-orders all terms such that highest-order interactions come last - but I??d like to know if it??s possible (and sensible) to change this ordering of terms. I??ve tried the terms() command (from aov) but I don??t know if something

Hi, Is there any facility in R to perform a stepwise process on a model, which will remove any highly-correlated explanatory variables? I am told there is in SPSS. I have a large number of variables (some correlated), which I would like to just chuck in to a model and perform stepwise and see what comes out the other end, to give me an idea perhaps as to which variables I should focus on. Thanks

Hello,<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" /><o:p></o:p> while doing my thesis in habitat modelling I´ve come across a problem with interaction terms. My question concerns the usage of interaction terms for linear regression modelling with R. If an interaction-term (predictor) is chosen for a multiple model, then, according to

Dear all users, I"d like to ask you how to make decision about colinearity among categorical independent variables when the model is multinomial logistic regression. Any help is appreciated, Niklas [[alternative HTML version deleted]]

Dear R users, I want to estimate a Cox PH model with time-dependent covariates so I am using a counting process format with the following formula: Surv(data$start, data$stop, data$event.time) ~ cluster(data$id) + G1 + G2 + G3 + G4 + G5 +G6 Gs represent a B-spline basis functions so they sum to 1 and I can't estimate the model as is without getting the last coefficient to be NA, which