similar to: Making the plot window wider and using the predict function

Hello, The dataset "cats" contain information about the heart weight ("Hwt"), body weight ("Bwt") and gender ("Sex") of a group of 144 cats. I write the following piece of code: library(MASS)attach(cats)ratio <- Hwt/Bwtmale <- ratio[Sex == "M"]female <- ratio[Sex == "F"] My question is, when I look at the object

Hi: I am trying to create a dynamic latex table using \Sexpr{} but it's not evaluating it. I also tried the example below without Sweave and also fails. I have also copied the Sweave.sty to my working directory but nothing seems to work. Do I need to have certain package in order to run \Sexpr{}? \documentclass[a4paper]{article} \usepackage{C:/R/R-2.8.1/share/texmf/Sweave} \begin{document}

Hola Tengo un problema con Sweave y xtable, concretamente quiero cambiar el color según un criterio, creo que es más fácil explicar con los siguientes ejemplos, uno puede correr directamente porque es R, al siguiente código hay que guardarlo como Rnw, y luego puede ser corrido en R, donde seguramente al correr este se darán cuenta de mis dos problemas porque saltan al abrir el pdf que se obtiene

Full_Name: Soren Feodor Nielsen Version: 2.5.0 OS: linux-gnu Submission from: (NULL) (130.225.103.21) The print-out of xtable in the following example is wrong; instead of yielding the correct ci's for the second model it repeats the ci's from the first model. require(xtable) require(MASS) data(cats) b1<-lm(Hwt~Sex,cats) b2<-lm(Hwt~Sex+Bwt,cats)

Hi, I am a very occasional user of R, and will be grateful for some help in constructing a regression across groups. Here is an example: library(MASS) attach(cats) Sex[120:144]<-factor(TG) #Renaming some males to transgender, to create 3 groups, male, female and transgender out<-lm(Bwt~Sex/Hwt) #Gives me 3 separate linear regressions for groups M, F and TG What I now want to do

Dear All, For logistic regression models: is it possible to use validate (rms package) to compute bias-corrected AUC, but have variable selection with AIC use step (or stepAIC, from MASS), instead of fastbw? More details: I've been using the validate function (in the rms package, by Frank Harrell) to obtain, among other things, bootstrap bias-corrected estimates of the AUC, when variable

Dear List-Members, I have a problem with the function lqs() from package MASS. In some cases it produces different results for the same settings and needs a random seed to be set, in other cases not. I really cannot understand, why this happens. As well I do not understand what exactly you need the random seed for. Is it a starting point for iterations? Or do different results occur because of

I simply put, > NEVER=subset(infants$bwt,ISNO1) > UNTILPREGNANT=subset(infants$bwt, ISNO2) > ONCENOTNOW=subset(infants$bwt, ISNO3) and I wanna combine those three. I do it like ISNO=NEVER&UNTILPREGNANT&ONCENOTNOW and R tells me 1: In NEVER & UNTILPREGNANT : longer object length is not a multiple of shorter object length 2: In NEVER & UNTILPREGNANT & ONCENOTNOW

Hello all I have what seems like a simple question but have not been able to find an answer on the forum. I'm trying to define a function which involves regression models and a large number of covariates. I would like the function to accept any number of covariates and, ideally, I would like to be able to enter the covariates in a group (e.g. as a list) rather than individually. Is there any

Dear all, as a follow-up to the question asked on R-package-devel (see link below): Someone sent a package to CRAN with a few problems. There's more things wrong with the submission, but one thing that really caught my eye was the following error: Warning message: running command '"C:/PROGRA~1/R/R-33~1.2/bin/x64/R" CMD BATCH --vanilla "testthat.R"

Dear all, I'm a trainee statistician in a company and we'd like to understand svm mechanism, at first with simple examples. I use e1071 package and I have several questions. I'm working with data extracted from cats data (from R). My dataset corresponds to a completely separable case with a binary response variable ( Sex with 2 levels: F and M), two explanatory variables (Bwt

Bellow, follows a proposal for a new opensolaris project. Of course, this is open to change since I just wrote down some ideas I had months ago, while researching the topic as a graduate student in Computer Science, and since I''m not an opensolaris/ZFS expert at all. I would really appreciate any suggestion or comments. PROJECT PROPOSAL: ZFS Compression Algorithms. The main purpose of

Hi Stuart, I often use this small function standardize <- function(x) ( x - mean(x, na.rm=T) ) / sqrt(var(x, na.rm=T)) to standardize variables. You should be able to use this to do what you want by splitting the data frame into sections based on the factor level, using standardize() to create a new variable in each section, then paste the data frame back together. Something like: #

I'm trying to get code along the following lines to work: temp.name <- paste(TimePt,'df',sep='.') # invent a relevant name/symbol as a character string. assign(temp.name,IGF.df[IGF.df$TPt==TimePt,]) # this works. The relevant variable is now a data frame lm(b ~ Strain+BWt+PWt+PanPix, data=temp.name)) # this gives an error, namely Error in eval(predvars, data, env) : invalid

why doesn't llvm transfer bytecode to RTL form? I mean the cross-platform compile in GCC. Could LLVM do that? maybe tree-ssa also create the representation with a testual format if needed. is it very difficalt? I want to know. BWT: if we port pthread library to llvm, what do we consider at first? yueqiang Chris Lattner wrote: >On Wed, 24 Dec 2003, yue wrote: > > >>what

Dear R users, The nnet package includes the multinom method for the confint function. The R Help file (?confint) for the generic function in the stats package and the help files for the glm and nls methods in the MASS package indicate that one can use the "parm" argument as "a specification of which parameters are to be given confidence intervals, either a vector of numbers or

we work on distribute software development, most programs are wroten in java. but the running speed of that is slowly, it requires thread library to support, you know. Chris Lattner wrote: >On Wed, 24 Dec 2003, yue wrote: > > > >>why doesn't llvm transfer bytecode to RTL form? I mean the >>cross-platform compile in GCC. Could LLVM do that? >> >>

Hi. guys, I am trying to write my own Stochastic Gradient Ascent for logistic regression in R. But it seems that I am having convergence problem. Am I doing anything wrong, or just the data is off? Here is my code in R - lbw <- read.table("http://www.biostat.jhsph.edu/~ririzarr/Teaching/754/lbw.dat" , header=TRUE) attach(lbw) lbw[1:2,] low age lwt race smoke ptl ht ui ftv

On Wed, 24 Dec 2003, yue wrote: > why doesn't llvm transfer bytecode to RTL form? I mean the > cross-platform compile in GCC. Could LLVM do that? Sure, it would be a realtively straight-forward (but large and hard to get right, if you're not a GCC expert), project to build an LLVM front-end for GCC. This would allow you to effectively use LLVM as a mid-level representation for

I recently posted a question concerning an inconsistency of expand.grid in defining the reference level of the factors. > > expand.grid(x = c("b", "a"), y = c(1, 2))$x > [1] b a b a > Levels: b a # reference level is b > > expand.grid(x = c("b", "a"))$x > [1] b a > Levels: a b # reference level is a Thank you