similar to: Hidden information in an object

Hello, I am new to R and have a couple of questions. My data set contains the variables "Bwt" and "Hwt", which are bodyweight and heartweight, respectively, of a group of cats. With the following code, I am making two plots, both to be viewed in the same plot window in R: library(MASS) maleData <- subset(cats, Sex == "M") linreg0 <- lm(maleData$Hwt ~

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 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

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

R developers, 2 things: a bug in model.frame and a question about setup of generic functions. I don't understand the following behavior for generic functions: Suppose I'm working with the cats data in the MASS library and I want to create a formula object to model Hwt on Sex: # This works: > formula(Hwt ~ Sex) Hwt ~ Sex # But the following does not: > formula(Hwt ~ Sex,

I currently have a data set in which gender is inputed as "Male" and "Female" , and I'm trying to convert this into "1" and "0". I found a website which reccomended using two commands: data$scode[data$sex=="M"] <- "1" data$scode[data$sex=="F"] <- "2" to convert to numbers, and: data$scode <-

On 13 October 2015 at 16:41, Rafael Espíndola <llvm-dev at lists.llvm.org> wrote: >> - *Be welcoming.* We strive to be a community that welcomes and supports >> people of all backgrounds and identities. This includes, but is not limited >> to members of any race, ethnicity, culture, national origin, colour, >> immigration status, social and economic class, educational

Hello R gurus, I have a data set from which i have to extract the gender and age matched rows from controls and disease group disease<-paste(rep(c('y','n'),11)) gender<-paste(rep(c('m','f'),11)) mcp<-rnorm(700,1400) age<-rnorm(32,34) dat<-data.frame(disease=disease,sex=gender,Dr_age=age[1:22],MCP=mcp[1:22]) I have other categorical variables also to

Hello, I am trying to get the same values for the adjusted means and standard errors using R that are given in SAS for the following data. The model is Measurement ~ Age + Gender + Group. I can get the adusted means at the mean age by using predict. I do not know how to get the appropriate standard errors at the adjusted means for Gender using values from predict. So I attempted to get them

I'm working with a nested model (mixed). I have four factors: Patients, Tissue, sex, and tissue_stage. Totally I have 10 patients, for each patient, there are 2 tissues (Cancer vs. Normal). I think Tissue and sex are fixed. Patient is nested in sex,Tissue is nested in patient, and tissue_stage is nested in Tissue. I tried aov and lme as the following, > aov(gene ~ tissue + gender +

This is a follow-up on my note of Saturday. Let me start with two important clarifications - I think this would be a nice addition, but I've had exactly one use for it in the 15+ years of developing the survival package. - I have a work around for the current case. Prioritize accordingly. The ideal would be to change survexp as follows: fit <- survexp( ~ gender,

Hello All, Any one using acts_as_enumerated? I need help using (I like that it caches values in memory) I am working on a dating website and there are lots of options I want to store as enumerated like Status; divorce, single, Sex: male, female Eye color; blue, brown, green.... and lots more.... But I do not wanna keep them in seperate tables, and wanna keep them all together. Anyone has a

On Tue, Oct 13, 2015 at 12:35:07PM -0400, Aaron Ballman via llvm-dev wrote: > On Tue, Oct 13, 2015 at 12:21 PM, Renato Golin via llvm-dev > <llvm-dev at lists.llvm.org> wrote: > > On 13 October 2015 at 16:41, Rafael Espíndola <llvm-dev at lists.llvm.org> wrote: > >>> - *Be welcoming.* We strive to be a community that welcomes and supports > >>>

Hello All R Users, Function loglm() in library MASS can be cajoled to accomodate structural zeros in a cross-classification table. An example from Fienberg demonstrates how this can be done. My question is: Can the function glm() perform the same task? Can glm() estimate a log-linear model with fixed zeros like loglm()? Thanks for your help, Andrew ## Fienberg, The Analysis of Cross-Classified

I apologize in advance if this is the wrong forum for this report/request, and for the fact that I have not read the code for ftable.formula in any detail. >From reading the documentation for ftable.formula, I expected that the following two calls to ftable would produce the same results: data(UCBAdmissions) ftable(UCBAdmissions, row.vars = "Dept", col.vars = c("Gender",

Hi All: I was recently working with a dataset on arsenic poisoning. Among the variables in the dataset, I used the following three variables to produce crosstabulations (variable names: FOLSTAT, GENDER, ASBIN; all three were categorical variables, FOLSTAT denoted follow up status for the subjects and had seven levels, GENDER denoted sex (two levels: male,female), and ASBIN denoted binarized

I'm trying to obtain within-group correlations on a subset of variables. I first selected my variables using the following command: mydata$x<-mydata[c("iq","education","achievement")] I'd like to look at correlations among those variables separately for men and women. My gender variable in mydata is coded 1 (women) and 0 (men). I have successfully used