similar to: A good way to store some profile-information?

I have been looking at some 'table' examples in Peter Dalgaard's ISwR book, and I am confused by how to get right justification of my table headings when I use the tables() command. Compare the following: # Produces right justfified column names caff.marital=matrix(c(652,1537,598,242,36,46,38,21,218,327,106,67),nrow=3,byrow=T)

Dear all, I have a data.frame that includes a series of demographic variables for a set of respondents plus a dependent variable (Theta). For example: Age Education Marital Familysize Income Housing Theta 1: 50 Associate degree Divorced 4 70K+ Owned with mortgage 9.147777 2: 65

En el libro EPICALC (pagina 229-230) en el que está el siguiente script, todo nos funciona bien, pero cuando vamos a life table, ya allí no avanza, lo señalamos en el script, por favor quizá se nos haya ido algún detalle, pero fuimos siguiéndolo por el libro paso a paso y no no hemos percatado Todos los de el paquete survival de la ayuda del R funcionan perfectamente

Hola Jose, y podrias enviar algun link al libro o enviar esa parte escaneada ?? para minimizar la posibilidad de que haya algo en la transcripcion del script ... Slds, eric. On Fri 04 Oct 2013 01:59:33 PM CLT, daniel wrote: > José, > > La función survfit espera como argumento una fórmula. Al pie del help de la > función survfit dice: > > "Versiones anteriores del código

Hello, I am running: R version 2.10.0 (2009-10-26) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 on a RedHat Linux box with 48Gb of memory. I am trying to create a model.matrix for a big model on a moderately large data set. It seems there is a size limitation to this model.matrix. > dim(coll.train) [1] 677236 128 > coll.1st.model.mat <-

Dear R-user, I have a data like this below, age <- c("young","mid","old") married <- c("no","yes") income <- c("low","high","medium") gender <- c("female","male") I want to make some of combination data like these, age.income.dat <- expand.grid(age,

Hello everybody. I'm trying to send the result of a decision tree for latex, but I do not get with the package(xtable), there is a package that make this export Export this for latex marital.status = Divorced | educational.num <= 12: <=50K (1795.0/90.0) | educational.num > 12 | | hours.per.week <= 41: <=50K (302.0/58.0) | | hours.per.week > 41 | | |

Dear R users, I have a problem, which I can not find a solution. Probably someone could help me? I have a result from my classification, like this > credit.toy [[1]] age married ownhouse income gender class 1 20-30 no no low male good 2 40-50 no yes medium female good [[2]] age married ownhouse income gender class 1 20-30 yes yes high male

Hi everyone, I am just trying to figure out how to do a xyplot where in addition to dots and lines I can change dots' colors according to an individual variable (e.g., marital disruption across time, a dummy 0/1). When I use "groups" specification (see below), I get two different lines for each individual based on groups, and what I want is to get one line connecting dots, and

Dear Sir/Ma, I Adelabu.A.A, one of the R-users from Nigeria. When am running a coxph command the below error was generated, and have try some idea but not going through. kindly please assist: > cox1 <- coxph(Surv(tmonth,status) ~ sex + age + marital + sumassure, X) Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not

ScopedProxy uses with_scope and proxy objects to make it easy to find and count different types of records. Example: class Person < ActiveRecord::Base scoped_proxy :minor, :conditions => ''age <= 17'' scoped_proxy :adult, :conditions => ''age >= 18'' scoped_proxy :old, :conditions => ''age >= 70'' scoped_proxy :male,

Thank you very much. After that I have the following error: cols<- c("GrMM", "RELG", "CASTE1", "SECTOR", "SECTOR4","AGE", "MARITAL", "JOBSTATUS", "ENG", "EDU", "PARENT_EDU", "MASSMEDIA_F", "MASSMEDIA_M", "HomeComputer", "HomeInternet") > for

I run this command for converting the numerical variable into factor. However, I get the following error message. > cols<- c(?GrMM", "RELG", "CASTE1", "SECTOR", "SECTOR4","AGE", "MARITAL" "JOBSTATUS", "ENG", "EDU", "PARENT_EDU", "MASSMEDIA_F", "MASSMEDIA_M",

You are missing a comma between "MARITAL" and "JOBSTATUS". On Tue, Apr 10, 2018 at 10:27 AM, Saif Tauheed <saif.tauheed at gmail.com> wrote: > I run this command for converting the numerical variable into factor. > However, I get the following error message. > > > cols<- c(?GrMM", "RELG", "CASTE1", "SECTOR",

Hi, I see the following is the credit scoreing in R guide : m2<-glm(formula = good_bad ~ checking + duration + history+ purpose +amount + savings + employed + installp + marital + coapp +age + other + depends + telephon + foreign +checking:amount What does checking:amount mean? Regards, Xiaobo Gu

José, La función survfit espera como argumento una fórmula. Al pie del help de la función survfit dice: "Versiones anteriores del código también permiten la especificación de una sola curva al omitir el lado derecho de la fórmula, es decir, ~ 1. El manejo de este caso requiere algunas manipulaciones poco comunes y frágiles, este caso ya no es compatible." (sepan disculpar la

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

Here''s the issue: When I submit a form for updating, @person.update_attributes doesn''t work. But the next nested clause for updating images DOES work. How is that possible? However no matter what I do the Person object will not change its attributes. def edit @person = Person.find(params[:id]) if request.post? @person.update_attributes(params[:person])

Hi everyone, Colleagues of mine at Lawrence Livermore National Laboratory (LLNL), having seen the advantages of working closely with LLVM, have decided to toss their hat into the ring and would like to hire an LLVM-focused compiler engineer. I collaborate with LLNL regularly, and I'm excited that they're taking this step. I expect that a qualified applicant will find this position both

This dataset below is one sample answer the questioner from our customer. > testbank <- read.table("testbank.txt", header=T) > testbank age married income gender ownhouse class 1 20-30 no high female yes 1st 2 30-40 no high female yes 1st 3 40-50 no low female yes 1st 4 50-60 no high female yes 1st 5 60-70