similar to: "Explore" SPSS function in R

Hello folks, I am trying to estimate the two-step Heckman regression model. I would like to make an adjustment for intragroup correlations. Stata can implement this with the "cluster" option, but I am really hoping to stick with R. It seems that the micEcon package is the primary source for this two-step regression model (i.e., heckit), but I can't find a way to make the

¡Hola! ¿Alguien sabe si existe una función en R que devuelve el nombre del área de trabajo (workspace) en uso si éste ha sido guardado anteriormente? Con getwd() R me devuelve la carpeta de trabajo, pero si en ésta tengo varios áreas de trabajo guardados, p.e. a.RData y b.RData, ¿cómo podría saber en cuál estoy trabajando si por un despiste no me acordara cuál he abierto anteriormente (sin tener

Hi all, This is probably going to come off as unnecessary (and show my ignorance) but I am trying to understand the parameter estimates I am getting from R when doing an ANCOVA. Basically, I am accustomed to the estimate for the categorical variable being equivalent to the respective cell means minus the grand mean. I know is the case in JMP - all other estimates from these data match the

Hi A.K Regarding my question on comparing normal/ obese/overweight with blood pressure change, I did finally as per the first suggestion of stacking the data and creating a normal category . This only gives me a obese not obese 14, but when I did with the wide format hoping to get a obese14,normal14,overweight 14 Vs hibp 21, i could not complete any of the models. This time I classified obese=1

Hello! Can you help me? How to order an data.table by values of an column? Per example: Table no initial Categ Perc 468  31.52 351  27.52 0  0.77 234  22.55 117  15.99 table final Categ Perc 0  0.77 117  15.99 234  22.55 351  27.52 468  31.52 Lesandro Veja quais são os assuntos do momento no Yahoo! +Buscados [[alternative HTML version deleted]]

Hi R-users, I have a simple question for R heavy users. If I have a data frame like this dfr <- data.frame(id=1:16, categ=rep(LETTERS[1:4], 4), var3=c(8,7,6,6,5,4,5,4,3,4,3,2,3,2,1,1)) dfr <- dfr[order(dfr$categ),] and I want to score values or points in variable named "var3" following this kind of logic: 1. the highest value of var3 within category (variable named

This is a question about removing the intercept in a binomial glm() model with categorical predictors. V&R (3rd Ed. Ch7) and Chambers & Hastie (1993) were very helpful but I wasn't sure I got all the answers. In a simplistic example suppose I want to explore how disability (3 levels, profound, severe, and mild) affects the dichotomized outcome. The glm1 model (see below) is

Hi all, Is there an equivalent to predict(...,type="linear") of a Proportional hazard model for a Cox model instead? For example, the Figure 13.12 in MASS (p384) is produced by: (aids.ps <- survreg(Surv(survtime + 0.9, status) ~ state + T.categ + pspline(age, df=6), data = Aidsp)) zz <- predict(aids.ps, data.frame(state = factor(rep("NSW", 83), levels =

Hi, I have a following data set: id event time (in sec) 1 add 1373502892 2 add 1373502972 3 delete 1373502995 4 view 1373503896 5 add 1373503996 ... I'd like to add new column "time on task" which is time elapsed between two events (id2 - id1...). What would be the best approach to do that? Thanks, Srecko [[alternative HTML

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Hi everyone, I am doing 5 years mortality predictive index score with survival analysis using a Cox proportional hazard model where I have a continous predictive variable and a right censored response which is the mortality, and the individuals were followed a maximum of 7 years. I'd like to asses the discrimination ability of survival analysis Cox model by computing a ROC curve and area

Dear R users, Basically, from the following arbitrary data set: a <- data.frame(id=c(c("A1","A2","A3","A4","A5"),c("A3","A2","A3","A4","A5")),loc=c("B1","B2","B3","B4","B5"),clm=c(rep(("General"),6),rep("Life",4))) > a

Hello, I'm new to R and in the process of testing it My first question: I fail to read in my data (ANSI toto.txt file, tab separated) > test <-read.table("toto.txt") Error in file(file, "r") : unable to open connection In addition: Warning message: cannot open file `toto.txt' > test <-

Dear developers, I have jutst started working with caret and all the nice features it offers. But I just encountered a problem: I am working with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor). Everything was working fine I got the results, confussion matrix etc. BUT for obtaining the AUC and predicted probabilities I had to add

HI Elisa, You could use ?cut() vec1<-c(33,18,13,47,30,10,6,21,39,25,40,29,14,16,44,1,41,4,15,20,46,32,38,5,31,12,48,27,36,24,34,2,35,11,42,9,8,7,26,22,43,17,19,28,23,3,49,37,50,45) label1<-unlist(lapply(mapply(c,lapply(seq(0,45,5),function(x) x),lapply(seq(5,50,5),function(x) x),SIMPLIFY=FALSE),function(i) paste(i[1],"<x<=",i[2],sep="")))

I still need to do some repetitive statistical analysis on some outcomes from a dataset. Take the following as an example; id sex hiv age famsize bmi resprate 1 M Pos 23 2 16 15 2 F Neg 24 5 18 14 3 F Pos 56 14 23 24 4 F Pos 67 3 33 31 5 M Neg 34 2 21 23 I want to know if there are statistically detectable differences in all of the continuous variables in

Hi all, Suppose I have a vector x with numerical values. In y, I have a categorial variable : y<-c(1,1,..2,2,...30,30,30) x and y have the same length. I would like to compute the mean for x for the modality 1 to 30 in y. mean(x[y==1]),...,mean(x[y==30]) I do not want to use an iterative procedure such that for (i in 1:30).. Thanks for your help, Regards. Olivier. --

Hi All, I'm trying to automate a data summary using summary or describe from the HMisc package. I want to stratify my data set by patient_type. I was hoping to do something like: Describe(myDataFrame ~ patient_type) I can create data subsets and run the describe function one at a time, but there's got to be a better way. Any suggestions? Rachel [[alternative HTML

Hi, Sorry, but how did you bring it out? Thanks On Fri, Dec 28, 2012 at 8:48 AM, arun kirshna [via R] < ml-node+s789695n4654093h10@n4.nabble.com> wrote: > Hi, > bp.sub<- structure(list(CODEA = c(1L, 3L, 4L, 7L, 8L, 9L, 10L, 11L, 12L, > 13L, 14L, 16L, 17L), C45 = c(NA, 2L, 2L, 2L, 2L, 1L, NA, 1L, > 1L, 2L, 1L, 2L, 1L), ragek = c(3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L, > 3L, 3L,

Dear listers, I was used to package pgirmess under Windows with everything OK, but, for the first time, I had a trial this afternoon on Ubuntu 7.10 gutsy (I have a double boot computer and work more and more under unix) and R 2.7.0. Everything went OK except this: sudo R CMD check pgirmess ..... * checking examples ... ERROR Running examples in 'pgirmess-Ex.R' failed. The error most