similar to: How to order an data.table by values of an column?

Hello all, How to draw a line in plot when I know the start point(x1,y1) and end point(x2,y2)? I need make this as additional information in the graph: plot(wl2[[1]],wl2[[2]]) I think that is possible make this with the function abline(), is possible? I looked the function lines() too, but don't understand as make. Thanks! Lesandro Veja quais são os assuntos do momento no Yahoo!

How to force R to print 2 decimal places in column of a data.frame? I tried to do so: x = inp(format(rounf(inp$Tamanho, 2), nsmall = 2),) where "INP" is data.frame and "Size" is the name of column. But has error: Error in eval.with.vis(expr, envir, enclos) : could not find function "inp" Lesandro Veja quais s?o os assuntos do momento no Yahoo! +Buscados

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

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 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! 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

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

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, 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

<http://beranger.org/index.php?categ=17&offset=0>, but nothing specific about the named/bind showstopper. rgds/ldv

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, I am currently conducting some survival analyses. I would like to extract coefficients at each level of the IVs. I read on a previous posting that dummy regression using coxph was not possible. Therefore I though, hey why not categorize the variables (I realize some folks object to categorization but the paper I am replicating appears to have done so ...) and turn the variables

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

Hi everyone, Does anybody knows if there is an equivalent R function that gives the same outcome as in "Explore" function in SPSS ? (Analize->Descriptive Statistics->Explore) It does a categorical vs quantitative variables analysis. ( But not linear regression) I need to compare intragroup (categorical variable with 4 values) means and confidence intervals of a quantitative