search for: categ

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 & overweight=1 as obese itself. Can you tell me if I can use the geese or geeglm...

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 output from R, except I don't understand why the parameter estimate for the categorical factor differs. library(car) options(contrasts=c...

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 "categ") -> "high&qu...

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 (...

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 = levels(Aidsp$state)), T.categ = factor(rep("hs", 83), levels = levels(Aidsp$T.categ)), age = 0:82), se = T, type = "linear") plot(0:82, exp(zz$fit)...

...l 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 I get a warning message when I do that with fit.multiple.impute. a<-aregImpute(~med.hist.err+ med.discr+newLiving+No.drugs+Days.categ+Los+Age+Ward+Sex, n.impute=20, nk=0,data=med.err) ddist<-datadist(Age,No.drugs,Days.categ, Sex, Living, Ward) options(datadist="ddist") fmi<-fit.mult.impute(med.hist.err~Age+No.drugs+Days.categ+Sex+Living+Ward, fitter=lrm, x=T, y=T,a,data=med.err) Error in 1:n.impute : NA/NaN argum...

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

...A5 B5 General 6 A3 B1 General 7 A2 B2 Life 8 A3 B3 Life 9 A4 B4 Life 10 A5 B5 Life I desire removing records (highlighted records above) with identical values in each fields ("id" & "loc") but with different value of "clm" (i.e according to category) i.e > categ <- table(a$id,a$clm) > categ General Life A1 1 0 A2 1 1 A3 2 1 A4 1 1 A5 1 1 The desired output is id loc clm 1 A1 B1 General 6 A3 B1 General Because the data set I am working on is quite big (~ 800,...

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 " classProbs = TRUE," in the trainControl. Thereafter everytime I run train I get this message: "undefi...

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

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

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 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 into factors and then try the analysis. E.g., Dataset <- read.table("categ.dat", header=TRUE) Dataset$eventbefore2c <- facto...

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 variable. Just like "Explore" function does. Thanks a lot -- View this message in context: http:/...

...ach contains a set of measurement values 2) The measurements of each file belong to a position in a two dimensional matrix (lets say "B02.csv" belongs to position 2,2 3) The size of the matrix is fix 4) I cannot assure to have a csv file for each position 5) Each position belongs to one category; This information is available in a file (means 2,2 and 2,3 may belong to category "c1"; 3,2 and 3,3 may belong to category "c2") Now, I process each available file and get a vector of 6 values or NA back. The aim is to calculate mean and sd for vectors (element wise) com...