similar to: what is the difference between the two logistic models?

Hi, I am not sure if this is just me using R (R-2.3.1 and R-2.4.0) in the wrong way or if there is a more serious bug. I was having problems getting some calculations to add up so I ran the following tests: > (2.34567 - 2.00000) == 0.34567 <------- should be true [1] FALSE > (2.23-2.00) == 0.23 <------- should be true [1] FALSE > 4-2==2 [1] TRUE > (4-2)==2 [1] TRUE >

Hi, is there a function or an easy way to convert a variable with continuous values into a categorial variable (with x levels)? here is what I mean: I want to transform x: x <- c(3.2, 1.5, 6.8, 6.9, 8.5, 9.6, 1.1, 0.6) into a 'categorial'-variable with four levels so that I get: [1] 2 2 3 3 4 4 1 1 so each element is converted into its rank- value / categorial-value (in

Hi there fellow R users, R has many different clustering packages (e.g. mclust,cluster,e1071). However, can anyone recommend a method to deal with data sets that contain categorial and continuous variables? Regards Wayne KSS Ltd Seventh Floor St James's Buildings 79 Oxford Street Manchester M1 6SS England Company Registration Number 2800886 Tel: +44 (0) 161 228 0040 Fax: +44 (0)

Dear all, in a previuos message was asked how get the mode of continous distribution. Now I'm asking if there an R function to obtain the mode in case of a discrete distribution or categorial data. The only way is to use table(): > x<-rep(1:5,100) > s<-sample(x,40) > t<-table(s) > t s 1 2 3 4 5 13 10 5 4 8 the mode is value=1 Thanks Cordially Vito =====

Hi All, I have a data set containing : pclass: A factor giving the class of the passenger: one of 1st, 2nd, 3rd. age The age of the passenger in years. sex Passenger's gender: female or male age.group Passengers age group, one of 0?9 , 10?19, 20?29, 30?39, 40?49, 50?59, 60?69, 70?79 survived Passenger's survival (1=survived, 0=did not survive)

Dear R-List, I would like to recode categorial variables into binary data, so that all values above median are coded 1 and all values below 0, separating each var into two equally large groups (e.g. good performers = 0 vs. bad performers =1). I have not succeeded so far in finding a nice solution to do that in R. I thought there might be a better way than ordering each column and recoding the

Dear R-List, I would like to recode categorial variables into binary data, so that all values above median are coded 1 and all values below 0, separating each var into two equally large groups (e.g. good performers = 0 vs. bad performers =1). I have not succeeded so far in finding a nice solution to do that in R. I thought there might be a better way than ordering each column and recoding the

Thanking John for his suggestion I build this function which get the mode of both categorial and discrete data. Mode<-function(x){t<-table(x) if (is.numeric(x)) as.numeric(names(t)[t == max(t)]) else (names(t)[t == max(t)]) } Any other improvement and suggestion will welcome. Best Vito > s [1] 1 1 6 1 1 7 6 5 6 2 1 4 5 6 6 7 3 5 4 1 7 3 7 3 3 7 7 2 1 4 4 2 7 7 6 6 1 2 [39] 5 1 7 7

Hi All, I am working on an example where the electric utility is investigating the effect of size of household and the type of air conditioning on electricity consumption. I fit a multiple linear regression Electricity consumption=size of the house hold + air conditioning type There are 3 air conditioning types so I modeled them as a dummy variable Type A Type B Type C Where type A is the

Hello again, everytime I think I got something to work, the next issue comes up... I have the following data.frame, I want to visualize: > data_rb tld spam1 spam2 share 1 ca 826436 73452 0.0889 2 org 470550 25740 0.0547 3 de 156042 15531 0.0995 4 com 140753 7527 0.0535 5 edu 34845 2507 0.0719 6 net 12781 382 0.0299 7 ru 7648 18 0.0024

Hi, (R version 2.15.0) I am running a pgm with 1 response (earlier standardized Y) and 44 independent vars (Xi) from the same data =a2: When I run the 'lm' function on single Xi at a time, the beta coefficient for let's say X1 is = -0.08 (se=0.03256) But when I run the same Y with 44 Xi-s with the 'step' function (because I left direction parameter empty, I assume a backward

Hello everyone, I would be glad if you could help out an R-beginner here... I have a vector of categorial data like this > v <- c(1, 1, 2, 2, 2, 3, 3, 4, 4, 4) When I do > hist(v) I get the x-axis of the histogram with floating point labels: 1.0, 1.5, 2.0, etc. Is it possible to tell R that the data consists of categories, i.e. that I only want the category names (1, 2, 3, 4) on my

Dear Helpers, I just started working with R and I'm a bit overloaded with information. My data is from marsupials reindroduced in a area. I have weight(wt), hind foot lenghts(pes) as continues variables and origin and gender as categorial. condition is just the residuals i took from the model. > names(dat1) [1] "wt" "pes" "origin" "gender"

I have a simple dataset of a numerical dependent Y, a numerical independent X and a categorial variable Z with three levels. I want to do linear regression Y~X for each level of Z. How can I do this in a single command that is without using lm() applied three isolated times? -- View this message in context: http://r.789695.n4.nabble.com/Linear-regression-on-several-groups-tp2322835p2322835.html

Hi, I have a dataframe ds1.2 - 503 categorial variables and 1 continuous response variables. I ran aregImpute to deal with NA's and got the followig error: > fmla = terms( Response ~ . ,data=ds1.2) > ds.i = aregImpute(fmla,data=ds1.2) Error in matrix(as.double(1), nrow = n, ncol = p, dimnames = list(rnam, : length of dimnames [2] not equal to array extent Could you explain

Hi All, I'm learning the R codes for Cox PH modeling. Could I ask you what the function of "factor" in modeling? Thank you! When dealing with the categorical covariates (for example 3 groups), it will come out different results if we add the command "factor" in front of the categorical covariate or not: if we don't add "factor", there is only one

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 am kind of stuck of using Predict function in R to make prediction for a model with continuous variable and categorial variables. i have no problem making the model, the model is e.g. cabbage.lm2<- lm(VitC ~ HeadWt + Date + Cult) HeadWt is a continuous variable, Date and Culte are factors. Date have three levels inside (d16,d20,d21), Cult has two levels(c39,c52). I need to

Dear latexRs, I tried to make a latex printout of a simple categorial ftable. It should look like the output of print.ftable. Any ideas how to get the syntax of summary.formula right. Or some alternative? As far I see, xtable does not have method for ftable. Dieter library(Hmisc) n=500 sex <- factor(sample(c("m","f"), n, rep=TRUE)) treatment <-

Hello R-experts! When I produce a plot R takes avoids overlapping axis labels in order to maintain readabilty which is great. But now I have written a little custom plot function in which I set my own labels and label positions after generating the actual plot: axis(..., lables=c('A', 'B', 'F', 'G', 'M'), at=mypositions) As you may have guessed: This is