similar to: generate correlated qualitative data

The problem remain unsolved. If you have any idea please do suggest .................... thank you; Ram H On Wed, Aug 31, 2011 at 12:06 PM, Ram H. Sharma <sharma.ram.h@gmail.com>wrote: > Dear R experts: > > I have following problem: > > # myfunction > mfun1 <- function(x) { > if ( x == 2){ > xv <- sample(c(2,1,0),100, replace = T, prob = c(0.6, 0.2,

Hi!! I am trying to generate data with specific correlation using corgen method from the ecodist package. It does not seem to generate the data properly. I am listing the code below. Secondly, when I put the code given below all in one file and source it, it is unable to complete the task. But when I execute it one at a time it is able to complete it. Can someone point out any mistake that I may

I wish to generate a vector of uniformly distributed data with a defined correlation to another vector The only function I have been able to find doing something similar is corgen from the library ecodist. The following code generates data with the desired correlation to the vector x but the resulting vector y is normal and not uniform distributed library(ecodist) x <- runif(10^5) y

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

RDQA is a package for Qualitative Data Analysis built upon R. It works both on the Windows and Linux/FreeBSD platforms. RQDA is an easy-to-use tool to assist in the analysis of textual data. At the present, it supports only plain text format data. All the information is stored in SQLite database via the R package of RSQLite. The GUI is based on RGtk2, via the aid of gWidgetsRGtk2. It includes a

Hello, My name is Dontrece Smith. I am creating figures for my GAMs. I change my qualitative variables to 1 or 2 in my dataset, so I would be able to run my GAMs. However, R will only display plots for my quantitative variables and not my qualitative variables. Is there any way to fix this issue? I listed some of my code below: > library(mgcv) This is mgcv 1.7-13. For overview type

I am using the read.table function to load an Excel data set into R. It has a few variables with very long qualitative (free response typically in sentences) response that I would like to keep, but would like to limit the "length" of the response that R shows. Is there some sort of string or column width command I can include in the read.table function to limit the length of words used

Hi, I have some doubt about how qualitative factors are coded in R. For instance, I consider a response y, a quantitative factor x and a qualitative factor m at 3 levels, generated as follow : y_c(6,4,2.3,5,3.5,4,1.,8.5,4.3,5.6,2.3,4.1,2.5,8.4,7.4) x_c(3,1,3,1,2,1,4,5,1,3,4,2,5,4,3) m_gl(3,5) lm(y~x+m) Coefficients: (Intercept) x m2 m3 3.96364 0.09818

Dear R People: Here is a function to minimized: >mfun1 function(x,a) { x[1] <- a[1]*x[2] + a[3] - a[2]*(a[1]-a[2])*a[3] x[2] <- a[1]*x[1] - a[2]*a[3] return(x) } Here is my first try: >nlm(mfun1,c(1,1)) Error in f(x, ...) : Argument "a" is missing, with no default > >nlm(mfun1,c(1,1),a=c(0.8,0.5,1)) Error in nlm(mfun1, c(1, 1), a = c(0.8, 0.5, 1)) :

Hello, I have a question regarding competing risk regression using cmprsk package (function crr()). I am using R2.9.1. How can I do to assess the effect of qualitative predictor (gg) with more than two categories (a,b,c) categorie c is the reference category. See above results, gg is considered like a ordered predictor ! Thank you for your help Jan > # simulated data to test > set.seed(10)

Hi, I want to know is there other functions in R to estimate qualitative response model besides multinom() in library nnet, if this is the only possibility, I have a question about the application: for example: there is three transportation choice : car, bus , subway. each alternative has own characteristic variables, I want to apply conditional logit model to analysis the choice of three

Hi Pedro, Not too hard. Just have to watch the order of the variables: ppdf<-read.table(text="N M W I 10 106 II 124 484 III 321 874 IV 777 1140 V 896 996 VI 1706 1250 VII 635 433 VIII 1437 654 IX 693 333 X 1343 624 XI 1221 611 XII 25 15 XIII 3 NA XIV 7 8", header=TRUE) plot(rev(ppdf[,2]),1:14,col="blue",lty=1,type="l",

See ?barplot and set the horiz argument to TRUE. (This is in the base R plotting version. The ggplot2 and lattice systems have other ways of doing this) Note: if you search on e.g. "barplots in R" or similar, you should find numerous examples with code. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into

Dear R People: I am trying to duplicate the example from Dennis and Schnabel's "Numerical Methods for Unconstrained Optimization and Nonlinear Equations", which starts on page 149. My reason for doing so: to try to understand the "nlm" function. Here is the function: >mfun1 function(x) { z <- matrix(0,nrow=2,ncol=1) z[1,1] <- x[1]^2 + x[2]^2 -

Hi all, I?m trying to plot this data N M W I 10 106 II 124 484 III 321 874 IV 777 1140 V 896 996 VI 1706 1250 VII 635 433 VIII 1437 654 IX 693 333 X 1343 624 XI 1221 611 XII 25 15 XIII 3 XIV 7 8 So that in de Y axis will be the level (qualitative data) and in the X axis will be M and W variables. So x axis will be wwith a lenght between 0 and 2000. I would like to plot a line with M and other

Hi, Arvind Sudarsanam: I know some of Open64. Above all, Open64 is designed for a high performance compiler. It is now supported by AMD, HP, ICT Chinese Academy of Science, etc. and has been ported to X86, Itanium, Loongson CPU etc. And to your questions 1, Open64 already have some main optimization phases, Inline for aggressive inline opt. LNO for loop opt, WOPT for machine independent opt(

Dear R experts, I am trying to simulate correlated binary data and have stumbled upon the following problem: With the help of "binarySimCLF" or "mvpBinaryEp" I have been able to simulate correlating binary vectors given certain mean values and a desired correlation. My problem is that these procedures do not allow you to specify the exact vector for which you want to generate

Hi, I have been working towards developing compiler optimization tools targeting multi core processors while using LLVM IR as the starting point and building on top of the analysis and optimization passes available in the llvm source. Recently, I looked into Open64 and its intermediate representation WHIRL. Documentation for developers to use Open64 seems to be inadequate (when compared to LLVM

Hi everyone. Based on a dependent variable (y), I'm trying to generate some independent variables with a specified correlation. For this there's no problems. However, I would like that have all my "regressors" to be orthogonal (i.e. no correlation among them. For example, y = x1 + x2 + x3 where the correlation between y x1 = 0.7, x2 = 0.4 and x3 = 0.8. However, x1, x2 and x3

Hi Pedro, melt() is probably working. The problem is I did not finish the copy and paste.? It would have been better if I had included the ggplot() command. Try ============================================================== library(reshape2) library(ggplot2) dat1? <- structure(list(N = c("I", "II", "III", "IV", "V", "VI",