similar to: qualitative response model

I'm having some problems in installing some source packages under linux. As an example, MCMCpack. An error is raised when linking: > install.packages("MCMCpack") [...] * Installing *source* package 'MCMCpack' ... checking for C++ compiler default output file name... a.out checking whether the C++ compiler works... yes checking whether we are cross compiling... no checking

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

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

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

Hello. I have a data base with 50 qualitative variables and a lot of individuals. I try to estimate the links between one of these variables (landcover) and the 49 others (geomorphology, hydrography...). I want to use a "discriminant analysis on qualitative variables" (as DISQUAL in SPAD) or a " log-linear model ". Which R-Package(s) or other methods can you advise me.

Hello all. I am doing one part of an evaluation of a mandatory welfare-to-work programme in the UK. As with all evaluations, the problem is to determine what would have happened if the initiative had not taken place. In our case, we have a number of pilot areas and no possibility of random assignment. Therefore we have been given control areas. My problem is to select for survey individuals in

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 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, 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

Hello everybody, I'm new to MCMClogit. I'm trying to use MCMClogit to fit a logistic regression model but I got some warnings I can't understand. My input data X is 32(tissue sample)*20(genes) matrix, each element in this matrix corresponds to the expression value of one particular gene in one of 32 samples. And the Y presents the corresponding classes (0-non cancer, 1-cancer)

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 all, I am trying to use the logistic regression with MCMClogit (package: MCMCpack/Coda) and I want to put a beta prior on the parameters, but it's giving me error message (please see output below) no matter what shape 1 or 2 I use. It works perfect with the cauchy or normal priors. Do you know if there is a catch there somewhere? Thanks logpriorfun <- function(beta,shape1,shape2){

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(

Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors. For example: res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5) res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5) res3 =

Many thanks, My goal is to make a plott like attached but the Y axis starts in XIV and end at top in I. Generally for instance in excel X axis is categories but Y axis is numbers I want the contrary plotted in lines, your last help is near what I look but barplot is not needed. Hope you can help me thanks in advance. 2018-05-22 0:58 GMT+02:00 Jim Lemon <drjimlemon at gmail.com>: > Hi

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",

Hi everyone: I am just wondering how can I use weights with MCMClogit function (in MCMCpack package). For example, in case of glm function as given below, there is weights option in the arguments. Aparently there is no option of using weights in MCMClogit. glm(formula, family = gaussian, data, weights, subset, na.action, start = NULL, etastart, mustart, offset, control =

Hi Everyone, I have some data that predicts both a nominal and ordinal response variable. I was wondering what packages in R would help me analyze the data? I was also curious if anyone could recomend me some textbooks that would help with the analysis of such data? I have the 5th edition of "Applied Multivariate Statistical Analysis" by Richard A. Johnson and Dean W. Wichern

Dear All, My question is more a statistical question than a R question. The reason I am posting here is that there are lots of excellent statistician on this list, who can always give me good advices. Per my understanding, the purpose of propensity score is to reduce the bias while estimating the treatment effect and its implementation is a 2-stage model. 1) First of all, if we assume that T =

Hi all, i am looking to built a simple example of a very basic propensity score adjustment, just using the estimated propensity scores as inverse probability weights (respectively 1-estimated weights for the non-treated). As far as i understood, MLE predictions of a logit model can directly be used as to estimates of the propensity score. I already considered the twang package and the