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...[intercambio] comment = Directorio de Intercambio AEP-Perforacion path = /home/usuario/intercambio read only = No guest ok = true strict allocate = Yes printable = No hide special files = Yes Thanks Best Regards Ernesto Lira

Dear all, I am a new user in r and I am facing some problems with the quantile regression specification. I have two matrix (mresultb and mresultx) with nrow=1000 and ncol=nsim, where I specify (let's say) nsim=10. Hence, the columns in my matrix represents each simulation of a determined variable. I need to regress each column of mresultb on mresultx. My codes are the following:

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Dear all, I need to run a simple linear regression such that: y = b0 + b1*x1 + (1-b1)*x2 + e which I know I can use: lm(y ~ I(x1 - x2) + offset(x2)). However, I also need to restrict the coefficient b1 to be between 0 and 1. Is there any way to include such restriction in the linear regression estimation? I saw suggestion related with the function Solve.QP, but I really did not understand such

Dear all, I need to extract the covariance matrix of my quantile regression estimation to use in a test. My regression is: qf2_1 <- summary(rq(wb2 ~ apv2 + vol2, tau = phi2[1]), cov = TRUE) I can extract the covaraince matrix by using: qf2_1 [3]. However, if I try to use it in the test, it does not work. I only need to transform qf2_1[3] in a matrix 3x3. I have already tried:

Dear all, I need to do a loop in R, but I am not sure the software is generating "n" times the variables I request differently. When I ask to print the last matrix created, I just can see the loop for n=1. To be more precise, supose I need to simulate 10 times one variable and I want to fit the 10 variables simulated in a matrix. I dont really know what I am doing wrong, but I just

Dear all, I have just sent an email with my problem, but I think no one can see the red part, beacuse it is black. So, i am writing again the codes: rm(list=ls()) #remove almost everything in the memory set.seed(180185) nsim <- 10 mresultx <- matrix(-99, nrow=1000, ncol=nsim) mresultb <- matrix(-99, nrow=1000, ncol=nsim) N <- 200 I <- 5 taus <- c(0.480:0.520) h <-

Dear all, I need to do a loop as following: #Consider a matrix: M <- matrix(1, nrow=10, ncol=20) #Matrices to store the looping results M1 <- matrix(0, nrow=10, ncol=400) h <- c(1:20/1000) #loop for (j in h){ M1 <- M/(2*j) } But this means that the first 20 columns of matrix M1 (that is, columns 1:20) should show the results of M/(2*0.001). Then, the following 20

Dear all, I sent an email on Friday asking about nlrq {quantreg}, but I haven't received any answer. I need to estimate the quantile regression estimators of a model as: y = exp(b0+x'b1+u). The model is nonlinear in parameters, although I can linearise it by using log.When I write: fitnl <- nlrq(y ~ exp(x), tau=0.5) I have the following error: Error in match.call(func, call = cll) :

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Dear all, I would like to know whether any specification test for linear against nonlinear model hypothesis has been implemented in R using the quantreg package. I could read papers concerning this issue, but they haven't been implemented at R. As far as I know, we only have two specification tests in this line: anova.rq and Khmaladze.test. The first one test equality and significance of

Dear all, I am trying to compare the estimated coefficients of a quantile regression model between two different samples. It is a Wald test, but I cannot find one way to do that in R.The samples are collected conditional on a specific characteristic and I would like to test whether such characteristic indeed affect the estimators. The problem in the test anova.rq is that the response variable

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Dear all, I am trying to run a loop in my codes, but the software returns an error: "subscript out of bounds" I dont understand exactly why this is happenning. My codes are the following: rm(list=ls()) #remove almost everything in the memory set.seed(180185) nsim <- 10 mresultx <- matrix(-99, nrow=1000, ncol=nsim) mresultb <- matrix(-99, nrow=1000, ncol=nsim) N

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Dear all, I am doing a simulation for my model that works when I use only the rq() command. However, since I need to use the varcov matrix for my Wald test, I need to compute summary(rq(), cov=TRUE). But the simulation does not work because of the error: tau + h > 1: error in summary.rq I tried to use: if (tau + h > 1) stop("tau + h > 1: error in summary.rq") But the