search for: taus

...data = Datos) tau: [1] 0.5 Coefficients: Value Std. Error t value Pr(>|t|) (Intercept) 4.24961 1.05525 4.02710 0.00046 lll -0.92941 0.22934 -4.05246 0.00043 > fit <- rqProcess( y.net ~ lgdp2 + fse2 + gedy2 + Iy2 + gcony2, + data = barro, taus = seq(.1,.9,by = .05)) Erro en inherits(x, "data.frame") : objeto "barro" no encontrado > fit <- rqProcess(yyy ~ lll, data = Datos, taus = seq(.1,.9, by = .05)) taus: 0.1 Erro en summary.rq(rq(formula, data = data, tau = taus[i], method = "fn"), : ta...

...) 1 3 4 5 3 1 1 1 , but how can i for example turn: 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 1 0 0 into 0 0 1 1 1 0 0 2 0 1 0 3 My second problem is, sorting rows and columns of a matrix by the rowSums/colSums. I did it this way, but i think there should be a more efficient way: sortRowCol<-function(taus) { swaprow <- function(rsum) { taus[(rowSums(taus)==rsum),] } for( i in 1:2 ) taus<-sapply(sort(rowSums(taus)),swaprow) } thanks in advantage, Stefan Nachtnebel -- "Feel free" - 5 GB Mailbox, 50 FreeSMS/Monat ... Jetzt GMX ProMail testen: www.gmx.net/de/go/mailfoot...

I've been trying to draw quantile linear regression lines across a scatterplot of my data using attach(forrq) plot(PREGNANT,DAY8,xlab="pregnant EPDS",ylab="postnatal EPDS",cex=.5) taus <- c(.05,.1,.25,.75,.9,.95) xx <- seq(min(PREGNANT),max(PREGNANT),100) for(tau in taus){ f <- coef(rq(DAY8~PREGNANT,tau=tau)) yy <- (f[1]+f[2]*xx) lines(xx,yy) } which is simply the method from the help file with my dataset a...

...amespace (and not attached): [1] compiler_3.4.0 Matrix_1.2-10 MatrixModels_0.4-1 grid_3.4.0 [5] lattice_0.20-35 *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*-* Here is the code that crashes: #-----Here I redefine the function as described in my first paragraph rq.fit.hogg2 = function (x, y, taus = c(0.1, 0.3, 0.5), weights = c(0.7, 0.2, 0.1), R = NULL, r = NULL, beta = 0.99995, eps = 1e-06) { n <- length(y) n2 <- nrow(R) m <- length(taus) p <- ncol(x) + m if (n != nrow(x)) stop("x and y don't match n") if (m != length(weights))...

Quantreg package allows to plot the summary of models derived by quantile regression at different taus. The plot shows the parameters variation by varying taus: intercept and slope (for a linear model). Together with these values even confidence intervals may be plotted, based on the threshold given within the summary (e.g. alpha=0.01 equals 99% CI). However the graphic even plots the mean of all...

...data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R I am trying to change the number quantiles being estimated. I change the codes about w and taus ,as following w=c(.01,.02,.03,.04,.05,.06,.07,.08,.09,.1,.11,.12,.13,.14,.15,.16,.17 ,.18,.19,.2,.21,.22,.23,.24,.25,.26,.27,.28,.29,.3,.31,.32,.33,.34,.35 ,.36,.37,.38,.39,.4,.41,.42,.43,.44,.45,.46,.47,.48,.49,.5,.49,.48,.47 ,.46,.45,.44,.43,.42,.41,.4,.39,.38,.37,.36,.35,.34,.33,.32,.31,.3,.29 ,...

...if(nchar(pkg)) library(pkg, character.only=TRUE)}) Loading required package: SparseM Package SparseM (0.78) loaded. To cite, see citation("SparseM") Package quantreg (4.22) loaded. To cite, see citation("quantreg") > fit<-rqProcess(Logloss~AS+AM+CB+CF+RB+RBR,data=CPBP,taus=-1) > KhmaladzeTest(fit,nullH="location") Error in switch(mode(x), "NULL" = structure(NULL, class = "formula"), : invalid formula I would greatly appreciate if you can help me out, thank you very much!!!! (See attached file: CPBP.txt) Best Regards, Lulu...

...s" 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 <- 200 I <- 5 taus <- c(0.480:0.520) h <- c(1:20/1000) codd <- c(1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81) ceven <- c(2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,7...

...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 <- c(1:20/1000) alpha1 <- c(1:82) aeven1 <- alpha[2 * 1:41] aodd1 <- alpha[-2 * 1:41] alpha2 <- c(1:40) aeven2 <- alpha2[2 * 1:20] #Create an object to hold results. M <- matrix(0, ncol=82, nrow=nsim) Mhb0 <- matrix(0, ncol=20, nrow=nsim) Mhb1 <- ma...

...){time[i] ~ dnorm( mu[i], tau ) # stochastic componenent# link and linear predictormu[i] <- beta0 + beta1 * cases[i] + beta2 * distance[i]}# prior distributionstau ~ dgamma( 0.01, 0.01 )beta0 ~ dnorm( 0.0, 1.0E-4)beta1 ~ dnorm( 0.0, 1.0E-4)beta2 ~ dnorm( 0.0, 1.0E-4)# definition of sigmas2<-1/taus <-sqrt(s2)# calculation of the sample variancefor (i in 1:n){ c.time[i]<-time[i]-mean(time[]) }sy2 <- inprod( c.time[], c.time[] )/(n-1)# calculation of Bayesian version R squaredR2B <- 1 - s2/sy2# Expected y for a typical delivery timetypical.y <- beta0 + beta1 * mean(cases[]) + bet...

...th same value that repeats it works well and creates the sparse matrix. Can you help me to correct this problem? Is there a bug in the sparse matrix command? My second problem is the following: after solving this problem I then run a sintax where you run quantile regression repeatedly changing the taus (the quantile estimated). Hence, when I run this program it stops completely sending a microsoft message saying "An error has occurred, the application will be closed" and there is no way to continue to run the program. I thought the problem was the dimension of data, but people told me t...

...tile, tau and that all of the variability arises due to beta. If I just fit the model using the quantreg package like so: mdl <- rq( y ~ alpha +beta, data, tau=c(0.25,0.5,0.75)) ..the values of alpha that I get vary according to the tau. Is there anyway to force alpha to be the same across all taus? Best wishes, Mark [[alternative HTML version deleted]]

Hi Does anyone know if there is a method to calculate a goodness-of-fit statistic for quantile regressions with package quantreg? Tanks [[alternative HTML version deleted]]

...uot;, Cval = integer(1), Tval = integer(1))) X Set.seed <- function(Cval = 12345, Tval =987654321) { X ## Purpose: Initialize my implementation of the 'S' RNG (following Ripley) X ## ------------------------------------------------------------------------- X ## Arguments: Congrval and Tausval, the 2 long integers [== seed] X ## ------------------------------------------------------------------------- X ## Author: Martin Maechler, Date: 9 May 94, 12:04 X if(!is.loaded(C.symbol("Init_seed"))) X dyn.load("/u/maechler/C/C-for-S/Runif.o") X invisible(.C("...

Dear all, I know that my question is somewhat special but I tried several times to solve the problems on my own but I am unfortunately not able to compute the following test statistic using the quantreg package. Well, here we go, I appreciate every little comment or help as I really do not know how to tell R what I want it to do^^ My situation is as follows: I have a data set containing a

...nds" 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 <- 200 I <- 5 taus <- c(0.480:0.520) h <- c(1:20/1000) codd <- c(1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,5 3,55,57,59,61,63,65,67,69,71,73,75,77,79,81) ceven <- c(2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52, 54,56,58,60,62,64,66,68,70,72,74,76...