similar to: Quantile Regression and R

Hey, I''d start by saying that I know Btrfs is a still experimental, and so there''s no guarantee that one would be able to help me at all... But I thought I''ll try anyway :-) Few months ago I bought a new laptop and installed ArchLinux on it, with Btrfs on the root filesystem... I know, it''s not the smartest thing to do... After a few month I had issues with my

Can't seam to make a call transfer. I tried dialing pound and transferring a call to extension 222. This just results in a disconnect. The exact steps a took were Dial pound, dial 222 and hangup. This is the result from the verbose output. Recieved Stimulus: Transfer(0) Collected digit: [2] Collected digit: [2] Collected digit: [2] skinny_hangup(Skinny/8077@duba-2) on 8077@duba Am I

Hello dear R help members. I am trying to understand the anova.rq, and I am finding something which I can not explain (is it a bug?!): The example is for when we have 3 nested models. I run the anova once on the two models, and again on the three models. I expect that the p.value for the comparison of model 1 and model 2 would remain the same, whether or not I add a third model to be compared

Hello. I am a beginer in R and I can not implement the KhmaladzeTest in the following command. Please help me!!!!!!!!!!! PD: I attach thw results and the messages of the R program R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) ISBN 3-900051-07-0 R es un software libre y viene sin GARANTIA ALGUNA. Usted puede redistribuirlo bajo ciertas

Hi, I am using the quantile function currently and I have just bumped into a little problem. I have a very small data frame something like this: small_df <- c(7,3,4,7,1,10,12,1,12,4,4,8,6,11,9,10,4,13,3,9,6,5,2,10,7,14,2,7,10,10,7,8,2,11,3,10,11,3,11,14,12,7,6,11) small_df Now in the next step I would like to calculate the quantile value of the previous data frame at the probability of 95%

I am calling quantiles as follows. I don't understand why sometimes the columns (data values) above 95% are returned as "NULL"!! When I drop the percentile down to 92%, I see colums appearing. Why would any quantile be empty? I see sometimes that 95% percentile is being chosen as "1" for my data between 0 and 1, where obviously there's no column value equal to 1. But

Hi, I'm working on a data set that contains a couple of factors and a number of dependent variables. From all of these dependent variables I would like to calculate mean, standard deviation and quantiles. With the function FUN I get all the means and stdev that I want but quantiles are only calculated for the first of the dependent variables (column 8 in the summarize command). What do I

Dear all, I am searching for a way to compute a test comparable to Chuang et al. ("Causality in Quantiles and Dynamic Stock Return-Volume Relations"). The aim of this test is to check wheter the coefficient of a quantile regression granger-causes Y in a quantile range. I have nearly computed everything but I am searching for an estimator of the density of the distribution at several

Dear list I am using the qq.plot command to create quantile-quantile plots. The plot should display a 45 degree reference line upon which the points of the graph should fall if the two distributions being examined are roughly equal. If I try: x<-rchisq(100, df=6) qq.plot(x, dist="chisq", df=6) Then I get a quantile plot which has an intercept of roughly 1 when the line should be

Guys: 1).When I using the 'quantile' function, I get really confused. Here is what I met: > x<-zoo(rnorm(500,0,1)) > quantile(x,0.8) 400 1.060258 > c=rnorm(500,0,1) > quantile(c,0.8) 80% 0.9986075 why do the results display different? Is that because of the different type of the class? 2).And I want to use the 'rollapply' function to compute a

Hi, I load my data set and separate it as folowing: presu <- read.table("C:/_Ricardo/Paty/qtdata_f.txt", header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) dep<-presu[,3]; exo<-presu[,4:92]; Now, I want to use it using the wls and quantreg packages. How I change the data classes for mle and rq objects? Thanks a lot, Ricardo

Dear all: I have a question about how to get the optimal estimate of coefficients using the penalized quantile regression (LASSO penalty in quantile regression defined in Koenker 2005). In R, I found both rq(y ~ x, method="lasso",lambda = 30) and rq.fit.lasso(x, y, tau = 0.5, lambda = 1, beta = .9995, eps = 1e-06) can give the estimates. But, I didn't find a way using either of

All, This worked: mBW <- function( ... ) ... # matrix-valued function BaconWatts <- function(formula, mmf=mBW, # model matrix function(x, bp, g) data, plot=T, tau=0.5 ) { ... m.nl <- nlrq(y ~ b0 + mBW(x,bp,g) %*% c(b1,b2), tau=tau, start=par0, trace=T )$m ... } For some reason the following reports a failure to find the

Hi, how to measure the goodness of fit, when using the rq() function of quantreg? I need something like an R^2 for quantile regression, a single number which tells me if the fit of the whole quantile process (not only for a single quantile) is o.k. or not. Is it possible to compare the (conditional) quantile process with the (unconditional) empirical distribution function? Perhaps with a Chi^2

Hi, everyone, Since quantile calculation has nine different methods in R, I wonder how I specify a method when calling the bwplot() in lattice. I couldn't find any information in the documentation. Thanks. -- Jun Shen PhD PK/PD Scientist BioPharma Services Millipore Corporation 15 Research Park Dr. St Charles, MO 63304 Direct: 636-720-1589 [[alternative HTML version deleted]]

Hi, Does anyone know how to compute the quantile of a mixture of four bivariate normal distriutions? Many thanks! Hannah [[alternative HTML version deleted]]

Hi, I am new to R, I am doing quantile normalization with a dat matix of 384X124 and I find that while computing the quantile normailzation it introduces 'NA' into some of the cells, can someone help me to overcome this problem ? This is the command that goes like upto g62 for 124 colomns >g1 <- normalize.quantiles(exprs(MSExpr[,1:2])) For a small set of data there is no problem,

Hi all, does anybody know about R implementations for quantile regression for longitudinal data? I am just aware of a very basic version of R. Koenker's approach using fixed effects. Thanks in advance Armin

Dear Dimitris, I have exactly the same problem than you, Do you get some solution? Thanks, Lola Lola Gadea Profesora titular de Economía Aplicada/Lecturer in Applied Economics Universidad de Zaragoza/University of Zaragoza (Spain) lgadea@unizar.es <http://estructuraehistoria.unizar.es/personal/lgadea/index.html>http://estructuraehistoria.unizar.es/personal/lgadea/index.html Grupo de

Hi,everyone I have some trouble in understanding the formula. http://r.789695.n4.nabble.com/file/n3033305/%E6%9C%AA%E5%91%BD%E5%90%8D.jpg http://r.789695.n4.nabble.com/file/n3033305/%E6%9C%AA%E5%91%BD%E5%90%8D1.jpg which is correct? best wish. thanks -- View this message in context: