similar to: data argument and environments

Hi, I have used package quantreg to estimate a non-linear fit to the lowest part of my data points. It works great, by the way. But I'd like to extract the predicted values. The help for predict.qss1 indicates this: predict.qss1(object, newdata, ...) and states that newdata is a data frame describing the observations at which prediction is to be made. I used the same technique I used

Rers: I installed R 2.9.0 from the Debian package manager on our amd64 system that currently has 6GB of RAM -- my first question is whether this installation is a true 64-bit installation (should R have access to > 4GB of RAM?) I suspect so, because I was running an rqss() (package quantreg, installed via install.packages() -- I noticed it required a compilation of the source) and

Hello, i have the following data: x=c(0,0.02,0.03,0.04,0.05,0.06,0.07,0.08,0.09,0.1,0.11,0.12,0.13,0.14,0.15,0.16,0.17,0.18,0.19,0.2,0.21,0.22,0.23,0.25,0.26,0.27,0.46,0.47,0.48,0.49) y=c(0.48,0.46,0.41,0.36,0.32,0.35,0.48,0.47,0.55,0.56,0.54,0.67,0.61,0.60,0.54,0.51,0.45,0.42,0.44,0.46,0.41,0.43,0.43,0.48,0.48,0.47,0.39,0.37,0.32,0.29) and tried to get piecewise linear regression. Doing a

Dear all, My machine is SUN Java Workstation 2100 with 2 AMD Opteron CPUs and 16GB RAM. R is compiled as 64bit by using SUN compilers. I trying to fit quantile smoothing on my data and I got an message as below. > fit1<-rqss(z1~qss(cbind(x,y),lambda=la1),tau=t1) Error in as.matrix.csr(diag(n)) : cannot allocate memory block of size 2496135168 The lengths of vector x and y are

I'm in the very initial stage of expanding the formula processing in my quantile regression function rq() to handle additive nonparametric components, say qss(x), or qss(x,z). I need some advice about strategy for formula processing. My initial foray was to use: terms(formula,specials="qss") and then modify the components of the resulting terms.object. But in changing formula

Hi, I need to fit a piecewise linear regression. x = c(6.25,6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50,50.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00) y = c(0.328,0.395,0.321,0.239,0.282,0.230,0.273,0.347,0.211,0.210,0.259,0.186,0.301,0.270,0.252,0.247,0.277,0.229,0.225,0.168,0.202) there are two change points. so the fitted curve should look like \ \ /\

Hi, Let me pick up this old thread. How does one extract the locations of the knots (ends of the segments) from the fit object below? Thanks, Vadim >From : roger koenker < roger_at_ysidro.econ.uiuc.edu > Date : Tue 31 May 2005 - 10:23:19 EST It is conventional to fit piecewise linear models by assuming Gaussian error and using least squares methods, but one can argue that

Suppose I have a formula like this: f <- y ~ qss(x, lambda = lambdas[1]) + qss(z, lambdas[2]) + s I?d like a function, g(lambdas, f) that would take g(c(2,3), f) and produce the new formula: y ~ qss(x, lambda = 2) + qss(z, 3) + s For only two qss terms I have been using g <- function(lambdas, f){ F <- deparse(f) F <- gsub("lambdas\\[1\\]",lambdas[1],F) F

Recursively walk the formula performing the replacement: g <- function(e, ...) { if (length(e) > 1) { if (identical(e[[2]], as.name(names(list(...))))) { e <- eval(e, list(...)) } if (length(e) > 1) for (i in 1:length(e)) e[[i]] <- Recall(e[[i]], ...) } e } g(f, lambdas = 2:3) ## y ~ qss(x, lambda = 2L) + qss(z, 3L) + s On Fri, Oct

Dear All, I'm trying to construct confidence interval for an additive quantile regression model. In the quantreg package, vignettes section: Additive Models for Conditional Quantiles http://cran.r-project.org/web/packages/quantreg/index.html It describes how to construct the intervals, it gives the covariance matrix for the full set of parameters, \theta is given by the sandwich formula

About 2 percent of the time I use fix() to edit a function that is sitting in .RData I get the response: > fix(qss) Error in edit(name, file, editor) : problem with running editor vi when I try to close the editing session. I used to think that these were always cases where there was some syntactical error with the edited file, but this is not the case. I realize that one surefire way to

Dear All, Just when I thought I had the plague of gfortran-9 under control, I made the tactical error of allowing my mac mini to ?upgrade? to macOS 10.14.6 which apparently also upgraded Xcode to 10.3. In consequence I?m having difficulty building my packages. The current symptom is: /usr/local/clang7/bin/clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG

R-ers: I was wondering if anyone had suggestions on how to implement a GAM in a quantile fashion? I'm trying to derive a model of a "hull" of points which are likely to require higher-order polynomial fitting (e.g. splines)-- would quantreg be sufficient, if the response and predictors are all continuous? Thanks! --j

I'd like to mention that there is a new quantile regression package "nprq" on CRAN for additive nonparametric quantile regression estimation. Models are structured similarly to the gss package of Gu and the mgcv package of Wood. Formulae like y ~ qss(z1) + qss(z2) + X are interpreted as a partially linear model in the covariates of X, with nonparametric components defined as

Thierry, I have this: if (require(MatrixModels) && require(Matrix)) { X <- model.Matrix(Terms, m, contrasts, sparse = TRUE) in my function rqss() I've tried variants of requireNamespace too without success. If I understand properly model.Matrix is from MatrixModels but it calls sparse.model.matrix which is part of Matrix, and it is the latter function that I'm not

Hello all, I wish to fit a loess smother to a plot of Y`X, but in predicting the 95% quantile. Something that will be a combination of what rq (package quantreg} does, with loess. Is there a function/method for doing this? Thanks, Tal ----------------Contact Details:------------------------------------------------------- Contact me: Tal.Galili@gmail.com | 972-52-7275845 Read me:

loess() should be able to do robust 2D smoothing. There's no natural ordering in 2D, so defining running medians can be tricky. I seem to recall Prof. Koenker talked about some robust 2D smoothing method at useR! 2004, but can't remember if it's available in some packages. Andy From: Vladislav Petyuk > > Hi, > Are there any multidimenstional versions of runmed() and >

I'm having difficulty with Rprof. I have a documentation example test.qss that runs fine without profiling, but under Rprof, > Rprof() > source("test.qss") Error in standardGeneric("model.matrix") : UseMethod used in an inappropriate fashion Luke wrote about a similar circumstance last summer: # From: Luke Tierney (luke@stat.umn.edu) # Date: Fri Jul

Hello everyone, I made a big effort to provide an mp3 stream on my site to please the unenlightened masses, and then I see in my logs that it doesn't work in Windows Media Player. Doh! There's no Windows machine around to test on so if anyone could help me out, I'd be very grateful. I couldn't find anything in the list's archive or on the web in general, sorry. Details:

Dear friends. On p 95 in 3. ed. MASS a zero-truncated Poisson distribution is analyzed. I understand the probability distribution and expected mean. The Newton iteration scheme is Lam(m+1)=Lam(m)-[Lam(m)-Ybar(1-exp(-Lam(m))]/[1-Ybar*exp(-Lam(m)], and I suppose the latter part should be f(Lam(m))/f ' (Lam(m)) and f(Lam(m)) is Lam(m)/(1-exp(-Lam(m)), right ? But then f ' (Lam(m)) is