search for: qss

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(&q...

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 at factors to drop the qss columns and rows, I ran afoul of met...

...acement: 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 23, 2020 at 9:33 AM Koenker, Roger W <rkoenker at illinois.edu> wrote: > > 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) t...

I'm having difficulty with an environmental issue: I have an additive model fitting function with a typical call that looks like this: require(quantreg) n <- 100 x <- runif(n,0,10) y <- sin(x) + rnorm(n)/5 d <- data.frame(x,y) lam <- 2 f <- rqss(y ~ qss(x, lambda = lam), data = d) this is fine when invoked as is; x and y are found in d, and lam is found the .GlobalEnv, or at least this is how I understand it. Now, I'd like to have a function say, h <- function(lam) AIC(rqss(y ~ qss(x, lambda = lam), data = d)) but now,...

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 functions of z1 and z2. Rather than estimating conditional mean functions, conditional quantile functions are estimated using penalty methods. When z1 is univariate fitt...

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 to extract predicted values of GAM fits with mgcv package: if I fit a GAM using x as the x variable on...

...6.25,12.50,12.50,18.75,25.00,25.00,25.00,31.25,31.25,37.50,37.50,5 0.00,50.00,62.50,62.50,75.00,75.00,75.00,100.00,100.00) 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) library(quantreg) plot(x,y) fit <- rqss(y ~ qss(x)) plot(fit) it gives 5 segments not 3, but this can be controlled by the choice of lambda in the qss function, for example, try: fit <- rqss(y ~ qss(x,lambda=3) plot(fit,col="red") which gives a fit like you suggest might be reasonable with only three segments. ... s...

...1,0.60,0.54,0.51,0.45,0.42,0.44,0.46,0.41,0.43,0.43,0.48,0.48,0.47,0.39,0.37,0.32,0.29) and tried to get piecewise linear regression. Doing a simple spline smoothing gives the basic shape of the expected curve: plot(x,y) lines(smooth.spline(x, y), lty=2, col = "red") Now I tried to do rqss. But what I got was only a straight line and nothing appropriate. No parameter change did help. Can anyone tell me what's wrong with this approach? library(quantreg) fit <- rqss(y ~ qss(x)) plot(fit) points(x,y) lines(smooth.spline(x, y), lty=2, col = "red") Thank You in advance....

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 \ \ /\

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 both 17664. I tried and found that the same command ran with x[1:16008] and y[1:16008]. So, it looks to me a memory related problem, but I...

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 watched the memory usage spike to 4.9GB (my input data contains > 500,000 samples). With this said, after 30 mins or so of processing, I got the following error: tahoe_rq &lt...

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.ed...

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 avoid this problem i...

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:

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

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

Hola, I've done everything as correct as I can see in smb.conf under fresh ubuntu 6.06 fully updated install to have it run as a PDC on hostname florentine, domain DAVEYST. There are no testparm errors. I've added users with useradd and smbpasswd -a I've added machines with useradd and smbpasswd -a -m I can see the server in my network neighbourhood and access/browse folders on