similar to: nlrq {quantreg}

I have been quantreg library for a number of projects but have just hit a snag. I am using nlrq to examine an asymptotic relationship between 2 variables at the 99th percentile. It performs as expected, however when I try to extract the coefficients along with se and significance I am running into problems. The problem is that for the nlrq regression Dat.nlrq, summary(Dat.nlrq) reports a different

Dear List, Apologies if this is a known problem ... I wasn't able to find it on the bug list, but it is a problem that does not seem to occur with a MAC build of R 2.0, so perhaps this problem has already been addressed for the future. I am getting *sporadic* errors when refitting the same model to the same data set, using nlrq() in the nlrq package. The algorithm is not stochastic, so I

Hi there, Can anyone please tell me if it is possible to limit parameters in nlrq() to 'upper' and 'lower' bounds as per nls()? If so how?? Many thanks in advance

In "lm" command, we can use "vcov" option to get variance-covariance matrix. Does anyone know how to get variance-covariance matrix in nlrq? Thanks, Kate [[alternative HTML version deleted]]

Dear r-help members I posted this message already yesterday, but don't know whether it reached you since I joined the group only yesterday. I would like to estimate the boxcox transformed model (y^t - 1)/t ~ b0 + b1 * x. Unfortunately, R returns with an error message when I try to perform this with the call nls( I((y^t - 1)/t) ~ I(b0 + b1*x), start = c(t=1,b0=0,b1=0), data = mydataframe)

Johannes, You can minimize an model expression by just putting the ~ on the left and everything else on the righthand side, but I don't think that this is really what you want. In the NLS expression this would ignore the jacobian of the transformation from errors to response, and in nlrq there is the same problem, however you can adjust for the jacobian by rescaling by the geometric mean of

Hello. I am trying to fit an asymptotic relationship (nonlinear) to some ecological data, and am having problems. I am interested in the upper bound on the data (i.e. if there is an upper limit to 'y' across a range of 'x'). As such, I am using the nonlinear quantile regression package (nlrq) to fit a michaelis mention type model. The errors I get (which are dependant on

Hi, I'm relatively new to R and need to do a quantile regression. Linear quantile regression works, but for my data I need some quadratic function. So I guess, I have to use a nonlinear quantile regression. I tried the example on the help page for nlrq with my data and it worked. But the example there was with a SSlogis model. Trying to write dat.nlrq <- nlrq(BM ~ I(Regen100^2),

Hello, I?m with a problem in using nonlinear quantile regression, the function nlrq. I want to do a quantile regression o nonlinear function in the form a*log(x)-b, the coefficients ?a? and ?b? is my objective. I try to use the command: funx <- function(x,a,b){ res <- a*log(x)-b res } Dat.nlrq <- nlrq(y ~ funx(x, a, b), data=Dat, tau=0.25, trace=TRUE) But a can?t solve de problem,

Hi, I'm attempting to calculate the 0.25 and 0.97 quantiles for tree height (0-50 meters) against tree age (0-300 years) and I am running into some difficulty with the plotted grafic. I've run the examples in the quantreg help and can get those to work properly and by plugging in my data I can also get the lines plotted on my dataset. Unfortunately I'm running into a problem with the

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

I used the nlrq function in the package "quantreg". There is a lambda in the output when I set trace=TRUE. With different start point, the results are converged, but the last lambda is different. I want to know the meaning "lambda=1" and "lambda=0". Many Thanks! Examples of output 1. Where the last lambda=1: 108.6581 : 0.3 8.0 iter 0 value 108.658087 final

Please, I have a problem with nonlinear quantile regression. My data shows a large variability and the quantile regression seemed perfect to relate two given variables. I got to run the linear quantile regression analysis and to build the graph in the R (with quantreg package). However, the up part of my data dispersion seems a positive exponential curve, while the down part seems a negative

I'm translating llvm's intermediate representation, after optimization, to the intermediate representation of another optimizer. One of the problems I've run into is that llvm sometimes (although rarely) produces strangely sized integers after an opt pass with -O3 (in this example, 832 bits). I need to use 8, 16, or 32 bit integers for the other intermediate language. In short,

Here's something really strange. ******************************************************** R> x <- 1:5 R> x [1] 1 2 3 4 5 R> test1 function (x) { structure(x, call = sys.call()) } R> test1(x) [1] 1 2 3 4 5 attr(,"call") test1(x) R> test2 function (x) { attr(x, "call") <- sys.call() x } R> test2(x) Error: stack overflow

I''m a newbie...I have this CSS file created STYLES.CSS which i want to cll into every page under views...how can i do that...<link> tag doesn''t seem to be working here...any help is greatly appreciated...thanks! -- Posted via http://www.ruby-forum.com/.

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

Given a large data.frame, a function trains a series of models by looping over two steps: 1. Create a model-specific subset of the complete training data 2. Train a model on the subset data The function returns a list of trained models which are later used for prediction on test data. Due to how models and closures work in R, each model contains a lot of data that is not necessary for

Hi all, I got following problem in fitting the data. Any kind of suggestions are welcome > beta <- 3.5 > d <- seq(0.1,62.5,0.1) > y <- exp(-beta*d) > y1 <- y > x <- read.table("epidist.txt", header = TRUE) > data.nls <- as.data.frame(cbind(y1,x)) > #attach(data.nls) > nls.fit <- nls(y1~dist,data.nls) Error in cll[[1L]] : object of type

Can someone help me understand the following behavior of "[<-" ? If I define a simple class based on a matrix, the [<- operation only inserts into the first column: > x <- matrix(rnorm(10),nrow=5,ncol=2) > class(x) <- "foo" > "[<-.foo" <- function(x, i, j, value) { + if(missing(i)) i <- 1:nrow(x) + if(missing(j)) j <-