search for: nlrq

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 diff...

...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 would expect to get errors either every time, or never. ### library(stats) # or library(nls) if using R < 1.9.0 library(nlrq) test <- data.frame(x = c(7.60090245954208, 6.90775527898214, 6.21460809842219, 5.52146091786225, 4.6051...

Dear all, I sent an email on Friday asking about nlrq {quantreg}, but I haven't received any answer. I need to estimate the quantile regression estimators of a model as: y = exp(b0+x'b1+u). The model is nonlinear in parameters, although I can linearise it by using log.When I write: fitnl <- nlrq(y ~ exp(x), tau=0.5) I have the following err...

...m 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 the quantile of interest and the particular dataset) tend to result in illogical regression results with the nlqr package. To test this, I created artificial datasets that were based on underlying asymptotic or...

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), data=dat, tau=0.25, trace=TRUE) or dat.nlrq <- nlrq(BM ~ poly(Regen100^2), data=dat, tau=0.25, trace=TRUE) (I don't know the difference) both gave me the...

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 the response. I hope that this helps. Roger url: www.econ.uiuc.edu/~roger/my.html Roger Koenker email rkoenker at uiuc.edu Department of Economics vox: 217-333-4558 University of Illin...

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 sol...

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]]

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

...y dataset. Unfortunately I'm running into a problem with the median and other regression lines with tree age younger than 50 years, basically the median is in this range overestimated and even comes out of the rage of oberservations. here is the code I'm using. # then fit the median using nlrq spruce.nlrq <- nlrq(height ~ SSlogis(age, Asym, mid, scal),data=spruce, tau=0.5, trace=TRUE) lines(1:200, predict(spruce.nlrq, newdata=list(age=1:200)), col="2") I believe  this has something to do with the SSlogis, as this gives the parameters for an S curve.  My data set does not...

...parently R seems not to accept parameters on the left hand side of a regression model. I know that my do-it-yourself strategy is not necessary, since the package box-cox is available. Unfortunately, I want the use the box-cox transformation in a quantile regression, i.e. I have to replace nls by nlrq in the call above. Any suggestions? Thanks and best regards, Johannes Ludsteck <><><><><><><><><><><><><><><><><><> Johannes Ludsteck Institut fuer Volkswirtschaftslehre Lehrstuhl Prof. Dr...

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 function mmf() mBW <- function( ... ) ... BaconWatts <- function(formula, mmf=mBW, # model matrix function(x, bp, g)...

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...

...regression for this dataset (including a tau = 0.1, 0.25, 0.5, 0.75 and 0.90 ). The problem is: I read very much many manuals about Quantile regression and the operational use of R, but I did not get to put parametrs in R to run this non-linear analysis. Might the function in R be the following? nlrq(formula, data=parent.frame(), start, tau=0.5, control, trace=FALSE,method="L-BFGS-B") What's the formula I could put here for my data?? How I put my file in this function? Might it be as [scan(file=read.dat" ]?? But where? At last, one confirmation: Can I run log X log analysi...

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Hi All, Could any one tells me if R or S has the capacity to fit nonlinear regression with Huber's M estimation? Any suggestion is appreciated. I was aware of 'rlm' in MASS library for robust linear regression and 'nls' for nonlinear least squares regression, but did not seem to be able to find robust non-linear regression function. Thanks and regards, Ray Liu

Hi, I'm trying to use Robust non-linear regression to fit dose response curves. Maybe I didnt look good enough, but I dind't find robust methods for NON linear regression implemented in R. A method that looked good to me but is unfortunately not (yet) implemented in R is described in http://www.graphpad.com/articles/RobustNonlinearRegression_files/frame.htm

Your understanding isn't similar to mine. Mine says robust/resistant methods are for data with heavy tails, not heteroscedasticity. The common ways to approach heteroscedasticity are transformation and weighting. The first is easy and usually quite effective for dose-response data. The second is not much harder. Both can be done in R with nls(). Andy From: Quin Wills > > I am

I have a loop that increases the size of an object after each iteration. When the Windows Task Manager shows "Mem Usage" about 1.8GB, the Rgui.exe process no longer responds. I use: "C:\Program Files\R\rw1080\bin\Rgui.exe" --max-mem-size=4000M --min-vsize=10M --max-vsize=3000M --min-nsize=500k --max-nsize=1000M I have a dual Xeon 2.8GHz processor box with 4GB of memory and

...and to be convex/concave for bivariate components. Fitting is done by new sparse implementations of the dense interior point (Frisch-Newton) algorithms already available in the package quantreg. The new package "nprq" thus supplements the existing packages "quantreg" and "nlrq" that can be used for linear and nonlinear parametric quantile regression fitting respectively. In particular, "nprq" provides general fitting functions for quantile regression problems with sparse design matrices paralleling the functionality of least squares function slm() in the...