similar to: nlrq coefficients querry

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

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 error: Error in match.call(func, call = cll) :

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

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

I have a need to connect to a separate database in order to get some information from that DB but I am trying to figure out the most elegant way to do it. With this application there will be times when a new DB will be added to the mix and I would rather not have to add models to my app every-time a new database is added. I could do it this way if that is how everyone tells me to go, but

Need to know the setting which generates following message every 300 seconds Name Query <1d> - Broadcast message every 300 seconds This is Coming from the samba clients running on a Solaris box. Using this with a WINS server where the setting for refresh interval of name is 6 days.

Hi R users, I want to calculate the partial correlation (first or second order) and their corresponding significance test (p-value). I used > library(corpcor) and "cor2pcor" function to calculate partial correlation but could not calculate "p-value". If some one knows the R package or function to calculate corresponding "p-value", It will be great help. Thanks  

Apologies for thickness - I'm sure that this operates as documented and with good reason. However... My understanding of arima.sim() is obviously imperfect. In the example below I assume that x1 and x2 are similar white noise processes with a mean of 5 and a standard deviation of 1. I thought x3 should be an AR1 process but still have a mean of 5 and a sd of 1. Why does x3 have a mean of ~7?

Dear R users, I am new users of this software. I want to make box plot. Here, i have simulated data set with following commands: x<-matrix(rnorm(90),nrow=10,ncol=9) x a<-matrix(c(1,1,1,1,1,2,2,2,2,2),nrow=10,ncol=1) xx<-cbind(a,x)

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