similar to: linear against nonlinear alternatives - quantile regression

Hello. I am a beginer in R and I can not implement the KhmaladzeTest in the following command. Please help me!!!!!!!!!!! PD: I attach thw results and the messages of the R program R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) ISBN 3-900051-07-0 R es un software libre y viene sin GARANTIA ALGUNA. Usted puede redistribuirlo bajo ciertas

Hey, My dataset has 1 dependent variable(Logloss) and 7 independent dummy variables(AS,AM,CB,CF,RB,RBR,TS) , it's attached in this email. The problem is I cant finish Khmaladze test because there's an error "Error in switch(mode(x), "NULL" = structure(NULL, class = "formula"), : invalid formula" which I really dont know how to fix. My R version is 2.7.2.

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) :

Dear all, I am a new user in r and I am facing some problems with the quantile regression specification. I have two matrix (mresultb and mresultx) with nrow=1000 and ncol=nsim, where I specify (let's say) nsim=10. Hence, the columns in my matrix represents each simulation of a determined variable. I need to regress each column of mresultb on mresultx. My codes are the following:

Dear all, I'm interested in testing 2 nonlinear restrictions on coefficients of a nls object. Is there a package for doing this? Something in the lines of `test(nls object, res=c("res 1","res 2"),...)' I only found the function delta.method in the alr3 library that calculates the se of a singleton nonlinear restriction of a nls object using the delta method. Thanks in

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,

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

Dear R users I recently migrated from Statistica/SigmaPlot (Windows) to R (Linux), so please excuse if this may sound 'basic'. When running a nonlinear regression (V = Vmax * conc / (Ks + conc), i.e. Michaelis-Menten) on SigmaPlot, I get the output listed below: >>>Begin SigmaPlot Output<<< R = 0.94860969 Rsqr = 0.89986035 Adj Rsqr = 0.89458984 Standard Error of

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

Dear all, I am trying to compare the estimated coefficients of a quantile regression model between two different samples. It is a Wald test, but I cannot find one way to do that in R.The samples are collected conditional on a specific characteristic and I would like to test whether such characteristic indeed affect the estimators. The problem in the test anova.rq is that the response variable

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),

It is easy to spot response nonlinearity in normal linear models using plot(something.lm). However plot(something.glm) produces artifactual peculiarities since the diagnostic residuals are constrained by the fact that y can only take values 0 or 1. What do R users find most useful in checking the linearity assumption of logistic regression (i.e. log-odds =a+bx)? Patrick Foley patfoley at

Hi, is there any method for goodness of fit testing of an (as general as possible) univariate distribution with parameters estimated, for normal, exponential, gamma distributions, say (e.g. the corrected p-values for the Kolmogorov-Smirnov or Chi-squared with corresponding ML estimation method)? It seems that neither ks.test nor chisq.test handle estimated parameters. I am aware of function

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

Dear colleagues, Von Bertalanffy model is commonly adjust to data on fish length (TL) and age (AGE) TL= Linf*(1-exp(-K*(AGE-t0)). Linf, K and t0 are parameters of the model. One main goal of the growth study is the comparison of growth parameter estimates between sexes of the same species, or estimates from different populations. The realibility statistical tests normally applied are highly

I have installed samba 3.0alpha20 and cups-1.1.18 on a freebsd 4.8 box. After reading all the documentation that samba provides as well as the cups administrators guide I am still stuck. I can print at the moment OK from my freebsd box to my network printer using cups, i can print from the shell and from a GUI (GNOME). I also have a professional w2k as a client and i want to print in my network

Hi, I have been brought back to the "R-Side" from MatLab. I have used R in graduate econometrics but only for statistics and regression (linear and nonlinear). But now I need to run general nonlinear optimization. I know about the add-in quadprog but my problem is not QP. My problem is a general nonlinear (obj funct) with linear constraints.I know about the "ms" and

Dear R-Help, I am trying to use R to solve a nonlinear equation many times for different values. I am looking for a mathematical nonlinear equation solution which may not have a closed solution form. For example, I have equation: 2 = (t^2)/log(t) What is t? I am wondering how to solve it in R. Many thanks, Zhu Wang Statistical Science Department SMU.

I have the following set-up. 6 values of a continuous variable (let's say light intensity) are presented to a system. The input is presented as a random series of blocks lasting (say) 5 sec each. ---- ---- ---- etc ---- time -> The output is measured and sampled at say 10 samples/sec. Please ignore the fact that this is a time series and

I am using nonlinear regression to fit a couple of variables to a set of measurements. I would like to do some significance tests for the estimated parameters. I am able to check the confidence intervals using the Jacobian coming out of nonlinear regression. I do see in a paper which shows t-value (it says estimated by White method??), f-value, f-test, and j-test, are these available in matlab,