search for: nonlinearities

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

Hi, I have a list of fit objects (fit objects from HMISC functions) I create elements in the list in this way lrm.sumtot <- lrm( ae7bepn ~ trarm + sumtot , data=sd.fix) lrm.list[['lrm.sumtot']] <- lrm.sumtot And I can run (anova(lrm.sumtot)) The following also gives the anova I'd expect zz <- lrm.list[['lrm.sumtot']];anova(zz) And similarly for the summary

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,

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.

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

Hi It is preferable to echo your posts to r-help, you usually get more answers and some definitelly superb to mine. It is also better to start a new mail if your question has nothing to do with original subject "Maura E Monville" <maura.monville at gmail.com> napsal dne 01.10.2007 17:44:43: > Unluckily I do not have the privilege of practising with R all day > long. I

Hi, I am curious about how to interpret the table produced by anova(ols(...)), from the Design package. I have a multiple linear regression model, with some interaction, defined by: ols(formula = log(ksat * 60 * 60) ~ log(sar) * pol(activity, 3) + log(conc) * pol(sand, 3), data = sm.clean, x = TRUE, y = TRUE) n Model L.R. d.f. R2 Sigma 1834 1203

Hello colleagues, I am attempting to determine the nonlinear least-squares estimates of the nonlinear model parameters using nls. I have come across a common problem that R users have reported when I attempt to fit a particular 3-parameter nonlinear function to my dataset: Error in nls(r ~ tlm(a, N.fix, k, theta), data = tlm.data, start = list(a = a.st, : step factor 0.000488281

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

Dear all, I would like to know whether any specification test for linear against nonlinear model hypothesis has been implemented in R using the quantreg package. I could read papers concerning this issue, but they haven't been implemented at R. As far as I know, we only have two specification tests in this line: anova.rq and Khmaladze.test. The first one test equality and significance of

...l me about nonlinear effects? e.g. lm(y~ d1 + d2 + d3 + d4 + d5 + d1*d2) etc Does this make any sense? If so, please suggest a good way to go about this; how to set up the dummy variables and how to interpret the results. Ideally, the same lm() fit would tell me about the linear effect y~x and the nonlinearities. Both sorts of effect will co-exist. Thanks very much for any help! Bill

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 have been trying to figure out how to use the nonlinear regression to fit the cumulative lognormal distribution to a number of data points I have but I am a new R user and I cant quite decipher the notes on nonlinear regression. Any help in this regard will be greatly appreciated, my email address is mmiller at nassp.uct.ac.za

Hi there: I'm trying to run a large number of nonlinear regressions on a time series dataset, where the data will be formatted something to the effect of: ObservationID,time,dependentvar I'll have a number of time and dependentvars for each observation, and I want to apply a nonlinear regression to one ObservationID at a time, and I want to have a dataset that is the parameter

Dear all, I just know how to solve an eaquation by using optim() or nlm(). But, now, I have three nonlinear equations, how could we use optim() or nlm() to solve  a system of nonlinear equations in R?  Thank you so much. Sincerely, Joe ___________________________________________________ 您的生活即時通 － 溝通、娛樂、生活、工作一次搞定！ [[alternative HTML version deleted]]

Dear R community, Using nlme library I have developed a nonlinear mixed model. Incorporating an autoregressive model gives me an error that I can't allocate vector of size X. The problem is that my computer does not have enough physical memory most probably due to a large number of observations (17,000). I was wondering what alternative options I might use: 1) To use ARIMA and

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

Hello! I am running R-2.3.1-i386-1 on Slackware Linux 10.2. I am a former matlab user, moving to R. In matlab, via the cftool, I performed nonlinear curve fitting using the method "nonlinear least squares" with the "Trust-Region" algorithm and not using robust fitting. Is it possible to perform the same analysis in R? I read quite a lot of R documentation, but I could not find

Could someone help me on the following: SAS has DUD (Does not Use Derivatives) for nonlinear regression. Does "R" has a similar capability? I am not good at derivatives and may get my derivative wrong before feeding it to a nonlinear regression procedure. Any help would be much appreciated. Liu Hancock Forest Management NZ Tokoroa, New Zealand DDI: 07-8850387 Mobile: 021-1576178

Dear All I am dealing at the moment with optimization problems with nonlinear constraints. Regenoud is quite apt to solve that kind of problems, but the precision of the optimal values for the parameters is sometimes far from what I need. Optim seems to be more precise, but it can only accept box-constrained optimization problems. I read in the list archives that optim can also be used with