similar to: Parameter Constraints in nls.lm()

Hello, I want to solve a nonlinear 3SLS problem with "nlsystemfit()". The equations are of the form y_it = f_i(x,t,theta) The functions f_i(.) have to be formulated as R-functions. When invoking "nlsystemfit()" I get the error Error in deriv.formula(eqns[[i]], names(parmnames)) : Function 'f1' is not in the derivatives table

Hi all, it is possible to invoke certain graphical functions (e.g. curve) with an expression argument, e.g. "ylab=expression(phi)". There are some greek letters with a second script. For instance, in latex two symbols do exist: phi and varphi. Is the second symbol also available in an expression()? If yes, how? Kind regards, Kilian

new klibc sparc build failure against gcc 4.2 ----- Forwarded message from Kilian Krause <kilian at debian.org> ----- Subject: Bug#440721: FTBFS on sparc while linking usr/klibc/libc.so From: Kilian Krause <kilian at debian.org> To: Debian Bug Tracking System <submit at bugs.debian.org> Date: Mon, 03 Sep 2007 23:35:23 +0200 Package: klibc Version: 1.5.6-2 Severity: serious

Hi there, I'm a PhD student investigating growth patterns in fish. I've been using the minpack.lm package to fit extended von Bertalanffy growth models that include explanatory covariates (temperature and density). I found the nls.lm comand a powerful tool to fit models with a lot of parameters. However, in order to select the best model over the possible candidates (without covariates,

SAS has DUD (Does not Use Derivatives)/Secant Method for nonlinear regression, does R offer this option for nonlinear regression? I have read the helpfile for nls() and could not find such option, any suggestion? Thanks, Derek [[alternative HTML version deleted]]

We would like to fit parameters using a simulation with stochastic processes as theoretical values. We generate a simple exemple with nls.lm to see the logic and the problem: First without stochasticity (it is a dummy example, the fited value is simple the mean of a set of 10 numbers): #Ten numbers x <- 1:10 #Generate 10 Gaussian random number with mean=3 sd=1 simy <- rnorm(length(x),

Dear R useRs, I have a problem with nls.lm function of minpackl.lm package. I need to fit the Van Genuchten Model to a set of data of Theta and hydraulic conductivity with nls.lm function of minpack.lm package. For the first fit, the parameter estimates keep changing even after 1000 iterations (Th) and I have a following error message for fit of hydraulic conductivity (k); Reason for

As it turns out, on cards that actually have CRTCs on them we're already calling drm_kms_helper_poll_enable(drm_dev) from nouveau_display_resume() before we call it in nouveau_pmops_runtime_resume(). This leads us to accidentally trying to enable polling twice, which results in a potential deadlock between the RPM locks and drm_dev->mode_config.mutex if we end up trying to enable polling

Is there a way to use R as a client server application? Whitch means: 1. install R on a server (running as a service) 2. from client send R-scripts through IDE/Editor (TINN-R) to "R-server" 3. receive R-output on client All the Best Kilian Schwab

Hello. I try to compute a "White-corrected" ANOVA. Therefore I use the command "Anova" implemented in the car()-package): Anova(modelname, white.adjust="hc3") and receive the error message: "Fehler in SS[i] <- SS.term(names[i]) : nichts zu ersetzen" (German) "Error in SS[i] <- SS.term(names[i]) : nothing to replace" (probably being the

Hi there, I'm new to R but I thought this is the most likely place I could get advice or hints w.r.t the following problem: I have a series of measurements xi with associated uncertainties dxi. I would like to construct the probability density histogram of this data where each density estimate has an associated error that is derived from the dxi. In other words, for large dxi the

I'm sure this must be a nls() newbie question, but I'm stumped. I'm trying to do the example from Draper and Yang (1997). They give this snippet of S-Plus code: Specify the weight function: weight < - function(y,x1,x2,b0,b1,b2) { pred <- b0+b1*x1 + b2*x2 parms <- abs(b1*b2)^(1/3) (y-pred)/parms } Fit the model gmfit < -nls(~weight(y,x1,x2,b0,b1,b2),

Hello, i have a linux box running samba as a fileserver for 4 Windows NT 4.0 Workstations. the performance is good on responing-times but loading and writing large files (more than 500 kByte) is too slow. All the users ale working as guest-users. Can this be the cause for my problem? This is my smb.conf file: **************************************************************************** [global]

Hi rsync developers, I'm trying to get some fancy mirroring done with selecting files from a larger archive through the --files-from option. The only problem that I do see is that rsync will not eat the --delete option whatever I try. Thus, all the old files remain in the mirror archive instead of being deleted. To be more clearly, I want the rsync --files-from option to behave like

Can some one give me a tip on how do I integrate two asterisks Thanks Felippe Kilian Martins NPD-UFSC ---------------------------------------------------------------- This message was sent using IMP, the Internet Messaging Program.

dear R experts---quick question. I need to estimate a model that looks like y = (b*T+d*T^3) + (1-b-3*d*T^2)*x + (3*d*T)*x^2 + (-d)*x^3 I only have three parameters. Is nls() the right tool for the job, or is there something faster/better? /iaw ---- Ivo Welch (ivo.welch@brown.edu, ivo.welch@gmail.com) [[alternative HTML version deleted]]

I am using nlsLM {minpack.lm} to find the values of parameters a and b of function myfun which give the best fit for the data set, mydata. mydata=data.frame(x=c(0,5,9,13,17,20),y = c(0,11,20,29,38,45)) myfun=function(a,b,r,t){ prd=a*b*(1-exp(-b*r*t)) return(prd)} and using nlsLM myfit=nlsLM(y~myfun(a,b,r=2,t=x),data=mydata,start=list(a=2000,b=0.05), lower = c(1000,0),

Dear R-users; I'm working with a a dataset that was previously used to fit a nonlinear model of the form: Y ~ a * (1 + b * log(1 - c * X^d)) The parameters published elsewhere are: a = 1.758863, b = .217217, c = .99031, and d = .054589 However, there is no way I can replicate this result. I've tried several options (including SAS) w/o success. The data is: X <-

All- I am interested in estimating a parameter that is the starting value for an ODE model. That is, in the typical combined fitting procedure using nls and lsoda (alternatively rk4), I first defined the ODE model: minmod <- function(t, y, parms) { G <- y[1] X <- y[2] with(as.list(parms),{ I_t <- approx(time, I.input, t)$y dG <- -1*(p1 + X)*G +p1*G_b dX <-

Greetings R users, maybe there is someone who can help me with this problem: I define a function "optim.fun" and want as output the sum of squared errors between predicted and measured values, as follows: optim.fun <- function (ST04, SM08b, ch2no, a, b, d, E) { predR <- (a*SM08b^I(2)+b*SM08b+d)*exp(E*((1/(283.15-227.13))-(1/(ST04+273.15-227.13)))) abserr