nls

I'm using nlsLM() to make a model of a power function, but I need to summarize my data within the function call to find the appropriate coefficient and exponent. More specifically, here is what my model code looks like: Jmod = nlsLM(value~(a)*summarise(funs(mean), (MW)^b), start = list(a=100000, b = 1/3), data = mod_data, upper = c(Inf,1), lower = c(0,1/5)) where MW is the data I am trying to use to predict value . The data for MW is already grouped by month based off a variable called datetime , so I would like to take the monthly average of MW^b where b is found by the nlsLM() statement. I

I'm trying to fit a curve over (the tail of) the following data: [1] 1 1 1 1 1 1 2 1 2 2 3 2 1 1 4 3 2 11 6 2 16 7 17 36 [25] 27 39 41 33 42 66 92 138 189 249 665 224 309 247 641 777 671 532 749 506 315 292 281 130 [49] 137 91 40 27 34 19 1 I'm using the following function in R to accomplish this: nls(y~a x exp(-b*x^2),start=list(a=1,b=1),trace=TRUE) However, I'm getting the following error: 3650202 : 1 1 Error in numericDeriv(form[[3L]], names(ind), env) : Missing value or an infinity produced when evaluating the model When using the following, artificial values for x and y, everything works

This is the most challenging thing I have done in R so far in that both nls and LPPL are fairly new to me. Below is a portion of script I have been working with. df is a data frame consisting of two columns, Date and Y, which are the closing prices for the S&P 500. I am not sure if it is relevant, but the dates start from 01-01-2003 through 12-31-2007. f <- function(pars, xx) {pars$a + pars$b*(pars$tc - xx)^pars$m * (1 + pars$c * cos(pars$omega*log(pars$tc - xx) + pars$phi))} # residual function resids <- function(p, observed, xx) {df$Y - f(p,xx)} # fit using Levenberg-Marquardt algorithm nls

I'm fitting some exponential data using nls . The code I'm using is: fit <- nls(y ~ expFit(times, A, tau, C), start = c(A=100, tau=-3, C=0)) expFit is defined as expFit <- function(t, A, tau, C) { expFit <- A*(exp(-t/tau))+C } This works well for most of my data, for which the starting parameters provided (100, -3 and 0) work well. Sometimes, though, I have data that doesn't go well with those parameters and I get errors from nls (e.g. "singular gradient" or things like that). How do I "catch" these errors? I tried to do something like fit <- NULL fit <- nls(...) if (is.null(fit)) { // Try nls

I'm trying to fit the conditional nls with R 2.15.1. The same code was working fine with R 2.13 but now R 2.15.1 throws errors. x <- seq(from = 17, to = 47, by = 5) y <- c(26.2, 173.6, 233.9, 185.9, 115.4, 62.0, 21.7) Data <- data.frame(y, x) Fit <- nls(formula = y ~ ifelse(test = x <= Mu, yes = Mean <- c1*exp(-((x-Mu)/Sigma11)^2), no = Mean <- c1*exp(-((x-Mu)/Sigma12)^2)), data = Data, start = list(c1 = 240, Mu = 25, Sigma11 = 5, Sigma12 = 14), algorithm = "port", lower = list(Sigma11 = 0, Sigma12 = 0)) The error is Error in nls(formula = y ~ ifelse(test = x <= Mu, yes = Mean <- c1 * exp(-((x