nls

问题 I'm trying to fit data with nls() function where the nature of data gives me bounds for one coefficient and for sum of two coefficients. Let me introduce short example to see where is the problem. I want parameter b1 to be between 0 and 1 and I want sum of parameters b1 and b2 to be between 0 and 1 as well. set.seed(123) # example where everything is OK x <- 1:200 g <- rbinom(200, 1, 0.5) y <- 3 + (0.7 + 0.2 * g) * x yeps <- y + rnorm(length(y), sd = 0.1) # both parameter b1 and sum of

问题 I'm trying to fit data with nls() function where the nature of data gives me bounds for one coefficient and for sum of two coefficients. Let me introduce short example to see where is the problem. I want parameter b1 to be between 0 and 1 and I want sum of parameters b1 and b2 to be between 0 and 1 as well. set.seed(123) # example where everything is OK x <- 1:200 g <- rbinom(200, 1, 0.5) y <- 3 + (0.7 + 0.2 * g) * x yeps <- y + rnorm(length(y), sd = 0.1) # both parameter b1 and sum of

问题 This is my first attempt at fitting a non-linear model in R, so please bear with me. Problem I am trying to understand why nls() is giving me this error: Error in nlsModel(formula, mf, start, wts): singular gradient matrix at initial parameter estimates Hypotheses From what I've read from other questions here at SO it could either be because: my model is discontinuous, or my model is over-determined, or bad choice of starting parameter values So I am calling for help on how to overcome this

问题 This is my first attempt at fitting a non-linear model in R, so please bear with me. Problem I am trying to understand why nls() is giving me this error: Error in nlsModel(formula, mf, start, wts): singular gradient matrix at initial parameter estimates Hypotheses From what I've read from other questions here at SO it could either be because: my model is discontinuous, or my model is over-determined, or bad choice of starting parameter values So I am calling for help on how to overcome this

问题 This is my first attempt at fitting a non-linear model in R, so please bear with me. Problem I am trying to understand why nls() is giving me this error: Error in nlsModel(formula, mf, start, wts): singular gradient matrix at initial parameter estimates Hypotheses From what I've read from other questions here at SO it could either be because: my model is discontinuous, or my model is over-determined, or bad choice of starting parameter values So I am calling for help on how to overcome this

问题 I a trying to fit a first order differential model using nlme and lsoda . Here is the basic idea: I first define the function allowing to generate the solution of the differential equation: library(deSolve) ODE1 <- function(time, x, parms) {with(as.list(c(parms, x)), { import <- excfunc(time) dS <- import*k/tau - (S-yo)/tau res <- c(dS) list(res)})} solution_ODE1 = function(tau1,k1,yo1,excitation,time){ excfunc <- approxfun(time, excitation, rule = 2) parms <- c(tau = tau1, k = k1, yo = yo1,

问题 Borrowing the example data from this question, if I have the following data and I fit the following non linear model to it, how can I calculate the 95% prediction interval for my curve? library(broom) library(tidyverse) x <- seq(0, 4, 0.1) y1 <- (x * 2 / (0.2 + x)) y <- y1 + rnorm(length(y1), 0, 0.2) d <- data.frame(x, y) mymodel <- nls(y ~ v * x / (k + x), start = list(v = 1.9, k = 0.19), data = d) mymodel_aug <- augment(mymodel) ggplot(mymodel_aug, aes(x, y)) + geom_point() + geom_line(aes

问题 I am trying to use nlsLM to fit experimental data. But for this example, I am using analytical data from another program so that I know the correct values. I am trying to use nls2 to give me starting points that I can use when using nlsLM. I was able to do this successfully with a less complex function, but it is not working with the more complicated function. I get values for nls2, but when I use nlsLM I get the error: "evaluation of fn function returns non-sensible value". , which I cannot

问题 I have a data set and I want to adjust to the following function and find the parameters a and b: I tried the nonlinear least squares approach, however, I'd like to try by trial and error, using a vector with values for a, and another for b, then plot all the alternatives mixing this values to choose a better fit. library(readxl) library(ggplot2) x <- c(52.67, 46.80, 41.74, 40.45) y <- c(1.73, 1.84, 1.79, 1.45) df <- data.frame(x,y) ggplot(data = df, aes(x, y))+ geom_point()+ stat_smooth

问题 I'm trying to fit hundreds of gompertz-shaped curves using SSgompertz. The dataset has three columns with "x" and "y" values and a coded column to separate the data into different samples: "GROUPING". Later, the parameters will be used to determine x from a fixed point on the y-axis for all samples (fit point methods) using predict(). I managed to fit multiple polynomials to the data before feeding the parameters into predict() using this code: Parameters<-lmList(x~poly(y,3,raw=TRUE)|GROUPING