nls

问题 R package ggpmisc can be used to show equation of lm model and poly model on ggplot2 (See here for reference). Wonder how to show nls model equation results on ggplot2 using ggmisc . Below is my MWE. library(ggpmisc) args <- list(formula = y ~ k * e ^ x, start = list(k = 1, e = 2)) ggplot(mtcars, aes(wt, mpg)) + geom_point() + stat_fit_augment(method = "nls", method.args = args) 回答1: Inspired by the post you linked. Use geom_text to add the label after extracting parameters. nlsFit <- nls

问题 The following nls code throws the following error Convergence failure: singular convergence (7) for fm2 (for Data2 ). But the same code for similar dataset works fine ( fm1 for Data1 ). Any help to figure out this problem will be highly appreciate. Thanks Works Fine for this Data Set Data1 <- structure(list(D = c(0L, 0L, 0L, 0L, 5L, 5L, 5L, 5L, 10L, 10L, 10L, 10L, 15L, 15L, 15L, 15L, 20L, 20L, 20L, 20L), Y = c(11.6, 9.3, 10.7, 9.2, 7.8, 8, 8.6, 7.9, 7.7, 7.6, 7.5, 7.5, 7.2, 7.3, 7, 6.5, 6.3,

问题 I want to fit Isotherm models for the following data in R. The simplest isotherm model is Langmuir model given here model is given in the bottom of the page. My MWE is given below which throw the error. I wonder if there is any R package for Isotherm models. X <- c(10, 30, 50, 70, 100, 125) Y <- c(155, 250, 270, 330, 320, 323) Data <- data.frame(X, Y) LangIMfm2 <- nls(formula = Y ~ Q*b*X/(1+b*X), data = Data, start = list(Q = 1, b = 0.5), algorith = "port") Error in nls(formula = Y ~ Q * b *

问题 I am learning how to use the nls function in R and am having some issues. I am simply attempting to recreate the curve found in a research paper for now. The model fits a curve to the the stock market movements prior to the 1987 crash. I have defined a function, func, as follows: func <- function(a,b,tc,t){ a+b*log(tc-t) } I have called nls like this: nls1 <- nls(Y ~ func(a,b,tc,t), data2, start=list(a=0, b=1, tc=1466, t=1)) data2 is a data frame that consists of two columns, one is the date,

问题 I am learning how to use the nls function in R and am having some issues. I am simply attempting to recreate the curve found in a research paper for now. The model fits a curve to the the stock market movements prior to the 1987 crash. I have defined a function, func, as follows: func <- function(a,b,tc,t){ a+b*log(tc-t) } I have called nls like this: nls1 <- nls(Y ~ func(a,b,tc,t), data2, start=list(a=0, b=1, tc=1466, t=1)) data2 is a data frame that consists of two columns, one is the date,

问题 With the linear model function lm() polynomial formulas can contain a shortcut notation like this: m <- lm(y ~ poly(x,3)) this is a shortcut that keeps the user from having to create x^2 and x^3 variables or typing them in the formula like I(x^2) + I(x^3) . Is there comparable notation for the nonlinear function nls() ? 回答1: poly(x, 3) is rather more than just a shortcut for x + I(x ^ 2) + I(x ^ 3) - it actually produces legendre polynomials which have the nice property of being uncorrelated:

问题 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

问题 There's a nice explanation here of how to use ggplot2 to create a scatterplot, fit the data using nls, and plot the fit, all in one line, like so myhist = data.frame(size = 10:27, counts = c(1L, 3L, 5L, 6L, 9L, 14L, 13L, 23L, 31L, 40L, 42L, 22L, 14L, 7L, 4L, 2L, 2L, 1L) ) ggplot(data=myhist, aes(x=size, y=counts)) + geom_point() + geom_smooth(method="nls", formula = y ~ N * dnorm(x, m, s), se=F, start=list(m=20, s=5, N=300)) My question is: using this construction, is it possible to pull out

问题 I would like to fit multiple curves at once, and compare them statistically, in terms of their 3 estimated parameters – asymptote, slope and x0. Here is an idealized image of the data to be modeled: Most searchable pages turn up methods to fit a single curve, as seen here: http://kyrcha.info/2012/07/08/tutorials-fitting-a-sigmoid-function-in-r/ and here http://rstudio-pubs-static.s3.amazonaws.com/7812_5327615eb0044cf29420b955ddaa6173.html In my case, I would like to test (statistically) the

问题 This question already has answers here : In r, get output values in power curve for 'a' and 'b' values (3 answers) Closed 5 years ago . i am trying to make a fit for a power function using the NLS function in R but i am failing to find good start values. This is part of my data "CentroM": Wg TLcm 3200 79 2650 77 2750 74 870 45 1480 52 3400 80.5 2400 76 2800 76.5 2900 77.5 2700 76 3215 76 3300 83 3100 79 3000 78.5 2800 76 2700 77 2500 74.5 2300 69 2700 73.5 3350 79 and here is the script i