non-linear-regression

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'm new to Gaussian processes and struggling to validate the output of my scikit GPR. I'm particularly concerned with the fact that my GPR returns a score of 1, which doesn't make any sense to me because the coefficient of determination of this data should not be equal to 1. Is there a particular problem with the GRP or data that is implied by a score of 1? I've included my code, and my X,Y are each arrays of length 15. I have additionally tried both the Matern and RBF kernels on their own

问题 I'm trying to perform a non linear fit to some simple data (corn yield by year). It's straight forward enough to do it with lm in R, but some of the data would fit better if there was a curve allowed, something on the order of year^1.5 or so. x <- c(1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L,

问题 I'm trying to perform a non linear fit to some simple data (corn yield by year). It's straight forward enough to do it with lm in R, but some of the data would fit better if there was a curve allowed, something on the order of year^1.5 or so. x <- c(1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L,

问题 I'm trying to perform a non linear fit to some simple data (corn yield by year). It's straight forward enough to do it with lm in R, but some of the data would fit better if there was a curve allowed, something on the order of year^1.5 or so. x <- c(1979L, 1980L, 1981L, 1982L, 1983L, 1984L, 1985L, 1986L, 1987L, 1988L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 2012L, 2013L,

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

来源： https://stackoverflow.com/questions/64316765/how-to-calculate-r-squared-in-nls-package-non-linear-model-in-r

问题 I am following trask's article to build a bare bone neural network in Python. Though he builds 1 layer Network (that maps 3 inputs to a single output) and a 2 layer network (that has 3 inputs, 4 neuron hidden layer and output layer with single neuron). My task was to build a network that can approximate the function Y = X1 + X2 + X3 . I provide the network with Y and it guesses values of x1, x2 and x3. For this, I modified the above network. I tried to invert both above networks, i.e, tried