In statistics, nonlinear regression is a form of regression analysis in which observations are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables.
Questions tagged [non-linear-regression]
597 questions
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Multinomial logit in R: mlogit versus nnet
I want to run a multinomial logit in R and have used two libraries, nnet and mlogit, which produce different results and report different types of statistics. My questions are:
What is the source of discrepency between the coefficients and standard…
splinter
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Calculation of R^2 value for a non-linear regression
I would first like to say, that I understand that calculating an R^2 value for a non-linear regression isn't exactly correct or a valid thing to do.
However, I'm in a transition period of performing most of our work in SigmaPlot over to R and for…
sinclairjesse
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non linear regression with random effect and lsoda
I am facing a problem I do not manage to solve. I would like to use nlme or nlmODE to perform a non linear regression with random effect using as a model the solution of a second order differential equation with fixed coefficients (a damped…
denis
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Keras model to fit polynomial
I generated some data from a 4th degree polynomial and wanted to create a regression model in Keras to fit this polynomial. The problem is that predictions after fitting seem to be basically linear. Since this is my first time working with neural…
FloodLuszt
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Distinction between linear and non linear regression?
In Machine Learning, we say that:
w1x1 + w2x2 +...+ wnxn is a linear regression model where w1,w2....wn are the weights and x1,x2...x2 are the features whereas:
w1x12 + w2x22 +...+ wnxn2 is a non linear (polynomial) regression model
However, in…
Ram
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Is deep learning bad at fitting simple non linear functions outside training scope (extrapolating)?
I am trying to create a simple deep-learning based model to predict y=x**2
But looks like deep learning is not able to learn the general function outside the scope of its training set.
Intuitively I can think that neural network might not be able to…
Krishan Subudhi
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Find initial conditions for nonlinear models using the nlsLM function
I am using the nlsLM function from the minpack.lm package to find the values of parameters a, e, and c that give the best fit to the data out.
Here is my code:
n <- seq(0, 70000, by = 1)
TR <- 0.946
b <- 2000
k <- 50000
nr <- 25
na <- 4000
nd <-…
Pierre
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How to perform piece wise/spline regression for longitudinal temperature series in R (New Update)?
Here I have temperature time series panel data and I intend to run piecewise regression or cubic spline regression for it. So first I quickly looked into piecewise regression concepts and its basic implementation in R in SO, got an initial idea how…
Andy.Jian
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Understanding the Jacobian output of scipy.optimize.minimize
I'm working with scipy.optimize.minimize to find the minimum of the RSS for a custom nonlinear function. I'll provide a simple linear example to illustrate what I am doing:
import numpy as np
from scipy import optimize
def response(X, b0, b1, b2):
…
khiner
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How can you remove only the interaction terms in a polynomial regression using scikit-learn?
I am running a polynomial regression using scikit-learn. I have a large number of variables (23 to be precise) which I am trying to regress using polynomial regression with degree 2.
interaction_only = True, keeps only the interaction terms such as…
Harshavardhan Ramanna
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Finding non-linear correlations in R
I have about 90 variables stored in data[2-90]. I suspect about 4 of them will have a parabola-like correlation with data[1]. I want to identify which ones have the correlation. Is there an easy and quick way to do this?
I have tried building a…
dorien
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Nonlinear multiple regression in R
I'm trying to run a nonlinear multiple regression in R with a dataset, it has thousands of rows so I'll just put the first few here:
Header.1 Header.2 Header.3 Header.4 Header.5 Header.6 Header.7
1 -60 -45 615 720 …
japem
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finding a point on a sigmoidal curve in r
Here is a data set:
df <- data.frame('y' = c(81,67,54,49,41,25), 'x' =c(-50,-30,-10,10,30,50))
So far, I know how to fit a sigmoidal curve and display it on screen:
plot(df$y ~ df$x)
fit <- nls(y ~ SSlogis(x, Asym, xmid, scal), data =…
B C
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Statistical tests: how do (perception; actual results; and next) interact?
What is the interaction between perception, outcome, and outlook?
I've brought them into categorical variables to [potentially] simplify things.
import pandas as pd
import numpy as np
high, size = 100, 20
df = pd.DataFrame({'perception':…
A T
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Tensorflow. Nonlinear regression
I have these feature and label, that are not linear enough to be satisfied with linear solution. I trained SVR(kernel='rbf') model from sklearn, but now its time to do it with tensorflow, and its hard to say what one should write to achieve same or…
Grail Finder
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