Stack Exchange Network

Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

Questions tagged [nonlinear-regression]

Use this tag only for regression models in which the response is a nonlinear function of the parameters. Do not use this tag for nonlinear data transformation.

1
vote
1answer
17 views

How to model counts of a categorical variable?

I'm interested in assessing the impact of various covariates (age, sex, Charlson comorbidity score, etc.) on the incidence rate of a pulmonary event. However, the event is not binary. Each patient ...
0
votes
0answers
14 views

Hypothesis testing for nlsList fitted coefficients

I am trying to test in R if the coefficient R10 (soil respiration at 10 °C) is significantly different between two plant types (Type1 and Type2). For this, I fitted a modified Lloyd-Taylor function ...
3
votes
1answer
38 views

How to fit a set of curves having some free and some shared parameters?

The problem I have a data set with 1 dependent and $N$ independent variables $x_1,\dots ,x_N$ (all real numbers), and need to fit a (nonlinear) function/curve $$ f_i(x_i; p_1,\dots ,p_{k_F}, r_1, \...
1
vote
0answers
13 views

como eu posso ajustar parameteros um sistema com duas equações com a função nls()? [closed]

I Ned to fit this system of equations: I'm using nls.lm(), but want to use the nls() function # rm(list= ls()) df=read.table( text =" 0 0.010000000000000 0 0....
3
votes
1answer
38 views

Hard in calculating predictor‘s Relative Importance for GAM?

Although there is no agreement upon "relative importance for predictors" with (even) linear models (one possible definition: lmg method), I would still want to know whether there are some acceptable ...
1
vote
0answers
22 views

Regression problem — which methods are appropriate?

Given: A non-differentiable, non-smooth function $f(p,q) \rightarrow R$ A set of points $S=\{(x_0,y_0), \ldots, (x_n,y_n)\}$ Bounds as $p_{min} < p < p_{max}$, and $q_{min} < q < q_{max}$...
0
votes
0answers
35 views

Multi State Models to analyze/plot disease progression and probability of being misdiagnosed

Let's say that I have the following dataset containing information for 100 patients that have been followed up for a certain number of years to check if they develop a certain disease. We know up-...
0
votes
0answers
9 views

Correct Specification for Censored Data

I have some construction data, which shows the starting year of unfinished and finished projects but don’t have any information on the completion time of finished projects. If I had data on the ...
1
vote
1answer
26 views

Residual Analysis assumptions for non-linear regression

I understand Regression analysis relies on the following assumptions about the residuals: Normally Distributed (normal plot of residuals) Be independent of each other (random and data must be time ...
2
votes
0answers
53 views

Is there a measure of “complexity” for linear/nonlinear model terms?

My apologies if this is grossly misunderstood or mis-worded, but I've been mildly bugged by a question to which I've not found a satisfactory answer. I can't say that I have seen a discussion about ...
1
vote
1answer
21 views

Model to capture non-linear patterns in data using R

I've fitted a linear model using: m2 <- lm(GPP ~ rainfall + summer.temp + parcel.size + soil.nutrients, data=gpp) As seen from the partial relationship plots ...
0
votes
0answers
18 views

Justification for LST model

Let’s say I have a linear model specification but I believe that a logistic smooth transition model would be better. Is there any evidence I can take from the linear model that would point me in ...
0
votes
0answers
39 views

Initial conditions for nonlinear models using the nlsLM function

I have a data frame containing 70000 rows. For each row, I am trying to apply the nlsLM function (minpack.lm package) to find ...
0
votes
1answer
17 views

Estimating a Conditional MNL in mlogit

The Problem I am trying to estimate a conditional multinomial logit model where each household has an observed house choice outcome given a set of alternatives. Each set of alternatives is unique to ...
0
votes
0answers
21 views

Goodness of fit test for any regression model?

Is there a general goodness-of-fit test for any kind of regression model? My problem is that I have a deep neural network that tries to predict some real value labels using high-dimensional input. The ...
1
vote
0answers
9 views

deciding on the form of the formula in multivariate nonlinear regression

dear members, lets say I have one response variable and two predictors (non linear): Y1, the response variable, and two predictors, X1 and X2. Using nonlinear least squares estimation, ...
0
votes
0answers
12 views

How to perform joint estimation of parameters from two NLS-regressions in R?

First of all, I am new to the board so excuse me if I am not writing this post in the most optimal way. That aside, I am trying to run some models on optimal entry timing for successive product/...
1
vote
2answers
49 views

The non-linear extra sum of squares? What is this?

I encounterd in the article I read the term the non-linear extra sum of sqare with the reference to Bates and Watts 1988. I do ...
0
votes
0answers
17 views

Fully connected layer vs Multiple parallel dense layers for multivariate nonlinear regression?

I'm trying to tackle a multivariate nonlinear regression problem that takes around 20 inputs and outputs around 200. I have a set of known points and need to come up with a performant neural network ...
0
votes
0answers
19 views

What does bandwidth in kernel regression mean?

here https://stat.ethz.ch/R-manual/R-devel/library/stats/html/ksmooth.html is bandwidth explained as "the bandwidth. The kernels are scaled so that their quartiles (viewed as probability densities) ...
3
votes
0answers
38 views

Extracting the linear equation for a circular-circular regression

I am trying to create a predictive model using a relationship found by the lm.circular function from the circular package. The ...
0
votes
0answers
34 views

How to do a sensitivity analysis on a non-linear equation?

In the company, it is very difficult to actually do quotations for our customers properly because we do not have perfect information regarding the factors that affect the cost and profit. So I created ...
0
votes
0answers
17 views

Minimizing numerically a nondifferentiable function

Many (likelihood) functions are not differentiable at the optimal point. Does this cause problems a) in the numerical methods used to minimize the function based on the gradient? b) in the statistical ...
0
votes
0answers
25 views

Estimating the trend over time

I have series of data which is quarterly. I want to analyze is there any significance increase over time. With my little knowledge about ARIMA and time series I used auto.arima function ...
1
vote
1answer
33 views

Linear regression using running parameters

I always asked myself what was the right method name for a simple linear regression using running parameters. I mean that instead of using constant mean $\bar{y}$ or $\bar{x}$ for the estimation of $ ...
0
votes
0answers
4 views

About Multicolinearity in Support Vector machine based regression model

Is there any need to check multicollinearity for Support Vector machine based Regression model(for prediction)? If yes, then how it can be handle?
0
votes
1answer
16 views

Prediction in logistic regression with prediction criteria ranges

I’m not sure how to best explain my problem but I’ll try. I can’t be too specific because this is a homework assignment; I just would like some guidance from the experts on how to approach it. So I’...
0
votes
0answers
33 views

How to run a nonlinear repeated measure multilevel regression?

I'm working with a colleague on a project that requires analysis a fair bit beyond my expertise. Background We are looking at recall of events in films. We broke down the film by Events and by shots ...
1
vote
1answer
16 views

Regression model using combination of ranges/parts

My main goal is making predictions using a nonlinear model that have many independent variables. I would like to split my numerical independent variables into ranges/parts. Then to use a combination ...
1
vote
1answer
27 views

I need help with choosing a mid-long term forecastic method for this demand

I am trying to forecast the demand of a product for the next 36 months, based on its sales history. The demand plot is shown below. I honestly don't know what to do with it. I tried linear and non-...
1
vote
0answers
26 views

How can I determine, in what extent the fit to experimental data is good in Matlab?

I have experimental spectrum in which y-axis is intensity values, and x-axis is frequency values. Int - array of experimental intensities (y-axis). w - array of frequencies (x-axis). I know the view ...
1
vote
1answer
39 views

Is it possible to calculate F-value for a neural network regression model?

I trained a model using neural network regression and used the F-value equation that is used for calculating F-value for linear regression: F=(SUM(Ypredicted-Ymean)^2/p)/(SUM(Ypredicted-Yobserved)^2/...
0
votes
0answers
23 views

Do we need ergodic-stationarity of the response variable in OLS spline regression?

I was wondering if we need the response variable to be ergodic stationarity when estimating an OLS spline regression. My intuition tells me that it's not needed but I would like to have a confirmation ...
0
votes
0answers
26 views

confidence limits on model parameters : Monte carlo vs chi-square

I performed a non-linear fitting on experimental datas to determine model parameters using least square methods ($\sigma$ is the same for all the experimental datas). And now, I want to determine ...
0
votes
1answer
39 views

Selecting appropriate likelihood during non-linear regression

When performing regression to fit a function, $f (x,{\bf \beta})$, to a set of observed data, $y_i(x_i)$, we are seeking to optimize the parameters, $\beta$, of the fitting function, to minimize some ...
3
votes
0answers
32 views

Should I standardize my variable for regression before nonlinear feature transformation?

I would like to fit a non-linear model by doing nonlinear feature transformation first (e.g. exp, log) and then using linear regression (or regularized linear regression). However, I am stuck at ...
0
votes
1answer
55 views

Linear regression feature selection equivalent for a classification problem?

I have the following: Label (y): a boolean flag indicating something is good or bad Features (X): lower-level features that are believed to be correlated with the boolean flag. Some of them are ...
0
votes
2answers
43 views

Would machine learning techniques help if the linear and nonlinear relationships is so weak?

I have a cross sectional data set at hand contains four predictors to predict one outcome, I employed bivariate analyses to check whether the relationship between the dependent and independent ...
1
vote
0answers
22 views

Is there a correlation coefficent for “smooth” functions?

There is Pearson's, which measures linear relationships. There is also Spearman's, which can detect monotonic relationships. I am wondering if there is a similar coefficient someone has come up with ...
0
votes
2answers
38 views

How to analyze a small dataset?

I have this dataset and I'm not sure how to analyze it. I threw the classical regression methods such as OLS at it and haven't achieved much success. My response variables are ...
0
votes
1answer
40 views

nonlinear regression with time series error

I have a question about data analysis. I fitted my data to non linear regression by using nls function in R. Then I plot the residuals. The residuals are non ...
0
votes
0answers
88 views

GAM residuals , GAM check

I am doing my GAM regression analysis in R and by using the gam.check() function from the ...
1
vote
1answer
59 views

Linear Model (on X or in $\beta$?)

I'm well aware that when we use the expression "linear model" we are actually making reference to models that are linear on the parameters $\beta$. And because of that any polynomial regression will ...
1
vote
2answers
69 views

Neural networks for regression vs. more classical regression methods?

I am interested in learning about when one would use neural networks for a regression problem over a more classical regression method such as least squares. Is it mostly related to the complexity of ...
0
votes
0answers
25 views

Are there any good, general optimization algorithms for nonlinear regression in R?

I have generated data similar to a model I want to build: ...
12
votes
1answer
284 views

How can I test if the two parameter estimates in the same model are significantly different?

I have the model $$ y=x^a \times z^b + e $$ where $y$ is the dependent variable, $x$ and $z$ are explanatory variables, $a$ and $b$ are the parameters and $e$ is an error term. I have parameter ...
1
vote
0answers
16 views

How to estimate error of regression parameters from data with errors

I'm a physics student and in school we often measure some data (like voltage and current) and then use regression to determine an unknown quantity (ie. resistance in this case). My problem is that I ...
0
votes
0answers
23 views

Set different link functions in Generalized Mixed Effects Model in R

Suppose I have a dataset of fish, some are salmon some are trout. I have a bivariate regression model specified roughly like below: prob(caught) = 0.5 + 0.5 * logit_inv(diet + fish_type) for salmon ...
0
votes
0answers
111 views

Using UMAP or other non-linear dimension reduction techniques on response variables prior to learning?

Background Suppose you have a training set where the response measurements are some $N$-dimensional vectors of related measurements - in my specific case, they happen to be cell viability scores for ...
3
votes
1answer
73 views

What is the purpose of doing a logistic regression when the predictor is dichotomous?

I would like to expand on this question. Knowing that it is possible to do a logistic regression when the IV is dichotomous, and that I've seen it done in studies: what is the purpose of doing so, and ...