Use this tag only for regression models (q.v.) in which the response is a nonlinear functions of the *parameters* (not because it's a nonlinear function of the *predictors*).

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Non linear regression using R [on hold]

I am working on a prediction problem for continuous data. I have some data which I want to fit in the equations. It's non-linear in nature. Can anyone suggest me good non-linear regression algorithms ...
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2answers
54 views

Regression analysis low R2 value - Result interpretation

When I run linear regression on my test data I get the following report: You can find the test data in here. The graph of actual vs predicted looks like: I would like to know if this is fairly a ...
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3answers
30 views

Hypothesis Tests for Non-linear least squares coefficients

I have a set of results that take the following form: ...
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0answers
13 views

Indicator regressions: standard errors

Hi I am estimating a regression of Y on X where both Y and X are indicator variables, that is, they take values 1 or 0. The model is $Y=\beta*X+\epsilon$ (without constant). I use OLS to estimate the ...
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0answers
47 views

Non-linear regressions with caret package in R [closed]

i'm new using R and my doubt is really basic. I have several dependent variables (x) and one independent variable (y), and I'd like to generate different regression models with ...
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0answers
10 views

Contributions of variables in Log-Log models [duplicate]

I have built a model on log(units) sold and want to measure the contribution of each independent variable in the model (which are also in ...
1
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1answer
33 views

How to fit intensity peaks from a image?

I have an image that I can convert to a text file / table of intensities. In this image, several regions show higher intensity, i.e., a peak. A peak may be stored in a 10x10 matrix with rows and ...
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1answer
37 views

Extremly poor polynomial fitting with SVR in sklearn

I try to fit an obvious around degree 5 polynomial function. Much to my despair, sklearn bluntly refuses to match the polynomial, and instead output a 0-degree like ...
3
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1answer
40 views

Predictive distributions in Poisson regression

I have the following question about Poisson regression. In "regular" (OLS) linear regression it is fairly easy to prove that the correct predictive distribution will be a $t$ distribution, but what if ...
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0answers
5 views

Developing cross validated regression model (nlinfit) in matab [migrated]

I am using the following code to fit a cross-validated non-linear regression model. ...
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0answers
21 views

R-Squared in a non-linear model [duplicate]

I am running a dynamic demand model (a non-linear model) in SAS. My model includes three equations which should be solved simultaneously. I am applying a Iterated Seemingly Unrelated Equation (ITSUR) ...
1
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1answer
31 views

Multiple regression approach strategies for non-normal dependent variable

I'm hoping to analyze the influence of a set of variables on a continous dependent variable (between 0-1). The independent variables are a mix of both categorical, continuous and discrete features. ...
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0answers
28 views

Trying to fit single layer neural net with R's nls (nonlinear least squares) function

Working on building a neural network modeling frame using graph objects in R. I have a data set on passengers of the Titanic, modeling binary "survived" variable against continuous "fare" and "age" ...
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0answers
30 views

How do you do constrained non-linear least squares in R [migrated]

I am fitting a non-linear least squares model in R. I wish to minimize $(Y - f(Xb))^2$ where $f$ is a nonlinear monotone differentiable function, $X$ is a set of features and $b$ is the parameter ...
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2answers
36 views

How can I make this biological relation into a glm model?

I have a biological relation: Y/m = (X * b) / (1 + X * b) where Y and X are variables, m and b are parameters. m is greater than Y, and everything is greater than 0. I have some training data with ...
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0answers
9 views

Finding redundant parts of a data set for training a nonlinear model

I like to train a nonlinear model based on a data set $D_1$,..$D_n$, where $D_i$ is collected by doing experiments $E_i$. It is possible that $D_i$ is redundant given $D_j$ and $D_k$. Since doing an ...
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0answers
17 views

Interpreting p-values of log regressions

The following output is for a log log model. ...
0
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0answers
20 views

interpret normal probability plot of residuals [duplicate]

I am looking at two normal probability plots of some residuals from a two different regressions. I am trying to make sure I fully understand what they are telling me. The first chart below appears ...
0
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0answers
34 views

What is the difference between Average Partial Effects (APE) and Average Marginal Effects (AME)?

In this answer, the terms Average Partial Effects (APE) and Average Marginal Effects (AME) are used interchangeably. But in this paper, the terms are used to mean different things (page 75). But it's ...
6
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2answers
198 views

How to tell the difference between linear and non-linear regression models?

I was reading the following link on non linear regression SAS Non Linear. My understanding from reading the first section "Nonlinear Regression vs. Linear Regression" was that the equation below is ...
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1answer
47 views

How should one assess model fit for non-linear regression?

I am looking at non linear regression. Below is some example output from a non linear regression using MATLAB. There are also two links below this output from the Minitab website. The links explain ...
4
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0answers
39 views

F-test to determine whether more than two sets of data differ

Here is the context for my question: I understand that you can fit the same model to two different datasets separately and then fit the model to the datasets pooled together as a way to discern ...
2
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0answers
99 views

How to plot the confidence interval from boot.ci output of nonlinear regression in R?

I have a data.frame with two columns (x and y) for which I have obtained a nonlinear least squares fit: -a/(b+exp(-x)). Now I'm trying to plot the 95% confidence interval for y. My best attempt so ...
2
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0answers
33 views

Which type of Regression model do I need and what do I need to do to my variable to allow it to work?

thanks for taking the time to read this! A little background, I'm pretty enw to doing most statistical analysis; I've only ever done linear regressions and I tried doing research online but was ...
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0answers
14 views

MATLAB: bounding the parameter values in nonlinear modelfitting and AICc scores

I am trying to fit a number of nonlinear models to a dataset, and I need to bound the model parameter values to all be positive. I tried lsqcurvefit function and it works. However, I also need AICc ...
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1answer
44 views

Consequences of violating assumptions of nonlinear regression when comparing models and/or datasets

I have a question about the consequences of using non-linear regression when the data violate the assumptions of (1) homoscedasticity and (2) normal distribution. Specifically, I am wondering about ...
2
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0answers
28 views

Neural Network Learning Curves with Low Test Set Error

TLDR: You can see my neural network learning curves here: http://imgur.com/0CL6LVY. Which regularization term would you pick given that the test error actually drops below the training error at some ...
3
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0answers
56 views

How to fit an exponential equation of the form $Y = A + Be^{CX}$ to data

I need some assistance with a nonlinear adjust. I am trying to make a mathematical model that describes the rate of silicic acid escaping from an underwater sediment. For theoretical reasons, the ...
2
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2answers
95 views

Why do I get linear model when I tried to fit exponential model?

I was wondering why do I get linear model when I'm using exponential model, y = a * exp(-b*-x), to fit my data. Here is my code: ...
4
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0answers
42 views

Confidence interval of the mean response from nonlinear model

My problem (question at the end) is to calculate confidence interval (CI) (NOT prediction interval) of the response of a nonlinear model. I am working with R but this question is not R-specific. I ...
2
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0answers
32 views

piecewise linear regression with unknown number of knots

I have a model that depends linearly on $v$ and $\alpha$, but not linearly on other two parameters $T_0$ and $T_1$: $f(i; v, \alpha, T_0, T_1)$. Using least squares, I can solve for $v$ and $\alpha$, ...
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1answer
37 views

can I apply loess or spline regression in mixed model?

My situation right now is that I have the mixed model with quadratic term but it doesn't perform very well. So I am wondering if I can apply loess or spline regression to the mixed model instead of ...
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0answers
75 views

How to fit a two phase exponential decay curve?

I have a model where the variable y theoretically should have an exponential decay over time x. The real data showed a fast decay to begin with and a slower decay towards the end. Here is the code: ...
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0answers
13 views

MARS Modelling for large no of variables

I have data with more than 200 predictors, can MARS be used for such a data? Objective is to identify predictors which are most important across each category. So these 200 predictors are from 6-7 ...
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0answers
211 views

Calculate PLS Xscores for predicting new data

I wish to extract Partial Least Squares (PLS) components to apply non-linear regression (Gaussian Process Regression (GPR)) on the scores of the predictors (Xscores). The reason is my data is very ...
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0answers
23 views

Multivariate quantile regression

I have a multivariate linear model: $\mathbf{Y} = \mathbf{X}\mathbf{B} + \mathbf{U}$ where the matrix $\mathbf{Y}$ represents stock returns, the design matrix is constituted by some explanatory ...
0
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1answer
23 views

MLP with 2 outputs vs 2 MLPs with single outputs for nonlinear regression

Assume i want to apply nonlinear regression to two output variables with multilayer perceptrons. Is there difference between using a MLP for each regression with single output and using a single MLP ...
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0answers
51 views

Can I use deviance to compare the fit of a model to different datasets?

I'm using R's nls to fit different datasets to the same model. I've read that using R-squared is usually not correct for ...
2
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0answers
32 views

Reverse-engineering a (custom goods) pricing algorithm - each db row has: | 3 factors | 2 co-variates | price | (I have over 100k rows of data) [closed]

just wanted to mention up front that my question doesn't concern dynamic pricing, price optimization, revenue management, etc. No time series analysis either. It's just a simple multivariable ...
1
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1answer
149 views

Deep neural nets, RELU's removing non-linearity?

are RELU (Rectified Linear Units) activation functions considered non-linear? They are linear when the input is > 0 and from my understanding to unlock the representative power of deep networks ...
2
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0answers
19 views

How to measure the degree of nonlinearity in regression model [duplicate]

Could anyone guide me on how can we measure the curvature of nonlinear models? I have to know the methodology to measure nonlinearity (both intrinsic and parametric) for nonlinear regression model. ...
2
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1answer
56 views

Stability of univariate fractional polynomial models

I can't decide what is the best way to assess the stability of a higher order fractional polynomial model. To use an example I have been working on, I am analyzing a dataset with panel data selected ...
3
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0answers
16 views

Multiple comparisons of parameters from non-linear regression

Hei, I want to compare parameters a_i and b_i estimated by nonlinear regression (y=a_i*x/(b_i+x)) for different data-sets (let's say 8 different data sets). I have calculated the non-linear ...
0
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0answers
30 views

How to pool mice results from piecewise regression

I'm trying to find breakpoints using the segmented package. As there are some missing values I would like to use mice for imputing these. Unfortunately I'm clueless ...
5
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1answer
41 views

Pitfalls in Fitting Nonlinear Models by Transforming to Linearity

Some nonlinear models can be transform to linear models. My understanding is that there might be one-to-one relationship between the estimates of nonlinear model and its linear model form but their ...
0
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0answers
67 views

lm and nls F test in R

I am trying to compare a linear model and other non linear models(Asymptotic, Logistic and Ricker) by means of an F test or a likelihood ratio test. I have tried anova(Linear, Logistic,Ricker, ...
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0answers
30 views

Confidence intervals for a curve with bootstrapping

I am estimating y= az + f(x) in a semi-parametric way. I want to compute the standard error for the estimated coefficient for a ...
0
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1answer
34 views

Non-Linear regression that captures jumps and an exponential decay

I have some data that has the pattern in the picture below (but little noisier than that). I want to run a non-linear regression that tries to capture the dynamic of this data in the time-series ...
0
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0answers
18 views

use of the hessian when minimizing sums of squares in non-linear regression model

Hi: I am using numerical optimization to minimize the sum of squares a non-linear regression model. I've done lots of checks with various algorithms and I use analytical derivatives so I'm confident ...
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0answers
9 views

Interpolating singular values

So I have the singular values associated with a data matrix and I would like to interpolate them and then find the maximum curvature of the interpolation in order to decide how many singular values to ...