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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28 views

How does MATLAB compute the correlation coefficient for robust nonlinear least squares?

As part of a much larger project, I was running some fits and compared the results with what a colleague had and I noticed that our correlation coefficients weren't matching. So I have some data, and ...
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21 views

nls standard deviation calculation

I have fitted a non linear assymptotic equation to a set of data and my interest is in getting the standard deviations of the fitted parameters. Is this possible in nls?
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20 views

Error in gnls step halving factor

I am getting an error running a gnls() on some data I have. I was able to converge using nlsLM(), but I ran into some autocorrelation in my errors, so I want to try to use gnls() so that I will be ...
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1answer
65 views

ARIMAX with a specified nonlinear model using the arima function in R

I am interested in fitting an ARIMAX model using R. As known, ARIMAX can be understood as a composition of ARIMA models and regression models with exogenous (independent) variables. I have a time ...
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1answer
27 views

Does the randome forest for regression solution are interpretable and sparse?

I have regression problem scenario. basically I want to model a certian biological problem as regression models and the end my model should be interpretable. I need to have sparse model. so I'm ...
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1answer
17 views

Cross-validation for nonlinear models that are linear in the parameters

I'd like to know if it's correct to the CV function in the forecast R package (http://cran.r-project.org/web/packages/forecast/forecast.pdf) to cross-validate a nonlinear model that is linear in the ...
3
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50 views

Trouble fitting mathematical model to data in R

I am trying to select the correct mathematical model for my standard curve. This data was collected from spectrophotometry. I am hoping to get a model to help me detect very small absorbance. ...
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0answers
15 views

NARX model to predict future values

I have this problem , where I have to predict a value of a indicator which depends on 270 other predictor variables. I read the time series modelling and prediction on MATLAB , which took the example ...
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25 views

creating a equation for non linear multiple regression to predict a value based on the inputs given in excel

I have data with 5 columns namely Botany,Zoology,Chemistry,Physics and Rank in a excel sheet . The data here is non linear . So I want to generate a equation in the form of y=a+bx1+cx2+dx3 In ...
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17 views

Pareto two-tailed GLM regression

How can I perform a Pareto two-tailed GLM regression? Any reference to link functions and code in R?
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1answer
58 views

What is the difference between GLM and splines?

Suppose we want to predict $Y$ given the following $X$ observations: ...
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2answers
74 views

Estimating “Probability” in normal probability plot

I plotted normal probability plot in R using qqnorm and qqline. I want some help on: How to estimate "probability" that a data ...
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0answers
35 views

Selection of failed fitting results in MC Simuation

I recorded a set of experimental rates $r = r(c,T,P)$ at 2 values of $c$ and >15 values of $T$. $r(c,T,P)$ obeys the following functional form: $$ r(c,T,P) = ...
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22 views

'systemfit' package and systems of non-linear equations/regression

I am trying to estimate a system of non-linear equations using 'systemfit' package in R. I have had issues with it. The two equations share the same parameters i.e. "sigma", "al" and "ae". I expect ...
3
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2answers
75 views

Linear post-treatment of nonlinear regression

I have often found in practice, using nonlinear regression techniques such as feedforward neural nets or random forests, that the resulting actual-vs-fitted plot (on training set) seems obviously ...
0
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0answers
8 views

Function domain as a problem for linear output unit

I'm doing some regression using neural net(using MLP implementation from http://deeplearning.net/tutorial/mlp.html, I used my own but it produced the same results before I opted for this one), ...
3
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2answers
57 views

Non-linear least absolute deviation regression with multiple global minima

I am fitting a single exponential decay formula with three parameters (a,b,c): y ~ $a \exp(-xb) + c$ using the LAD cost function: $ \min \sum |(y - f(x))| $. $x$ is in units of time (as is $b$), and ...
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0answers
12 views

hierarchical logistic regression Block non-significant, interaction significant

I'm using hierarchical logistic regression and having some difficulty. Im trying to predict self-harm vs none based on IVs age, gender, alcohol, educational attainment. They are all yes/no except ...
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3answers
399 views

What does “curvilinear” mean?

As far as I can tell, curvilinear is defined vaguely but means the same as nonlinear. Is that correct? Or does curvilinear have a distinct definition?
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27 views

Beyond multiple linear regression for longitudinal data?

I have a relatively large dataset from a longitudinal study (~100k subjects and ~10 random effects) where the outcome is a real-valued parameter. A simple run of R's ...
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1answer
47 views

How to compare different nlme models ?

If there are 2 nlme models with same non-linear mean function, model 1 and model 2, how do you compare them ? Which R function does this for us ? And when there are random effects or fixed effects, I ...
2
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1answer
19 views

Varying Non-Linear Parameters Based on Groups in R

I'm trying to develop a non-linear model, but I'd like to have the values for the parameters vary by group. To give you an example, a section of my data (just random numbers here) looks like: ...
4
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1answer
101 views

Model with non-linear transformation

I don't understand this concept well and need help. I was choosing whether to use a linear model or apply a non-linear transformation in my model formula. To do a diagnostic, I quickly plotted my ...
2
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1answer
59 views

Choosing variable transformations in non-linear relationships

I am confused about how to apply a transformation to my predictor/response variables to test curvilinear relationships. I read about log transformations, polynomials, quadratic functions. But I am not ...
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2answers
93 views

Good machine learning algo for partial derivatives?

Does anyone know of good robust algos to estimate partial derivatives of a regression model? I am talking about a general regression model like this: $\mathbb{E}(y|x_1, x_2, ... x_n) = f(x_1, x_2, ...
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0answers
21 views

how to implement linear or non linear regression for 3d position estimation?

I am a beginner in Machine Learning. For my project I need a regression algorithm that can estimate the 3D position of a device based on some constraints (moreover inputs). I know how to implement ...
2
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2answers
149 views

R: Fitting a model with periodic, nonlinear and categorical components

Can anyone give me some advice on how to fit a model with linear (some categorical), non-linear and time series components in R? I don't want to use a non-parametric model like a Loess smooth or ...
2
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0answers
42 views

Catagorical variables with very uneven distributions? Removal/modify/leave?

In my current dataset I have quite a few categorical variables. Most have decent distributions between the categories. 30:40:30 splits etc. where these are percentage of dataset members per category ...
3
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2answers
40 views

What model would be appropriate for predicting electrical consumption given multiple (mostly) independent variables?

I have about 1000 samples worth of daily electrical consumption for a building. I'd like to build a predictor based on a number of observable inputs, including: daily temperature (continuous) hours ...
3
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1answer
39 views

Fit nonlinear parameter

I'm attempting to fit this model: $P = C_0 + C_1*U^r$ Given known vectors of observations $P$ and $U$, I want to fit values for $C_0$, $C_1$ and $r$. How do I make this fit in R? or preferably GSL ...
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2answers
25 views

Can time be squared to develop a curvilinear model of crop yield against time?

I am developing a linear model of yield against time (33 years of yield data) where year is 1975,1976....2007. I want to know whether change in yield over time was linear or not. So I fitted a linear ...
2
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1answer
80 views

N-sigma curves for a non-linear least square curve fit

I'm using python's scipy.optimize.curve_fit routine (which uses a non-linear least squares) to fit an exponential function of the form: ...
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1answer
52 views

Multiple regression analysis with spatial data as independent variable

In my PhD thesis I am working on spatial modeling of different chemical parameters in groundwater, and for spatial modeling I am also using the multiple statistical approach. I have a question about ...
4
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1answer
191 views

Residual plot for nonlinear regression

I have a couple of questions regarding performance of nonlinear regression models. Are the residuals from a nonlinear regression model supposed to be randomly distributed too (as in linear ...
2
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1answer
14 views

Error on extrapolated values from a fitted function

I have some data points and I fitted a function (2nd order polynomial here) to the data. The algorithm (scipy.optimize.curve_fit) gave me the optimal parameters and ...
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1answer
53 views

Why can certain variables in a multiple regression not be included in logarithmic form?

I have a multiple regression equation where log(salary) = b0 + b1(ceotenure). What is the purpose of putting the dependent variable in logarithmic form? How would you interpret the change in y for a ...
6
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1answer
239 views

Nonlinear regression

I have some functions of $x$, in the form of $d\sqrt{x}$ or $d\log(x)$ where $d$ is known. I would like to rewrite (approximate is fine) them in the form $a/(1 + bx^c)$, where $a$, $b$ and $c$ are ...
1
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1answer
68 views

$R^2$ correspondence for nonlinear time series

Is there a statistical measure for nonlinear time series data that is comparable to $R^2$ value in linear regression (giving an idea of how well the fit is)? The data is not monotonic, so I cannot ...
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0answers
42 views

Linear Kernel taking more time to train than RBF Kernel (SVR)

I'm doing a Support Vector Regression with about 70k samples with 500 features each. I'm using sklearn implementation of SVR and my input for the train set is a sparse matrix. But, for my surprise, ...
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0answers
44 views

Guessing starting values for PARAMETERS statement in PROC NLIN

How to guess what should be the starting values for beta estimates, which we need to specify in PARMS or PARAMETERS statement while using PROC NLIN (PROC NLIN is used to run non-linear regression in ...
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63 views

Google Prediction API Flaws

I have been testing the google prediction api https://developers.google.com/prediction/ It seems to be excellent, i gave it 5 features so it can predict a regression problem, the API fitts the data ...
6
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1answer
126 views

Gaussian mixture regression in higher dimensions

Problem: I have a discrete representation of a surface/height-map $z = f(x,y)$ that i want to model as a mixture of gaussians (please take probability distributions out of your mind for a moment). ...
0
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1answer
26 views

How can I apply LR with non-linear features?

My training data looks like this: ...
4
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3answers
338 views

Any algorithms better than polynomial regression

I am trying to fit a baseline through my data, and I am not getting close enough with polynomial regression. I used gradient descent to set the parameters. Are there any other ways or algorithms that ...
3
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1answer
119 views

Is this identifiable?

I am interested in the following model : ($1 \leq i \leq p$, $1 \leq j \leq n_{i}$) $$y_{i,j} = A (1+a_{i})(t_{i,j}+\gamma_{i}) + \varepsilon_{i,j}$$ where $A \in \mathbb{R}$, $(a_{i})_{1 \leq i ...
0
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1answer
30 views

Looking for measurement for nonlinear model

I use KNN regression to train out a model. The model estimates running time of a program based on different inputs, and the output is a single variable, which is time (double type). I want to ...
0
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0answers
30 views

Parametric spline regression after Gaussian process regression?

I'm trying to develop a (functional) model to predict the output of a computer simulation. I've run a small set of experiments, varying the 4 predictors $a$, $b$, $c$, and $d$ via LHS, and calculated ...
0
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0answers
29 views

Regression with t distributed autocorrelated errors

I am new, so please bear with me, until its correctly formulated: In short: I am doing simultaneous non-linear regression (parameter estimation) of two different parametric models to three different, ...
0
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1answer
77 views

Confidence region about a regression model

I have a set of means and standard deviations. For each mean I can calculate a 95% confidence interval. I plot these means and confidence intervals against an independent variable and I fit a best ...
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54 views

Predictive model for heavy tailed distribution

I have a variable with values strongly skewed towards zero: table() ...