Error in nonlinear regression
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Keegan Carvalho
le 10 Oct 2018
Commenté : Keegan Carvalho
le 27 Oct 2018
I am trying to make a nonlinear regression model of the attached csv file. I used the following code:
if true
data=readtable('ban1.csv')
sea=data(:,1)
sst=data(:,2)
at=data(:,3)
X = [sst,at];
y = sea;
modelfun = @(b,x)b(1) + b(2)*x(:,1).^b(3) + ...
b(4)*x(:,2).^b(5)
beta0 = [100 100 100 100 100];
mdl = fitnlm(X,y,modelfun,beta0)
% code
end
I am getting the following errors:
Error using internal.stats.parseArgs (line 42) Wrong number of arguments.
Error in NonLinearModel.fit (line 1385) internal.stats.parseArgs(paramNames, paramDflts, otherArgs{:});
Error in fitnlm (line 99) model = NonLinearModel.fit(X,varargin{:});
Would be grateful if someone can help me, if possible with code. I would also like to know where I am going wrong.
Thank you
2 commentaires
Image Analyst
le 12 Oct 2018
Which is x and which is y? Why are you having 2 x but only 1 corresponding y? What do you want along the x/horizontal/independent axis and what do you want along the y/vertical/dependent axis?
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Star Strider
le 10 Oct 2018
You are not extracting your table variables correctly.
This works (extracting the data from your table):
X = [data.sst, data.at];
y = data.sea;
modelfun = @(b,x)b(1) + b(2)*x(:,1).^b(3) + ...
b(4)*x(:,2).^b(5)
beta0 = [100 100 100 100 100];
mdl = fitnlm(X,y,modelfun,beta0)
and so does this (using your table, and simply rearranging its columns to be compatible with what fitnlm expects):
new_table = data(:, [2 3 1]);
modelfun = @(b,x)b(1) + b(2).*x(:,1).^b(3) + ...
b(4).*x(:,2).^b(5)
beta0 = [100 100 100 100 100];
mdl = fitnlm(new_table,modelfun,beta0)
The ‘carbig’ model is likely not appropriate for your sea level and temperature data, so I am not surprised that the model fails, even though the code now runs without error.
6 commentaires
Star Strider
le 13 Oct 2018
My pleasure.
This is not my area of expertise. I suggest you do an Interweb search for an appropriate model.
Plus de réponses (2)
Image Analyst
le 13 Oct 2018
You should use scatteredInterpolant(). Write back if you can't figure it out.
1 commentaire
Star Strider
le 13 Oct 2018
Keegan Carvalho has a complete data set, so interpolation would likely not gain anything.
Image Analyst
le 13 Oct 2018
Try using scatteredInterpolant(). Below is a complete demo:
clc; % Clear the command window.
close all; % Close all figures (except those of imtool.)
clear; % Erase all existing variables. Or clearvars if you want.
workspace; % Make sure the workspace panel is showing.
format long g;
format compact;
fontSize = 20;
format bank
% Read in data.
data=importdata('ban1.csv')
data = data.data;
% Extract various components.
sea=data(:,1)
sst=data(:,2)
at=data(:,3)
%================================ MAIN PART RIGHT HERE ==============================================
% Make the scattered interpolant.
F = scatteredInterpolant(sst, at, sea)
% Get a grid of points at every pixel location in the RGB image.
resolution = 300; % Number of points across that you want ot evaluate it at.
x = linspace(min(sst), max(sst), resolution);
y = linspace(min(at), max(at), resolution);
[xGrid, yGrid] = meshgrid(x, y);
xq = xGrid(:);
yq = yGrid(:);
% Evaluate the interpolant at query locations (xq,yq).
vq = F(xq, yq);
fittedImage = reshape(vq, resolution, resolution);
%================================ END OF MAIN PART ==============================================
imshow(fittedImage, [], 'XData', x, 'YData', y);
axis on;
xlabel('sst', 'FontSize', fontSize);
ylabel('at', 'FontSize', fontSize);
title('sea as a function of sst and at', 'FontSize', fontSize);
colorbar;
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0, 0.04, 1, 0.96]);
% For fun, overlay the known points.
hold on;
plot(sst, at, 'r*', 'MarkerSize', 20, 'LineWidth', 2);
You can use a colormap if you want.
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