R-squared value for fitted line
7 vues (au cours des 30 derniers jours)
Afficher commentaires plus anciens
I have plotted log-log graph for data series. than fit a line by ployfit i want to find R-squared for line and data how it can be done (R-squared is explained variance)
0 commentaires
Réponse acceptée
Star Strider
le 13 Mar 2015
Using the Wikipedia article on Coefficient of Determination, it’s easiest (and likely correct) to compute the R-squared value on your data using the nonlinear regression and not the log-log linear fit:
dry=[49 12 5 1 1 1 0 0 0 ];
x1=[1 2 3 4 5 6 7 8 9];
x1dry = linspace(min(x1), max(x1));
pwrfit = @(b,x) b(2) .* x.^b(1);
OLSCF = @(b) sum((dry-pwrfit(b,x1)).^2);
B = fminsearch(OLSCF, [-2; 50]);
SStot = sum((dry - mean(dry)).^2); % Compute R-squared
SSres = OLSCF(B);
Rsq = 1 - (SSres/SStot);
When I did this calculation, I got R-squared to be 0.998. Do the same with your ‘wet’ value, with the appropriate changes in the code.
2 commentaires
Star Strider
le 14 Mar 2015
Modifié(e) : Star Strider
le 14 Mar 2015
My pleasure.
When I run this code:
wet=[120 49 30 21 12 10 9 7 4];
dry=[49 12 5 1 1 1 0 0 0 ];
x1=[1 2 3 4 5 6 7 8 9];
pwrfit = @(b,x) b(2) .* x.^b(1);
% ‘Dry’ Calculations
OLSCF = @(b) sum((dry-pwrfit(b,x1)).^2);
Bdry = fminsearch(OLSCF, [-2; 50]);
SStot = sum((dry - mean(dry)).^2); % Compute R-squared
SSres = OLSCF(Bdry);
RsqDry = 1 - (SSres/SStot);
% ‘Wet’ Calcualtions
OLSCF = @(b) sum((wet-pwrfit(b,x1)).^2);
Bwet = fminsearch(OLSCF, [-2; 50]);
SStot = sum((wet - mean(wet)).^2); % Compute R-squared
SSres = OLSCF(Bwet);
RsqWet = 1 - (SSres/SStot);
fprintf(1, '\n\t Wet = %8.3f * x^%.3f,\t\tRsq = %.4f\n', Bwet(2), Bwet(1), RsqWet)
fprintf(1, '\n\t Dry = %8.3f * x^%.3f,\t\tRsq = %.4f\n', Bdry(2), Bdry(1), RsqDry)
I get:
Wet = 120.447 * x^-1.323, Rsq = 0.9980
Dry = 49.123 * x^-2.148, Rsq = 0.9977
I have no idea why you’re getting 0 for those. At worst, if your default format is set to something that rounds to integers, you should get 1 instead.
Copy and paste my code and run it. You should get the same results.
Plus de réponses (0)
Voir également
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!