linear regression statistical parameters
1 vue (au cours des 30 derniers jours)
Afficher commentaires plus anciens
Hello,
This is a question following my previous one but I explain the problem here as well. I am trying to use linear and nonlinear regression to predict a response. I am wondering how I can get the most possible statistical results from regress or nlinfit (like durbin watson, probabilities, R2, adjusted R2, etc.).
y=(c.^4+2*c.*p+3*p.^3-c+2*d.^0.5)'; % a sample response
X = [c;p;d]';
beta0 = [1 -2 0 -1 0 1 1];
X = [ones(size(c)); c.^4 ;c.*p; p.^3 ;c; d.^0.5]';
[b,stats] = regress(y,X)
Results: b =
0
1.0000
2.0000
3.0000
-1.0000
2.0000
stats = (how to interpret these?)
0 0
NaN NaN
NaN NaN
NaN NaN
NaN NaN
NaN NaN
0 commentaires
Réponses (1)
Ahmet Cecen
le 20 Avr 2015
There is a good chance there are other things wrong with your problem, but first off, it is:
[b,bint,r,rint,stats] = regress(y,X)
not:
[b,stats] = regress(y,X)
in your case, your stats is actually bint...
if you do not want the other results, do this instead:
[b,~,~,~,stats] = regress(y,X)
3 commentaires
Ahmet Cecen
le 21 Avr 2015
Modifié(e) : Ahmet Cecen
le 21 Avr 2015
Now your stats looks like its actually rint. Stats would look like:
stats=
number <- R2 statistic
number <- the F statistic
number <- p value of F statistic
number <- estimate of the error variance
Voir également
Catégories
En savoir plus sur Linear and Nonlinear Regression dans Help Center et File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!