Matlab regression

 

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% linear regression % least squares fitting a straight line load LinearRegressData.mat plot(x,y,'.') X=[ones(size(x)),x]; a=X\y   a =       5.2745     0.9950 clear load jump.mat plot(x,y,'.') X=[ones(size(x)),x]; a=X\y   a =      -0.0095     0.7465 Y=-0.0095 + 0.7465*x; plot(x,Y,'.') x1=x; x2=x.^2; x3=x.^3; X=[ones(size(x)),x1,x2,x3]; a=X\y   a =      -0.0226     1.3906     0.0389    -1.0536 yp=X*a; plot(x,y,'o',x,yp,'-') % weighted factors in linear regression hold on indx=find(x>0); X(indx,:)=X(indx,:)/10; yw=y; yw(indx)=yw(indx)/10; aw=X\yw; ypw=aw(1)+aw(2)*x; plot(x,ypw,'g-') clear load DIC_O2.mat x=DIC; y=O2; plot(x,y,'.') xlabel('DIC') ylabel('O_2') X=[ones(size(x)),x]; a=X\y; hold on yp=X*a; plot(x,yp,'-') X=[ones(size(x)),x]; Y=[ones(size(x)),y]; b=Y\x; xp=Y*b; plot(xp,y,'m-')

　　

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>> % type 2 regression >> c(2)=sqrt(a(2)/b(2))   c =            0    0.3559   >> c(2)=-c(2)   c =            0   -0.3559   >> c(1)=mean(y)-c(2)*mean(x)   c =     931.6370   -0.3559 >> yII=c(1)+c(2)*x; >> plot(x,yII,'g-')   % multidimensional regression

　　

  

 

 

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1 2	function y=modfunc(a,x) y=a(1)+a(2)*exp(-(x-a(3)).^2./(2.*a(4).^2));

　　

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>> load gauss_data.mat >> plot(x,y,'o') >> a0=[1,10,4,1]; >> hold on >> ezplot(@(x)modfunc(a0,x),[0,10]) >> a=nlinfit(x,y,@modfunc,a0)   a =       0.6731   10.0709    4.4948    0.7142   >> ezplot(@(x)modfunc(a,x),[0,10])<br>>> [a,R,J,CovB]=nlinfit(x,y,@modfunc,a0);<br>

    >> err=sqrt(diag(CovB))

err =

0.0828
0.2318
0.0216
0.0202