matlab因子旋转因子得分程序.doc

因子旋转因子得分
clear all;
close all;
clc;

% load data
data = xlsread('stockdata');

% transform data
xt = data;
xt(:,[1,3,10,11]) = data(:,[1,3,10,11])./(10.^9);
xt(:,[2,4]) = data(:,[2,4])./(10.^10);
xt(:,5) = data(:,5)./(10.^5);
xt(:,[6,12,13]) = data(:,[6,12,13])./(10.^11);
xt(:,7) data(:,7)./(10.^2);
xt(:,8) = data(:,8).*10;
xt(:,9) = data(:,9)./10;
for i=1:13
zz(:,i)=(xt(:,i)-mean(xt(:,i)))/(sqrt(var(xt(:,i))));
end

% correlation matrix
dat = corr(zz);

% Maximum Likelihood Factor Analysis without varimax rotation
% factanal performs maximum-likelihood factor analysis
[lambda,psi,T,stats,F] = factoran(zz,3,'xtype','data','rotate','none');
%estimated factor loadings
% the estimated factor loadings matrix
ld = lambda;
com = diag(ld*ld'); %communalities are calculated
psi = diag(dat)-diag(ld*ld'); %specific variances are calculated
tbl = [lambda(:,1),lambda(:,2),lambda(:,3),com,psi]
figure(1)
subplot(2,2,1)
s=[ 'X1 '
'X2 '
'X3 '
'X5 '
'X6 '
'X7 '
'X8 '
'X9 '
'X10'
'X11'
'X12'
'X13'
'X14'];
% plot first factor against second
hold on
plot(lambda(:,1),lambda(:,2),'w')
ylim([-0.7,0.7])
title('Factors21 - theta21')
ylabel('y')
xlabel('x')
for i=1:13
text(lambda(i,1),lambda(i,2),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% plot first factor against third
subplot(2,2,3)
hold on
plot(lambda(:,1),lambda(:,3),'w')
xlabel('x')
ylabel('y')
title('Factors31 - theta31')
ylim([-0.5,0.5])
for i=1:13
text(lambda(i,1),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off
% plot second factor against third
subplot(2,2,2)
hold on
plot(lambda(:,2),lambda(:,3),'w')

xlabel('x')
ylabel('y')
title('Factors32 - theta32')
xlim([-0.7,0.7])
ylim([-0.5,0.5])
for i=1:13
text(lambda(i,2),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% Maximum Likelihood Factor Analysis after varimax rotation
lambda = rotatefactors(ld, 'Method','varimax'); %rotates the factor loadings matrix
% and estimates factor loadings after varimax
vl = [lambda(:,1),lambda(:,2),lambda(:,3)];
com = diag(vl*vl'); %communalities are calculated
psi = diag(dat)-diag(vl*vl'); %specific variances are calculated
tbl = [vl,com,psi]

figure(2)
subplot(2,2,1)
hold on
% plot first factor against second
plot(lambda(:,1),lambda(:,2),'w')
xlabel('x')
ylabel('y')
title('Factors21 - theta21')
for i=1:13
text(lambda(i,1),lambda(i,2),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% plot first factor against third
subplot(2,2,3)
plot(lambda(:,1),lambda(:,3),'w')
xlabel('x')
ylabel('y')
title('Factors31 - theta31')
for i=1:13
text(lambda(i,1),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% plot second factor against third
subplot(2,2,2)
hold on
plot(lambda(:,2),lambda(:,3),'w')
xlabel('x')
ylabel('y')
title('Factors32 - theta32')
for i=1:13
text(lambda(i,2),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% Principal Component Method after varimax rotation
% spectral decomposition
[eigvec,eigval] = eigs(dat);
E = ones(size(eigvec(:,1:3)))*eigval(1:3,1:3);

% the estimated factor loadings matrix
Q = sqrt(E).*eigvec(:,1:3);
lambda = rotatefactors(Q, 'Method', 'varimax'); %rotates the factor loadings matrix
%and estimates factor loadings after varimax
ld = [lambda(:,1),lambda(:,2),lambda(:,3)];
com = diag(ld*ld'); %communalities are calculated
psi = diag(dat)-diag(ld*ld'); %specific variances are calculated
tbl = [ld,com,psi]

figure(3)
% plot first factor against second
subplot(2,2,1)
hold on
plot(lambda(:,1),lambda(:,2),'w')
xlabel('x')
ylabel('y')
title('Factors21 - theta21')
for i=1:13
text(lambda(i,1),lambda(i,2),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% plot first factor against third
subplot(2,2,3)
plot(lambda(:,1),lambda(:,3),'w')
xlabel('x')
ylabel('y')
title('Factors31 - theta31')
for i=1:13
text(lambda(i,1),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% plot second factor against third
subplot(2,2,2)
hold on
plot(lambda(:,2),lambda(:,3),'w')
xlabel'x')
ylabel('y')
title('Factors32 - theta32')
for i=1:13
text(lambda(i,2),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% Principal Factor Method after varimax rotation
% inverse of the correlation matrix dat
f = inv(dat);
% preliminary estimate of psi
psiini = diag(diag(1./f));
psi = psiini;

[eigvec,eigval] = eigs(dat-psi);
EE = ones(size(eigvec(:,1:3)))*eigval(1:3,1:3);
QQ = sqrt(EE).*eigvec(:,1:3);
psiold = psi;
psi = diag(1-sum((QQ.*QQ)'));

z = psi-psiold;
convergence = z;

lambda = rotatefactors(QQ, 'Method', 'varimax'); %rotates the factor loadings matrix
% and estimates factor loadings after varimax
ld = [lambda(:,1),lambda(:,2),lambda(:,3)];
com = diag(ld*ld'); %communalities are calculated
psi = diag(dat)-diag(ld*ld'); %specific variances are calculated
tbl = [ld,com,psi]

figure(4)
% plot first factor against second
subplot(2,2,1)
hold on
plot(lambda(:,1),lambda(:,2),'w')
xlabel('x')
ylabel('y')
title('Factors21 - theta21')
for i=1:13
text(lambda(i,1),lambda(i,2),s(i,1:3));
end;
line([-1,1],[0,0])
line([00],[-1,1])
box on
hold off

% plot first factor against third
subplot(2,2,3)
plot(lambda(:,1),lambda(:,3),'w')
xlabel('x')
ylabel('y')
title('Factors31 - theta31')
for i=1:13
text(lambda(i,1),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off

% plot second factor against third
subplot(2,2,2)
hold on
plot(lambda(:,2),lambda(:,3),'w')
xlabel('x')
ylabel('y')
title('Factors32 - theta32')
for i=1:13
text(lambda(i,2),lambda(i,3),s(i,1:3));
end;
line([-1,1],[0,0])
line([0,0],[-1,1])
box on
hold off