matlab中有没有canoncorr,有的话怎么用

matlab中有没有canoncorr,有的话怎么用

典范相关分析(canoncorr) [a,b,r,u,v,stats] = canoncorr(X, Y)

[A,B,R,U,V,STATS] = CANONCORR(X,Y) returns a structure containing
information relating to the sequence of D null hypotheses H0_K, that
the (K+1)st through Dth correlations are all zero, for K = 0:(D-1).
STATS contains eight fields, each a 1-by-D vector with elements
corresponding to values of K:

Wilks: Wilks' lambda (likelihood ratio) statistic
chisq: Bartlett's approximate chi-squared statistic for H0_K,
with Lawley's modification
pChisq: the right-tail significance level for CHISQ
F: Rao's approximate F statistic for H0_K
pF: the right-tail significance level for F
df1: the degrees of freedom for the chi-squared statistic,
also the numerator degrees of freedom for the F statistic
df2: the denominator degrees of freedom for the F statistic

Example:

load carbig;
X = [Displacement Horsepower Weight Acceleration MPG];
nans = sum(isnan(X),2) > 0;
[A B r U V] = canoncorr(X(~nans,1:3), X(~nans,4:5));

plot(U(:,1),V(:,1),'.');
xlabel('0.0025*Disp + 0.020*HP - 0.000025*Wgt');
ylabel('-0.17*Accel + -0.092*MPG')

典范相关分析(canoncorr) [a,b,r,u,v,stats] = canoncorr(X, Y)
[A,B,R,U,V,STATS] = CANONCORR(X,Y) returns a structure containing
information relating to the sequence of D null hypotheses H0_K, that
the (K+1)st through Dth correlations are all zero, for K = 0:(D-1).
STATS contains eight fields, each a 1-by-D vector with elements
corresponding to values of K:
Wilks: Wilks' lambda (likelihood ratio) statistic
chisq: Bartlett's approximate chi-squared stati stic for H0_K,
with Lawley's modification
pChisq: the right-tail significance level for CHISQ
F: Rao's approximate F statistic for H0_K
pF: the right-tail significance level for F
df1: the degrees of freedom for the chi-squared statistic,
also the numerator degrees of freedom for the F statistic
df2: the denominator degrees of freedom for the F statistic
Example:
load carbig;
X = [Displacement Horsepower Weight Acceleration MPG];
nans = sum(isnan(X),2) > 0;
[A B r U V] = canoncorr(X(~nans,1:3), X(~nans,4:5));
plot(U(:,1),V(:,1),'.');
xlabel('0.0025*Disp + 0.020*HP - 0.000025*Wgt');
ylabel('-0.17*Accel + -0.092*MPG')