• corrcoefCorrelation coefficientsSyntaxR = corrcoef(X)R = corrcoef(x,y)[R,P]=corrcoef(...)[R,P,RLO,RUP]=corrcoef(...)[...]=corrcoef(...,'param1',val1,'param2',val2,...)DescriptionR = corrcoef(X) return...

corrcoef
Correlation coefficients
Syntax
R = corrcoef(X)R = corrcoef(x,y)[R,P]=corrcoef(...)[R,P,RLO,RUP]=corrcoef(...)[...]=corrcoef(...,'param1',val1,'param2',val2,...)
Description
R = corrcoef(X) returns a matrix R of correlation coefficients calculated from an input matrix X whose rows are observations and whose columns are variables. The matrix R = corrcoef(X) is related to the covariance matrix C = cov(X) by
corrcoef(X) is the zeroth lag of the normalized covariance function, that is, the zeroth lag of xcov(x,'coeff') packed into a square array.
R = corrcoef(x,y) where x and y are column vectors is the same as corrcoef([x y]). If x and y are not column vectors, corrcoef converts them to column vectors. For example, in this case R=corrcoef(x,y) is equivalent to R=corrcoef([x(:) y(:)]).
[R,P]=corrcoef(...) also returns P, a matrix of p-values for testing the hypothesis of no correlation. Each p-value is the probability of getting a correlation as large as the observed value by random chance, when the true correlation is zero. If P(i,j) is small, say less than 0.05, then the correlation R(i,j) is significant.
[R,P,RLO,RUP]=corrcoef(...) also returns matrices RLO and RUP, of the same size as R, containing lower and upper bounds for a 95% confidence interval for each coefficient.
[...]=corrcoef(...,'param1',val1,'param2',val2,...) specifies additional parameters and their values. Valid parameters are the following.
'alpha'A number between 0 and 1 to specify a confidence level of 100*(1 - alpha)%. Default is 0.05 for 95% confidence intervals.'rows'Either 'all' (default) to use all rows, 'complete' to use rows with no NaN values, or 'pairwise' to compute R(i,j) using rows with no NaN values in either column i or j.The p-value is computed by transforming the correlation to create a t statistic having n-2 degrees of freedom, where n is the number of rows of X. The confidence bounds are based on an asymptotic normal distribution of 0.5*log((1+R)/(1-R)), with

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MATLAB Function Reference        copyobj   cos
corrcoef
Correlation coefficients
Syntax
R = corrcoef(X)
R = corrcoef(x,y)
[R,P]=corrcoef(...)
[R,P,RLO,RUP]=corrcoef(...)
[...]=corrcoef(...,'param1',val1,'param2',val2,...)
Description
R = corrcoef(X) returns a matrix R of correlation coefficients calculated from an input matrix X whose rows are observations and whose columns are variables. The matrix R = corrcoef(X) is related to the covariance matrix C = cov(X) by
corrcoef(X) is the zeroth lag of the normalized covariance function, that is, the zeroth lag of xcov(x,'coeff') packed into a square array.
R = corrcoef(x,y) where x and y are column vectors is the same as corrcoef([x y]). If x and y are not column vectors, corrcoef converts them to column vectors. For example, in this case R=corrcoef(x,y) is equivalent to R=corrcoef([x(:) y(:)]).
[R,P]=corrcoef(...) also returns P, a matrix of p-values for testing the hypothesis of no correlation. Each p-value is the probability of getting a correlation as large as the observed value by random chance, when the true correlation is zero. If P(i,j) is small, say less than 0.05, then the correlation R(i,j) is significant.
[R,P,RLO,RUP]=corrcoef(...) also returns matrices RLO and RUP, of the same size as R, containing lower and upper bounds for a 95% confidence interval for each coefficient.
[...]=corrcoef(...,'param1',val1,'param2',val2,...) specifies additional parameters and their values. Valid parameters are the following.
'alpha'
A number between 0 and 1 to specify a confidence level of 100*(1 - alpha)%. Default is 0.05 for 95% confidence intervals.
'rows'
Either 'all' (default) to use all rows, 'complete' to use rows with no NaN values, or 'pairwise' to compute R(i,j) using rows with no NaN values in either column i or j.
The p-value is computed by transforming the correlation to create a t statistic having n-2 degrees of freedom, where n is the number of rows of X. The confidence bounds are based on an asymptotic normal distribution of 0.5*log((1+R)/(1-R)), with an approximate variance equal to 1/(n-3). These bounds are accurate for large samples when X has a multivariate normal distribution. The 'pairwise' option can produce an R matrix that is not positive definite.
Examples
Generate random data having correlation between column 4 and the other columns.
x = randn(30,4);     % Uncorrelated data
x(:,4) = sum(x,2);   % Introduce correlation.
[r,p] = corrcoef(x)  % Compute sample correlation and p-values.
[i,j] = find(p<0.05);  % Find significant correlations.
[i,j]                % Display their (row,col) indices.
r =
1.0000   -0.3566    0.1929    0.3457
-0.3566    1.0000   -0.1429    0.4461
0.1929   -0.1429    1.0000    0.5183
0.3457    0.4461    0.5183    1.0000
p =
1.0000    0.0531    0.3072    0.0613
0.0531    1.0000    0.4511    0.0135
0.3072    0.4511    1.0000    0.0033
0.0613    0.0135    0.0033    1.0000
ans =
4     2
4     3
2     4
3     4
cov, mean, median, std, var
xcorr, xcov in the Signal Processing Toolbox

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• 参考链接：... 可以计算两组数据的相关系数啊 >> a=[0.6557,0.0357,0.8491,0.9340,0.6787]; b=[0.7315,0.1100,0.8884,0.9995,0.6959]; corrcoef(a,b) ans = 1.0
参考链接：http://www.zybang.com/question/cdba651ce57a115d8bad1eff7302e672.html
可以计算两组数据的相关系数啊
>> a=[0.6557,0.0357,0.8491,0.9340,0.6787]; b=[0.7315,0.1100,0.8884,0.9995,0.6959]; corrcoef(a,b) ans = 1.0000 0.9976 0.9976 1.0000 % 第一个1是a与a的相关系数,左边第一个0.9976是a与b相关系数,第二个0.9976是b与a相关系数,第二个1是b与b的相关系数
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• Type corrcoef(Matrix<Type>x, Matrix<Type>y) { Vector<Type>tmp_x(x.size(), x.operator Type *()); Vector<Type>tmp_y(y.size(), y.operator Type *()); return myPearson(tmp_x, tmp_y); }
参考概念https://blog.csdn.net/crcr/article/details/58594432?utm_source=blogxgwz0
用最简单的公式4可以实现下面代码。
//皮尔逊相关系数计算
Type myPearson(Vector<Type>x, Vector<Type>y)
{
Type A = sum(x*y) - (sum(x)*sum(y)) / x.size();
Type B = sqrt((sum(x*x) - sum(x)*sum(x) / x.size())*(sum(y*y) - sum(y)*sum(y) / y.size()));
Type C = A / B;
return C;
}
Type corrcoef(Matrix<Type>x, Matrix<Type>y) {

Vector<Type>tmp_x(x.size(), x.operator Type *());
Vector<Type>tmp_y(y.size(), y.operator Type *());

return myPearson(tmp_x, tmp_y);
}

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• % for i = 1:length(lon) for j = 1:length(lat) [r p] = corrcoef( squeeze(evap(i,j,:)), squeeze(prec(i,j,:)) ); corr(i,j) = r(1,2); end end [lat2d lon2d] = meshgrid(lat, lon); figure(1) set( gcf , '...
• 想用MATLAB中corrcoef函数求两个向量的相关系数。 举报违规检举侵权投诉|2011-02-23 21:32 匿名 | 分类：数学 | 浏览19891次 比如A=[1 2 3];B=[5 3 7]; r= corrcoef(A,B)可以求出相关系数是0.5.为什么两个向量...
• 本代码主要利用MATLAB工具实现MATLAB——cov和corrcoef计算协方差和相关系数，简单明了，易于理解
• 如果x、y是矩阵，那么MATLAB会将其转换为列向量，相当于cov([A(:),B(:)])。 【例4-27】 cov函数使用示例。 >> A = [-1 1 2 ; -2 3 1 ; 4 0 3] A = -1 1 2 -2 3 1 4 0 3 >> C=cov(A) % 协方差矩阵 C = 10.3333 -4....
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• R = corrcoef(A) R = corrcoef(A,B) [R,P] = corrcoef(___) [R,P,RL,RU] = corrcoef(___) ___ = corrcoef(___,Name,Value) 说明 示例 R = corrcoef(A) 返回 A 的相关系数的矩阵，其中 A 的列表示随机变量，行表示...
• 格式 corrcoef(X,Y) %返回列向量X,Y的相关系数，等同于corrcoef([X Y])。 corrcoef (A)    %返回矩阵A的列向量的相关系数矩阵 例 >> A=[1 2 3;4 0 -1;1 3 9] A =  1  2  3  4  0  -1  1  3  9 >> C1...
• 如果x、y是矩阵，那么MATLAB会将其转换为列向量，相当于cov([A(:),B(:)])。 【例4-27】 cov函数使用示例。 >> A = [-1 1 2 ; -2 3 1 ; 4 0 3] A =  -1 1 2  -2 3 1  4 0 3 >> C=cov(A) % ...
• 需要应用MATLAB中的corr(X, Y)或者 corrcoef(X,Y)函数。 其中corr(X, Y)既可以计算矩阵相关也可以计算序列相关，而corrcoef(X,Y)如果X, Y为矩阵，则会将其转换为序列再进行计算。 CORR 伪代码 X,Y # 为两个序列...
• A,B为两个长度相同的向量 求协方差 S=cov(A,B);...相关系数存在许多种类，上述corrcoef 指 pearson correlation coefficient。 扩展阅读 1 【copy from: https://www.cnblogs.com/sansha...
• 计算表所有可以包含非数字变量的数字变量的Spearman相关函数。 它还处理内置的matlab函数corrcoef指定的缺失值，并打印出具有缺失值的变量
• % 生成1000个正态N(0,1)随机数>> [p,h]=ranksum(x,y) %检验x与y分布是否相同 p = 0.6298 h = 0 7、概率和统计相关指令 表1 概率统计主要MATLAB命令 主题词 意义 主题词 意义 max 最大值 random 随机数 min 最小值 ...
• Matlab中实际上有多个函数可以实现回归分析的功能，如regress，polyfit，lsqcurvefit等。这里简单总结一下polyfit函数的用法：Matlab中实际上有多个函数可以实现回归分析的功能，如regress，polyfit，lsqcurvefit等...
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