相关矩阵

3 回答

• 1

  x1=rnorm(20)
  x2=rnorm(20)
  x3=rnorm(20)
  y=rnorm(20)
  data=cbind(y,x1,x2,x3)
  cor(data)
  

  回复于 2024-04-28T02:34:52+08:00
• 2

  如果我已经正确理解，你有一个3列的矩阵（比如说x1到x3）和很多行（作为y值） . 您可以采取以下行动：

  foo = matrix(runif(30), ncol=3) # creating a matrix of 3 columns
  cor(foo)
  

  如果你已经在3个向量x1到x3中的值，你可以像这样制作 foo ： foo=data.frame(x1,x2,x3)

  回复于 2024-04-28T02:34:52+08:00
• 1

  如果我错了，请纠正我，但假设这与回归问题有关，这可能就是你要找的：

  #Set the number of data points and build 3 independent variables
  set.seed(0)
  numdatpoi <- 7
  x1 <- runif(numdatpoi)
  x2 <- runif(numdatpoi)
  x3 <- runif(numdatpoi)
  
  #Build the dependent variable with some added noise
  noisig <- 10
  yact <- 2 + (3 * x1) + (5 * x2) + (10 * x3)
  y <- yact + rnorm(n=numdatpoi, mean=0, sd=noisig)
  
  #Fit a linear model
  rmod <- lm(y ~ x1 + x2 + x3)
  
  #Build the variance-covariance matrix.  This matrix is typically what is wanted.
  (vcv <- vcov(rmod))
  
  #If needed, convert the variance-covariance matrix to a correlation matrix
  (cm <- cov2cor(vcv))
  

  从上面，这里是方差 - 协方差矩阵：

  (Intercept)        x1        x2        x3
  (Intercept)    466.5773   14.3368 -251.1715 -506.1587
  x1              14.3368  452.9569 -170.5603 -307.7007
  x2            -251.1715 -170.5603  387.2546  255.9756
  x3            -506.1587 -307.7007  255.9756  873.6784
  

  而且，这是相关的相关矩阵：

  (Intercept)          x1         x2         x3
  (Intercept)  1.00000000  0.03118617 -0.5908950 -0.7927735
  x1           0.03118617  1.00000000 -0.4072406 -0.4891299
  x2          -0.59089496 -0.40724064  1.0000000  0.4400728
  x3          -0.79277352 -0.48912986  0.4400728  1.0000000
  

  回复于 2024-04-28T02:34:52+08:00