p********0
发帖数: 186 | 1 Hi,
I have 300 observation of two diemensional data, X1(a1, b1), X2(a2, b2), ...
X3(a3, b3).
how do I use the PCA analysis to get the eigen vector and eigen value?
Do I need to get covariance matrix first? E(a) = Average(a) and E(b) =
average(b).
All the E(X1) = E(X2) = ... = E(Xn)???
How do I get Covariance Matrix Cov(Xi, Xj) | d******e
发帖数: 7844 | 2 你要求两个样本的Coviarance Matrix?
..
【在 p********0 的大作中提到】
: Hi,
: I have 300 observation of two diemensional data, X1(a1, b1), X2(a2, b2), ...
: X3(a3, b3).
: how do I use the PCA analysis to get the eigen vector and eigen value?
: Do I need to get covariance matrix first? E(a) = Average(a) and E(b) =
: average(b).
: All the E(X1) = E(X2) = ... = E(Xn)???
: How do I get Covariance Matrix Cov(Xi, Xj)
| o****o
发帖数: 8077 | 3 To obtain Eigen Decomposition from PCA, you need to observe the relationship below:
eigen decomposition of square matrix obtains the same eigen vector matrix as in PCA (the V matrix)
and Eigen values are those satisfy: AV=A[v1, v2...vk]=[\lambda1, \lambda2...\lambda_k].*V
so that you can first use PROC PRINCOMP NOINT COV outstat=_V(where=(_TYPE_='USCORE'))
then conduct matrix multiplication of A%*%V=\Omega
load \Omega and V into a data set, divid each element of \Omega by corresponding element i | g**a
发帖数: 2129 | 4 better use correlation matrix instead of covariance matrix. Call eigen() in
SAS to get eigen value and eigen vector. |
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