cov.MSSD {fastmatrix} | R Documentation |
Mean Square Successive Difference (MSSD) estimator of the covariance matrix
Description
Returns a list containing the mean and covariance matrix of the data.
Usage
cov.MSSD(x)
Arguments
x |
a matrix or data frame. As usual, rows are observations and columns are variables. |
Details
This procedure uses the Holmes-Mergen method using the difference between each successive pairs of observations also known as Mean Square Successive Method (MSSD) to estimate the covariance matrix, which is given by
\bold{S}_{HD} = \frac{1}{2(n-1)} \sum\limits_{i=2}^n (\bold{x}_i - \bold{x}_{i-1})(\bold{x}_i - \bold{x}_{i-1})^T.
Value
A list containing the following named components:
mean |
an estimate for the center (mean) of the data. |
cov |
the estimated covariance matrix. |
References
Holmes, D.S., Mergen, A.E. (1993).
Improving the performance of the T^2
control chart.
Quality Engineering 5, 619-625.
See Also
Examples
x <- cbind(1:10, c(1:3, 8:5, 8:10))
z0 <- cov(x)
z0
z1 <- cov.MSSD(x)
z1
[Package fastmatrix version 0.5-9017 Index]