sampleCovMat: Compute a sample covariance matrix.

In bayesSurv: Bayesian Survival Regression with Flexible Error and Random Effects Distributions

Compute a sample covariance matrix.

Description

This function computes a sample covariance matrix.

Usage

sampleCovMat(sample)

Arguments

sample

a matrix or data.frame with sampled values in rows. I.e. number of rows of sample determines a sample size, number of columns of sample determines a dimension of the distribution from which it was sampled.

Details

When y_1, \dots, y_n is a sequence of p-dimensional vectors y_i the sample covariance matrix S is equal to

S = \frac{1}{n-1} \sum_{i=1}^n (y_i - m)(y_i - m)^T

where

m = \frac{1}{n}\sum_{i=1}^n y_i.

When n=1 the function returns just sum of squares.

Value

This function returns a matrix.

Author(s)

Arnošt Komárek [e-mail]

Examples

  ## Sample some values
  z1 <- rnorm(100, 0, 1)           ## first components of y
  z2 <- rnorm(100, 5, 2)           ## second components of y
  z3 <- rnorm(100, 10, 0.5)        ## third components of y

  ## Put them into a data.frame
  sample <- data.frame(z1, z2, z3)

  ## Compute a sample covariance matrix
  sampleCovMat(sample)

bayesSurv documentation built on Sept. 30, 2024, 9:34 a.m.