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], ..., y[n] is a sequence of p-dimensional vectors y[i] the sample covariance matrix S is equal to

S = (1/(n-1)) sum[i=1][n] (y[i] - m)(y[i] - m)'

where

m = (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 Dec. 5, 2022, 5:22 p.m.