sampleCovMat {bayesSurv} R Documentation

## 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 arnost.komarek@mff.cuni.cz

### 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)


[Package bayesSurv version 3.7 Index]