sampleCovMat {bayesSurv} | R Documentation |

## Compute a sample covariance matrix.

### Description

This function computes a sample covariance matrix.

### Usage

```
sampleCovMat(sample)
```

### Arguments

`sample` |
a |

### 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]