# nugget: Gaussian Process Nugget Related Functions In mlegp: Maximum Likelihood Estimates of Gaussian Processes

 mlegp-nugget-related R Documentation

## Gaussian Process Nugget Related Functions

### Description

Functions for detecting replicates and for calculating sample variance at specific design points

### Usage

```varPerReps(X, Y)
estimateNugget(X, Y)
anyReps(X)
```

### Arguments

 `X` the design matrix `Y` a vector (or 1 column matrix) of observations

### Value

`varPerReps` returns a 1-column matrix where element i corresponds to the sample variance in observations corresponding to design point X[i]

`estimateNugget` returns a double calculated by taking the mean of the matrix returned by `varPerReps`

`anyReps` returns `TRUE` if two or more rows of `X` are identical

### Note

These functions are used by `mlegp` to set an initial value of the nugget when a constant nugget is being estimated. The function `varPerReps` may also be useful for specifying the form of the nugget matrix for use with `mlegp`.

### Author(s)

Garrett M. Dancik dancikg@easternct.edu

### Examples

```
x = matrix(c(1,1,2,3,3))   # the design matrix
y = matrix(c(5,6,7,0,10))  # output

anyReps(x)
varPerReps(x,y)
estimateNugget(x,y)

```

mlegp documentation built on March 18, 2022, 5:29 p.m.