# Calculate the Scaled Prediction Variance (or SPV)

### Description

Calculates the SPV for a sample of points in a design region of specified type. Sampling is done
by calling `sampler`

.

### Usage

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
spv(n, design, type = "spherical", formula, at = FALSE, keepfun, sample,
unscaled = FALSE, ...)
## S3 method for class 'data.frame'
spv(n, design, type = c("spherical", "cuboidal", "lhs",
"mlhs", "slhs", "rslhs", "custom"), formula, at = FALSE, keepfun, sample,
unscaled = FALSE, ...)
## S3 method for class 'list'
spv(n, design, type = c("spherical", "cuboidal", "lhs", "mlhs",
"slhs", "rslhs", "custom"), formula, at = FALSE, keepfun, sample,
unscaled = FALSE, ...)
## S3 method for class 'matrix'
spv(n, design, type = c("spherical", "cuboidal", "lhs",
"mlhs", "slhs", "rslhs", "custom"), formula, at = FALSE, keepfun, sample,
unscaled = FALSE, ...)
``` |

### Arguments

`n` |
number of samples to take |

`design` |
a design or list of designs. Each design must be either a matrix or a data.frame or coercible to a data.frame. |

`type` |
type of sampling passed to |

`formula` |
either a single model formula of a list of formulae |

`at` |
only used when type is |

`keepfun` |
optional; function operating on the columns of a matrix with the same number of columns as design which return a logical value for including a specific point in the sample or not. Useful for rejection sampling for nonstandard design regions. |

`sample` |
optional; if not missing it should contain a matrix or data.frame containing points sampled over the required design region. If it is not missing, no further sampling will be done: the SPV is simply evaluated at these points. |

`unscaled` |
logical indicating whether to use the unscaled prediction variance (UPV) instead of the scale prediction variance (SPV) |

`...` |
additional arguments passed to |

### Value

Object of class 'spv', 'spvlist', 'spvforlist' or 'spvlistforlist', depending on whether single designs/formulas are passed or lists of these.

### Author(s)

Pieter C. Schoonees

### References

Pieter C. Schoonees, Niel J. le Roux, Roelof L.J. Coetzer (2016). Flexible Graphical Assessment of
Experimental Designs in R: The vdg Package. *Journal of Statistical Software*, 74(3), 1-22.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.18637/jss.v074.i03")}.

### See Also

`plot.spv`

for more examples

### Examples

1 2 3 4 5 6 7 |

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