Convenient helper function, which creates an initial sample - either based on random (uniform) sampling or using latin hypercube sampling.

1 | ```
createInitialSample(n.obs, dim, control)
``` |

`n.obs` |
[ |

`dim` |
[ |

`control` |
[ |

Per default, this function will produce `n.obs`

observations of size
`dim`

in the range from 0 to 1. If you want to create a more specific
initial sample, the following control arguments might be helpful:

`init_sample.type`

: Should the initial sample be created based on random uniform sampling (`"random"`

) or on a latin hypercube sample (`"lhs"`

)? The default is`"random"`

.`init_sample.lower`

: The lower bounds of the initial sample. Either a vector of size`dim`

or a scalar (if all lower bounds are identical). The default is`0`

.`init_sample.upper`

: The upper bounds of the initial sample. Either a vector of size`dim`

or a scalar (if all upper bounds are identical). The default is`1`

.

[`matrix`

].

A matrix, consisting of `n.obs`

rows of `dim`

-dimensional
observations.

1 2 3 4 5 6 7 8 9 10 | ```
# (1) create a simple initial sample:
X = createInitialSample(300, 5)
summary(X)
# (2) create a more specific initial sample:
ctrl = list(init_sample.type = "lhs",
init_sample.lower = c(-5, 2, 0),
init_sample.upper = 10)
X = createInitialSample(200, 3, control = ctrl)
summary(X)
``` |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.