Description Usage Arguments Details Value Note Author(s) See Also Examples

Search for a design key or a collection of design keys that satisfy the design properties specified by the arguments. This function implements the core algorithms of the planor package.

1 2 3 |

`factors` |
an object of class |

`nlevels` |
see |

`block` |
see |

`ordered` |
see |

`hierarchy` |
see |

`model` |
a list of model-estimate pairs of formulae, typically an
output from |

`estimate` |
see |

`listofmodels` |
see |

`resolution` |
see |

`nunits` |
a scalar giving the total number of units in the design |

`base` |
an optional additive formula to specify the basic factors. See Note. |

`max.sol` |
maximum number of solutions before exit. |

`randomsearch` |
a |

`verbose` |
a |

The methods implemented in planor rely on a decomposition
of the design search according to prime numbers. The prime numbers
involved are those that decompose the numbers of levels of the
factors. For example, if all factors have 2, 4, or 8 levels, then the
number of units must be a power of 2 and the only prime number
involved is 2. This is called the *symmetric* case. But if at
least one factor has 6 levels, or if factor *A* has 2 levels and
factor *B* has 3 levels, then the number of units must be the
product of a power of 2 by a power of 3. In this case the search is
automatically decomposed into one for prime 2 and one for prime
3. This is called the *asymmetric* case.

In the symmetric case with prime *p*, a regular factorial design
requires a single key matrix of integers modulo *p*. In the
asymmetric case, it requires one key matrix per prime. In
planor, key matrices are stored in objects of class
`keymatrix`

. The lists made of one key matrix per
prime are called design keys. They are stored in objects of class
`designkey`

.

The function `planor.designkey`

essentially searches for
design keys that satisfy the user specifications. For technical
reasons, however, its output can take two different forms: either an
object of class `listofkeyrings`

or an object of
class `listofdesignkeys`

. The function
`planor.designkey`

detects automatically which case
applies. In the first case (*independent case*), the key matrix
solutions can be searched independently between primes and they are
stored in objects of class `listofkeyrings`

. The
second case (*recursive case*) occurs exceptionnally. In that
case the search cannot be independent between primes and so the
different solutions are directly stored in a list of class
`listofdesignkeys`

.

An object of class `listofkeyrings`

in most
cases. Otherwise, i.e in recursive cases, an object of class
`listofdesignkeys`

.

The `nunits`

argument is compulsory except if the `base`

argument is used. When both arguments are missing, the program stops and
it gives the size that would be required by a full factorial
design. When only `nunits`

is missing, the number of units is
given by the product of the numbers of levels of the base
factors.

The `base`

formula must be an additive formula involving a
subset of factors, called the basic factors. Using the `base`

argument ensures that the design solutions will include the full
factorial design for the basic factors. This option can speed up
the search because it restricts the domain to be explored by the search
algorithm.

Monod, H. and Bouvier, A.

`planor.factors`

, `planor.model`

, and
the classes `designfactors`

,
`listofkeyrings`

,
`listofdesignkeys`

1 2 3 4 5 6 |

```
Loaded planor 1.4.1
Preliminary step 1 : processing the model specifications
Preliminary step 2 : performing prime decompositions on the factors
*** Main step for prime p = 3 : key-matrix search
=> search for columns 4 to 5
first visit to column 4
first visit to column 5
--- col. 5 ( j = 2) 8 selected candidates
The search is closed: max.sol = 2 solution(s) found
Preliminary step 1 : processing the model specifications
Preliminary step 2 : performing prime decompositions on the factors
*** Main step for prime p = 2 : key-matrix search
=> search for columns 2 to 5
first visit to column 2
--- col. 2 ( j = 1) 14 initial candidates
first visit to column 3
--- col. 3 ( j = 2) 14 initial candidates
--- col. 3 ( j = 2) 13 selected candidates
first visit to column 4
--- col. 4 ( j = 3) 13 initial candidates
--- col. 4 ( j = 3) 12 selected candidates
first visit to column 5
--- col. 5 ( j = 4) 12 initial candidates
--- col. 5 ( j = 4) 11 selected candidates
The search is closed: max.sol = 1 solution(s) found
```

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