# Create an Object of Class designfactors

### Description

Create an
object of class `designfactors`

,
from the factor names and their numbers of levels, or from
a named list of factor levels. Both ways can be used in the same
call. Additional information can be provided: they will be used during the
design search or in the summary functions applied to the object.

### Usage

1 2 3 |

### Arguments

`factors` |
a character vector of factor names, or possibly a scalar, a dataframe or a list (see DETAILS). |

`nlevels` |
a vector of level numbers for each factor name (see DETAILS). |

`block` |
an additive model formula to indicate the block factors. |

`ordered` |
an additive model formula to indicate the quantitative factors (not used at all in the present version). |

`hierarchy` |
a formula or a list of formulae to indicate hierarchy relationships between factors (see the planor vignette for details). |

`dummy` |
a logical to identify dummy factors created and deleted by planor functions for technical reasons. |

### Value

An object of class `designfactors`

.

### Note

The basic usage is to specify the names of the factors by a
character vector of length *n* in argument `factors`

and
their numbers of levels by a numeric vector of length *n* in
argument `nlevels`

. Alternatively, the `factors`

argument
can be an integer *n*, in which case the first *n* capital
letters of the alphabet are used as factor names. If `nlevels`

is a scalar *s*, it is considered that all factors have *s*
levels. There are two more possibilities which allow for alphanumeric
factor levels. If `factors`

is a dataframe, the factors in this
dataframe are extracted together with their levels. Finally
`factors`

can be a named list of *n* vectors, with each
vector containing the levels of one factor. Note that `nlevels`

is ignored in these latter two cases. See the examples. The argument
`block`

allows to specify the block or nuisance factors. This
information is used by the `alias`

and
`summary`

functions but it has no effect on the design
generation and randomization which depend on other arguments.

### Author(s)

Monod, H. and Bouvier, A.

### See Also

Class `designfactors`

### Examples

1 2 3 4 5 6 7 8 9 | ```
planor.factors(c("A","B","C","P"),c(2,3,6,3))
planor.factors(LETTERS[1:12],2)
planor.factors(12,2)
planor.factors( c("A","B","Block"), 3, block=~Block)
zz <- planor.factors( c("A","B","Block"), c(2,3,5))
zz@levels$A <- c("plus","moins")
planor.factors(factors=list(A=c("plus","moins"), B=1:3, Block=1:5))
AB <- data.frame( A=c(rep(c("a","b"),3)), B=rep(c("z","zz","zzz"),rep(2,3)), C=1:6)
planor.factors(factors=AB)
``` |

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