mlogreg: Multinomial Logic Regression
In holgerschw/logicFS: Identification of SNP Interactions

Description Usage Arguments Value Author(s) References See Also

Performs a multinomial logic regression for a nominal response by fitting a logic regression model (with logit as link function) for each of the levels of the response except for the level with the smallest value which is used as reference category.

## S3 method for class 'formula'
mlogreg(formula, data, recdom = TRUE, ...)

## Default S3 method:
mlogreg(x, y, ntrees = 1, nleaves = 8, anneal.control = logreg.anneal.control(), 
    select = 1, rand = NA, ...)

`formula`	an object of class `formula` describing the model that should be fitted.
`data`	a data frame containing the variables in the model. Each column of `data` must correspond to a binary variable (coded by 0 and 1) or a factor (for details on factors, see `recdom`) except for the column comprising the response, and each row to an observation. The response must be a categorical variable with less than 10 levels. This response can be either a factor or of type `numeric` or `character`.
`recdom`	a logical value or vector of length `ncol(data)` comprising whether a SNP should be transformed into two binary dummy variables coding for a recessive and a dominant effect. If `TRUE` (logical value), then all factors (variables) with three levels will be coded by two dummy variables as described in `make.snp.dummy`. Each level of each of the other factors (also factors specifying a SNP that shows only two genotypes) is coded by one indicator variable. If `FALSE` (logical value), each level of each factor is coded by an indicator variable. If `recdom` is a logical vector, all factors corresponding to an entry in `recdom` that is `TRUE` are assumed to be SNPs and transformed into the two binary variables described above. Each variable that corresponds to an entry of `recdom` that is `TRUE` (no matter whether `recdom` is a vector or a value) must be coded by the integers 1 (coding for the homozygous reference genotype), 2 (heterozygous), and 3 (homozygous variant).
`x`	a matrix consisting of 0's and 1's. Each column must correspond to a binary variable and each row to an observation.
`y`	either a factor or a numeric or character vector specifying the values of the response. The length of `y` must be equal to the number of rows of `x`.
`ntrees`	an integer indicating how many trees should be used in the logic regression models. For details, see `logreg` in the `LogicReg package`.
`nleaves`	a numeric value specifying the maximum number of leaves used in all trees combined. See the help page of the function `logreg` in the `LogicReg` package for details.
`anneal.control`	a list containing the parameters for simulated annealing. For details, see the help page of `logreg.anneal.control` in the `LogicReg` package.
`select`	numeric value. Either 0 for a stepwise greedy selection (corresponds to `select = 6` in `logreg`) or 1 for simulated annealing.
`rand`	numeric value. If specified, the random number generator will be set into a reproducible state.
`...`	for the `formula` method, optional parameters to be passed to the low level function `mlogreg.default`. Otherwise, ignored.

An object of class mlogreg composed of

`model`	a list containing the logic regression models,
`data`	a matrix containing the binary predictors,
`cl`	a vector comprising the class labels,
`ntrees`	a numeric value naming the maximum number of trees used in the logic regressions,
`nleaves`	a numeric value comprising the maximum number of leaves used in the logic regressions,
`fast`	a logical value specifying whether the faster search algorithm, i.e.\ the greedy search, has been used.