mlogreg               package:logicFS               R Documentation

_M_u_l_t_i_n_o_m_i_a_l _L_o_g_i_c _R_e_g_r_e_s_s_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     ## 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(), 
         rand = NA, ...)

_A_r_g_u_m_e_n_t_s:

 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

    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

_V_a_l_u_e:

     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

_A_u_t_h_o_r(_s):

     Holger Schwender, holger.schwender@udo.edu

_R_e_f_e_r_e_n_c_e_s:

     Holger Schwender (2007). Measuring the Importances of Genotypes
     and Sets of Single Nucleotide Polymorphisms. Technical Report, SFB
     475, Department of Statistics, University of Dortmund. Appears
     soon.

_S_e_e _A_l_s_o:

     'predict.mlogreg', 'logic.bagging', 'logicFS'

