multinom                package:nnet                R Documentation

_F_i_t _M_u_l_t_i_n_o_m_i_a_l _L_o_g-_l_i_n_e_a_r _M_o_d_e_l_s

_D_e_s_c_r_i_p_t_i_o_n:

     Fits multinomial log-linear models via neural networks.

_U_s_a_g_e:

     multinom(formula, data=parent.frame(), weights, subset, na.action, 
     contrasts=NULL, Hess=FALSE, summ=0, censored=FALSE, ...)

_A_r_g_u_m_e_n_t_s:

 formula: a formula expression as for regression models, of the form 
          `response ~ predictors'. The response should be a factor or a
          matrix with K columns, which will be interpreted as counts
          for each of K classes. A log-linear model is fitted, with
          coefficients zero for the first class. An offset can be
          included: it should be a matrix with K columns if the
          response is a matrix with K columns or a factor with K > 2
          classes, or a vector for a factor with 2 levels. See the
          documentation of `formula' for other details. 

    data: an optional data frame in which to interpret the variables
          occurring in `formula'. 

 weights: optional case weights in fitting. 

  subset: expression saying which subset of the rows of the data should
           be used in the fit. All observations are included by
          default. 

na.action: a function to filter missing data. 

contrasts: a list of contrasts to be used for some or all of the
          factors appearing as variables in the model formula. 

    Hess: logical for whether the Hessian (the observed information
          matrix) should be returned. 

    summ: integer; if non-zero summarize by deleting duplicate rows and
          adjust weights. Methods 1 and 2 differ in speed (2 uses `C');
          method 3 also combines rows with the same X and different Y,
          which changes the baseline for the deviance. 

censored: If Y is a matrix with `K > 2' columns, interpret the entries
          as one for possible classes, zero for impossible classes,
          rather than as counts. 

     ...: additional arguments for `nnet' 

_V_a_l_u_e:

     A `nnet' object with additional structure.

deviance: the residual deviance. 

     edf: the (effective) number of degrees of freedom used by the
          model 

     AIC: the AIC for this fit. 

 Hessian: (if `Hess' is true). 

_S_e_e _A_l_s_o:

     `nnet'

_E_x_a_m_p_l_e_s:

     options(contrasts = c("contr.treatment", "contr.poly"))
     library(MASS)
     example(birthwt)
     bwt.mu <- multinom(low ~ ., bwt)
     bwt.mu
     Call:
     multinom(formula = low ~ ., data = bwt)

     Coefficients:
      (Intercept)         age         lwt raceblack raceother  
         0.823477 -0.03724311 -0.01565475  1.192371 0.7406606
          smoke      ptd        ht        ui       ftv1     ftv2+ 
       0.7555234 1.343648 1.913213 0.6802007 -0.4363238 0.1789888

     Residual Deviance: 195.4755 
     AIC: 217.4755 

