

multinom(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=sys.parent(), 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 coef-
          ficients 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 fac-
          tor with 2 levels.  See the documentation of `for-
          mula' 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 dupli-
          cate rows and adjust weights.  Methods 1 and 2
          differ in speed (2 uses `C'); method 3 also com-
          bines 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

