mlogit              package:globaltest              R Documentation

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

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

     Fits a multinomial logistic regression model to a nominal scale
     outcome.

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

     mlogit(formula, data, control = glm.control())

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

 formula: An object of class 'formula' containing a symbolic
          description of the model to be fit. See the documentation of
          'formula' for details.

    data: An optional data frame containing the variables in the model.
          If not found in 'data', the variables are taken from the
          environment from which 'mlogit' is called.

 control: A list of parameters for controlling the fitting process. See
          the documentation of 'glm.control' for details.

_D_e_t_a_i_l_s:

     The function mlogit fits a multinomial logistic regression model
     for a multi-valued outcome with nominal scale. The implementation
     and behaviour are designed to mimic those of 'glm', but the
     options are (as yet) more limited. Missing values are not allowed
     in the data.

     The model is fitted without using a reference outcome category;
     the parameters are made identifiable by the requirement that the
     sum of corresponding regression coefficients over the outcome
     categories is zero.

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

     An object of (S4) class 'mlogit'. The class has slots:
     coefficients (matrix), standard.err (matrix), fitted.values
     (matrix), x (matrix), y (matrix), formula (formula), call (call),
     df.null (numeric), df.residual (numeric), null.deviance (numeric),
     deviance (numeric), iter (numeric), converged (logical).

     Methods implemented for the 'mlogit' class are 'coefficients',
     'fitted.values', 'residuals' and which extract the relevant
     quantities, and 'summary', which gives the same output as with a
     'glm' object.

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

     Jelle Goeman: j.j.goeman@lumc.nl; Jan Oosting

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

     'glm', 'multinom'.

_E_x_a_m_p_l_e_s:

       y <- factor(rep(1:4, 5))
       x <- 1:20
       fit <- mlogit(y ~ x)
       summary(fit)
       residuals(fit)

