lmp                 package:normalp                 R Documentation

_F_i_t_t_e_d _l_i_n_e_a_r _m_o_d_e_l _w_i_t_h _e_x_p_o_n_e_n_t_i_a_l _p_o_w_e_r _d_i_s_t_r_i_b_u_t_i_o_n _e_r_r_o_r_s

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

     The function 'lmp' is used to fit linear model. It can be used
     when the errors are distributed as an exponential power
     distribution.

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

     lmp(formula, data, p)

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

 formula: A symbolic description of the model to be fitted.

    data: An optional data frame containing the variables in the model.
          By default the variables are taken from the environment.

       p: The shape parameter. If specified, this function estimates
          the parameter by using the Lp-norm method.

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

     To evaluate the coefficients of the linear model, 'lmp' uses the
     maximum likelihood estimators. This function can give some
     problems if the number of regressors is too high.

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

     The function 'lmp' returns an object of 'class' '"lmp"' and
     '"lm"'. The function 'summary' print a summary of the results. The
     generic accessor functions 'coefficients, effects, fitted.values'
     and 'residuals' extract various useful features of the value
     returned by 'lmp'. An object of 'class' '"lmp"' is a list
     containing at least the following components: 

coefficients: A named vector of coefficients.

residuals: The residuals, that is responses minus fitted values.

fitted.values: The fitted values.

    rank: The numeric rank of the fitted linear model.

df.residual: The residual degrees of freedom computed as in 'lm'.

    call: The matched call.

   terms: The 'terms' object used.

       p: Estimate of the shape parameter computed on residuals.

     knp: A logical parameter used by 'summary'.

   model: The model frame used.

    iter: If its value is 1 we have had a difficult convergence.

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

     Angelo M. Mineo

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

     Mineo, A.M. (1995) _Stima dei parametri di regressione lineare
     semplice quando gli errori  seguono una distribuzione normale di
     ordine p (p incognito)_. Annali della Facolt\`a di Economia
     dell'Universit\`a di Palermo (Area Statistico-Matematica), pp.
     161-186.

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

     e<-rnormp(n=100,mu=0,sigmap=4,p=3,method="d")
     x<-runif(100)
     y<-0.5+2*x+e
     lmp(y~x)

