pgam                  package:pgam                  R Documentation

_P_o_i_s_s_o_n-_G_a_m_m_a _A_d_d_i_t_i_v_e _M_o_d_e_l_s

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

     Fit Poisson-Gamma Additive Models using the roughness penalty
     approach

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

     pgam(formula, dataset, omega = 0.8, beta = 0.1, offset = 1, digits = getOption("digits"),
     maxit = 100, eps = 1e-06, lfn.scale=1, control = list(), optim.method = "L-BFGS-B", partial.resid = "response",
     smoother = "spline", bkf.eps = 0.001, bkf.maxit = 100, se.estimation = "numerical", verbose = TRUE)

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

 formula: a model formula. See 'pgam.parser' for details

 dataset: a data set in the environment search path

   omega: initial value for the discount factor

    beta: vector of initial values for covariates coefficients. If a
          sigle value is supplied it is replicated to fill in the whole
          vector

  offset: default is 1. Other value can be supplied here

  digits: number of decimal places for printing information out

   maxit: convergence control iterations

     eps: convergence control criterion

lfn.scale: scales the likelihood function and is passed to 'control' in
          'optim'. Value must be positive to ensure maximization

 control: convergence control of 'optim'. See its help for details

optim.method: optimization method passed to 'optim'. Different methods
          can lead to different results, so the user must attempt to
          the trade off between speed and robustness. For example,
          'BFGS' is faster but sensitive to starting values and
          'L-BFGS-B' is more robust but slower. See its help for
          details.

partial.resid: type of partial residual to be used if semiparametric
          fitting. See 'residuals.pgam' for details

smoother: smoother to be used in backfitting. See 'pgam.smooth' for
          details

 bkf.eps: convergence control criterion for the backfitting algorithm

bkf.maxit: convergence control iterations for the backfitting algorithm

se.estimation: if 'numerical' numerical standard error of parameters
          are returned. If 'analytical' then analytical extraction of
          the standard errors is performed. By setting it to 'none'
          standard error estimation is avoided

 verbose: if 'TRUE' information during estimation process is printed
          out

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

     There are a lot of details to be written. It will be very soon.

     Specific information can be obtained on functions help.

     This algorithm fits fully parametric Poisson-Gamma model also.

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

     List containing an object of class 'pgam'.

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

     Washington Leite Junger 
      wjunger@ims.uerj.br

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

     Junger, W. L. (2004) Modelo Poisson-Gama Semi-Paramtrico: Uma
     Abordagem de Penalizao por Rugosidade. MSc Thesis. Rio de
     Janeiro, PUC-Rio, Departamento de Engenharia Eltrica

     Harvey, A. C., Fernandes, C. (1989) Time series models for count
     data or qualitative observations. Journal of Business and Economic
     Statistics, 7(4):407-417

     Campos, E. L., De Leon, A. C. M. P., Fernandes, C. A. C. (2003)
     Modelo Poisson-Gama para Sries Temporais de Dados de Contagem -
     Teoria e Aplicaes. 10a ESTE - Escola de Sries Temporais e
     Econometria

     Green, P. J., Silverman, B. W. (1994) Nonparametric Regression and
     Generalized Linear Models: a roughness penalty approach. Chapman
     and Hall, London

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

     'predict.pgam', 'pgam.parser', 'residuals.pgam', 'backfitting'

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

     library(pgam)
     data(aihrio)
     attach(aihrio)
     form <- ITRESP5~f(WEEK)+HOLIDAYS+rain+PM+g(tmpmax,7)+g(wet,3)
     m <- pgam(form,aihrio,omega=.8,beta=.01,maxit=1e2,eps=1e-4,optim.method="BFGS",partial.resid="response")

     summary(m)

