nbkal                package:repeated                R Documentation

_N_e_g_a_t_i_v_e _B_i_n_o_m_i_a_l _M_o_d_e_l_s _w_i_t_h _K_a_l_m_a_n _U_p_d_a_t_e

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

     'nbkal' fits a negative binomial regression with Kalman update
     over time. The variance is proportional to the mean function,
     whereas, for 'kalcount' with exponential intensity, it is a
     quadratic function of the mean.

     Marginal and individual profiles can be plotted using 'mprofile'
     and 'iprofile' and residuals with 'plot.residuals'.

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

     nbkal(response, times, mu, preg, pdepend, kalman=TRUE,
             print.level=0, ndigit=10, gradtol=0.00001, steptol=0.00001,
             fscale=1, iterlim=100, typsiz=abs(p), stepmax=10*sqrt(p%*%p))

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

response: A list of two column matrices with counts and corresponding
          times for each individual, one matrix or dataframe of counts,
          or an object of class, response (created by 'restovec') or
          repeated (created by 'rmna' or 'lvna').

   times: When response is a matrix, a vector of possibly unequally
          spaced times when they are the same for all individuals or a
          matrix of times. Not necessary if equally spaced. Ignored if
          response has class, response or repeated.

      mu: The mean function.

    preg: The initial parameter estimates for the mean function.

 pdepend: The estimates for the dependence parameters, either one or
          three.

  kalman: If TRUE, fits the kalman update model, otherwise, a standard
          negative binomial distribution.

  others: Arguments controlling 'nlm'.

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

     A list of classes 'nbkal' and 'recursive' is returned.

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

     P. Lambert and J.K. Lindsey

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

     Lambert, P. (1996) Applied Statistics 45, 31-38.

     Lambert, P. (1996) Biometrics 52, 50-55.

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

     'gar', 'gnlmm', 'gnlr', 'iprofile' 'kalcount', 'mprofile',
     'read.list', 'rmna', 'restovec', 'tcctomat', 'tvctomat'.

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

     y <- matrix(rnbinom(20,5,0.5), ncol=5)
     times <- matrix(rep(seq(10,50,by=10),4), ncol=5, byrow=TRUE)
     y0 <- matrix(rep(rnbinom(5,5,0.5),4), ncol=5, byrow=TRUE)
     mu <- function(p) p[1]*log(y0)+(times<30)*p[2]*
             (times-30)+(times>30)*p[3]*(times-30)
     nbkal(y, preg=c(1.3,0.008,-0.05), times=times, pdep=1.2, mu=mu)

