simHHH         package:surveillance         R Documentation(latin1)

_S_i_m_u_l_a_t_e_s _d_a_t_a _b_a_s_e_d _o_n _t_h_e _m_o_d_e_l _p_r_o_p_o_s_e_d _b_y _H_e_l_d _e_t. _a_l (_2_0_0_5)

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

     Simulates a multivariate time series of counts based on the
     Poisson/Negative Binomial model as described in Held et al.
     (2005).

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

     ## Default S3 method:
     simHHH(model=NULL, control = list(coefs = list(alpha=1, gamma = 0, delta = 0,
            lambda = 0, phi = NULL, psi = NULL, period = 52),
            neighbourhood = NULL, population = NULL, start = NULL),
            length)

     ## S3 method for class 'ah':
     simHHH(model, control = model$control, length)

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

 control: list with

          _c_o_e_f_s list with the following parameters of the model - if
               not specified, those parameters are omitted

               _a_l_p_h_a vector of length 'm' with intercepts for 'm' units
                    or geographic areas respectively

               _g_a_m_m_a vector with parameters for the "sine" part of
                    nu_{i,t} 

               _d_e_l_t_a vector with parameters for the "cosine" part of
                    nu_{i,t} 

               _l_a_m_b_d_a autoregressive parameter

               _p_h_i autoregressive parameter for adjacent units

               _p_s_i overdispersion parameter of the negative binomial
                    model; 'NULL' corresponds to a Poisson model

               _p_e_r_i_o_d period of the seasonal component, defaults to 52
                    for weekly data

          _n_e_i_g_h_b_o_u_r_h_o_o_d neighbourhood matrix of size m times m with
               element 1 if two units are adjacent; the default 'NULL'
               assumes that there are no neighbours

          _p_o_p_u_l_a_t_i_o_n matrix with population proportions; the default
               'NULL' sets n_{i,t}=1 

          _s_t_a_r_t if 'NULL', the means of the endemic part in the 'm'
               units is used  as initial values y_{i,0} 

   model: Result of a model fit with 'algo.hhh', the estimated
          parameters are used to simulate data

  length: number of time points to simulate  

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

     Simulates data from a Poisson or a Negative Binomial model with
     mean

 mu_{it} = lambda y_{i,t-1} + phi sum_{j sim i} y_{j,t-1} + n_{it} nu_{it}

     where

 log nu_{it} = alpha_i + sum_{s=1}^{S}(gamma_s sin(omega_s t) + delta_s cos(omega_s t))

     omega_s = 2spi/'period' are Fourier frequencies and n_{it} are
     possibly standardized population sizes.

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

     Returns a list with elements 

    data: 'disProgObj' of simulated data 

    mean: matrix with mean mu_{i,t} that was used to simulate the data

 endemic: matrix with only the endemic part nu_{i,t} 

   coefs: list with parameters of the model

_N_o_t_e:

     The model does not contain a linear trend.

_S_o_u_r_c_e:

     Held, L., Hoehle, M., Hofmann, M. (2005). A statistical framework
     for the analysis of multivariate infectious disease surveillance
     counts. Statistical Modelling, 5, p. 187-199.

