testSim         package:surveillance         R Documentation(latin1)

_P_r_i_n_t _x_t_a_b_l_e _f_o_r _a _S_i_m_u_l_a_t_e_d _D_i_s_e_a_s_e _a_n_d _t_h_e _S_u_m_m_a_r_y

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

     Just a test method.

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

         testSim(p = 0.99, r = 0.01, length = 400, A = 1, alpha = 1,
                 beta = 0, phi = 0, frequency = 1, state = NULL, K, 
                 range = 200:400)

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

       p: probability to get a new epidemy at time i if there was one
          at time i-1, default 0.99

       r: probability to get no new epidemy at time i if there was none
          at time i-1, default 0.01

  length: number of weeks to model, default 400

       A: amplitude (range of sinus), default = 1

   alpha: parameter to move along the y-axis (negative values not
          allowed) with alpha > = A, default = 1

    beta: regression coefficient, default = 0

     phi: factor to create seasonal moves (moves the curve along the
          x-axis), default = 0

frequency: factor to determine the oscillation-frequency, default = 1

   state: use a state chain to define the status at this timepoint
          (outbreak or not). If not given a Markov chain is generated
          by the programme, default NULL

       K: additional weigth for an outbreak which influences the
          distribution parameter mu, default = 0

   range: range of timepoints to be evaluated by the RKI 1 system,
          default 200:400.

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

     A pointSource epidemy is generated and sent to the RKI 1 system,
     the quality values for the result are computed and shown as a
     latex table. Additionally a plot of the result is generated.

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

  xtable: one printed latex table and a result plot

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

     M. Hoehle, A. Riebler, C. Lang

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

     'sim.pointSource', 'algo.call', 'algo.compare', 'plot.survRes',
     'compMatrix.writeTable'

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

         testSim(K = 2)
         testSim(r = 0.5, K = 5)

