FLXSA                 package:FLXSA                 R Documentation

_C_r_e_a_t_e _a _n_e_w _F_L_X_S_A _o_b_j_e_c_t -_r_u_n _a_n _X_S_A _a_n_a_l_y_s_i_s-

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

     This function runs an XSA (extended survivor analysis) and creates
     an FLXSA object used to analyse its results.

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

      FLXSA(stock, indices, control = FLXSA.control(), desc, diag.flag=TRUE)

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

   stock: An FLStock object to be used for the analysis 

 indices: An FLIndices object holding the indices of abundance to
          consider in the model 

 control: An 'FLXSA.control' object giving parameters of the model (see
          'FLXSA.control') 

    desc: A short description of this analysis 

diag.flag: If TRUE returns all diagnostics, if FALSEonly returns
          stock.n, harvest and control

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

     Virtual population analysis and cohort analysis are essentially
     accountancy methods whereby a stock's historical population
     structure may be reconstructed from total catch data given a
     particular level of natural mortality. Firstly, however, numbers
     at age in the last year and age have to be found since both
     methods iterate backwards down a cohort. The main problem in many
     sequential age based assessment methods is therefore to estimate
     these terminal population numbers. In XSA these are found from the
     relationship between catch per unit effort (CPUE), abundance and
     year class strength.

     Estimates of the catchability for the oldest age in an assessment,
     tuned by the ad hoc or XSA procedures, are directly dependent on
     the terminal population or F values used to initialise the
     underlying VPA. Catchability at the oldest age is therefore
     under-determined and cannot be utilised without additional
     information. Within the ad hoc tuning procedures the additional
     information is obtained by making the assumption that the
     exploitation pattern on the oldest ages was constant during the
     assessment time series. F on the oldest age in the final year is
     estimated as a proportion of an average of the F for preceding
     ages in the same year. XSA uses an alternative approach by making
     the assumption that fleet catchability is constant (independent of
     age) above a certain age. The age (constant for all fleets) is
     user-defined. For each fleet, the catchability value estimated at
     the specified age, is used to derive population abundance
     estimates for all subsequent ages in the fleet data set.

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

     An 'FLXSA' object is returned, whith slots: 

 n      : An FLQuant with the number of individuals at age

 f      : An FLQuant with the fishing mortality

 swt    : An FLQuant with the stock weight

 mat    : An FLQuant with the maturity indices

 qres   : A list with residuals for q

 cpue   : A list with the various cpues

 wts    : A list with the various weights

 control: The 'FLXSA.control' object that was used for this analysis

 call   : A copy of the call to run this analysis

 desc   : A description of the analysis

_N_o_t_e:

     See 'update' to learn how to update stock data according to an XSA
     analysis

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

     Laurence Kell and Philippe Grosjean

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

     Darby, C. D., and Flatman, S. 1994. Virtual Population Analysis:
     version 3.1 (Windows/Dos) user guide. Info. Tech. Ser., MAFF
     Direct. Fish. Res., Lowestoft, (1): 85pp.

     Shepherd, J.G. 1992. Extended survivors analysis: an improved
     method for the analysis of catch-at-age data and
     catch-per-unit-effort data. Working paper No. 11 ICES
     Multi-species Assessment Working Group, June 1992, Copenhagen,
     Denmark. 22pp. (mimeo).

     Shepherd, J.G. 1994. Prediction of yearclass strength by
     calibration regression analysis of multiple recruit index series.
     ICES J. Mar. Sci. In Prep.

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

     'FLXSA.control', 'FLStock-class'

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

     #TO DO...

