OC2c-class        package:AcceptanceSampling        R Documentation

_C_l_a_s_s _F_a_m_i_l_y "_O_C_2_c".

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

     The family '"OC2c"' ("Operating Characteristic" function) of
     classes provides methods for creating, plotting, printing and
     assessing single, double, and multiple acceptance sampling plans
     based on the Binomial ('"OCbinomial"'), Hypergeometric
     ('"OChypergeom"'), and Poisson ('"OCpoisson"') distributions.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     The '"OC2c"' class is a virtual Class: No objects may be created
     from it.

     However, objects from the derived classes can be created by calls
     of the form 'new("OCbinomial", ...)', for example, or preferably
     using the creator function 'OC2c'.

_S_l_o_t_s:

     '_n': Object of class '"numeric"'. A vector of length k giving the
          sample size at each of the k stages of sampling, e.g. for
          double sampling k=2. 

     '_c': Object of class '"numeric"'.  A vector of length k giving the
          *cumulative* acceptance numbers at each of the k stages of
          sampling.

     '_r': Object of class '"numeric"'.   A vector of length k giving
          the *cumulative* rejection numbers at each of the k stages of
          sampling.

     '_t_y_p_e': Object of class '"character"'. The possible types relate
          to the distribution on which the plans are based on, namely,
          'binomial', 'hypergeom', and 'poisson' 

     '_p_d': Object of class '"numeric"'. A numeric vector indicating the
          quality for which a probability of acceptance is calculated
          under the specified sampling plan. Meaning differs for the
          different 'types'.

          For '"OCbinomial"' this relates to the proportion of
          defectives created by the process.

          For '"OChypergeom"' this relates to the proportion of
          population defectives created by the process.

          For '"OCpoisson"' this relates to the rate of defects (per
          item) created by the process.  

     '_N': Object of class '"numeric"'. Only for class '"OChypergeom"',
          a number giving the population (lot) size from which the
          sample is drawn.

     '_p_a_c_c_e_p_t': Object of class '"numeric"'. A numeric vector with the
          probability of acceptance which correspond to the quality as
          given by 'pd'.

_M_e_t_h_o_d_s:

_p_l_o_t 'signature(x="OCbinomial", y="missing")',
      'signature(x="numeric", y="OCbinomial")',
      'signature(x="OChypergeom", y="missing")',
      'signature(x="numeric", y="OChypergeom")',
      'signature(x="OCpoisson", y="missing")' or
      'signature(x="numeric", y="OCpoisson")': Plot the OC curve.

_s_h_o_w 'signature("OC2c")' or 'signature("OChypergeom")': Show the
     details of the sampling plan.

_s_u_m_m_a_r_y 'signature("OC2c")' or 'signature("OChypergeom")': Summarise
     the sampling plan. Optional argument 'full' (defaults to 'FALSE')
     will show the details at all quality values ('pd') supplied when
     the object was created.

_a_s_s_e_s_s 'signature(object="OC2c")': Assess whether the sampling plan can
     meet the specified _Producer Risk Point (PRP)_ and/or _Consumer
     Risk Point (CRP)_. For details see 'assess,OC2c-method'

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

     Andreas Kiermeier

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

     Hald, A. (1981), _Statistical theory of sampling inspection by
     attributes_, Academic Press.

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

     'OC2c'

