cml                 package:mixstock                 R Documentation

_M_i_x_e_d _s_t_o_c_k _a_n_a_l_y_s_i_s _b_y _c_o_n_d_i_t_i_o_n_a_l _m_a_x_i_m_u_m _l_i_k_e_l_i_h_o_o_d

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

     Find the conditional maximum likelihood estimate (assuming marker
     frequencies in the sources are exactly equal to the sample
     frequencies) of the contributions of different sources to a mixed
     stock

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

     cml(x, start.type="lsolve", fuzz=0, bounds=1e-4,
     ndepfac=1000,method="L-BFGS-B",lower=NULL,upper=NULL,
     ndeps=NULL,
     control=NULL,debug=FALSE,transf="part",grad=cml.grad,...)

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

       x: a list with elements 'mixsamp' (a vector of the sampled
          markers in the mixed stock) and 'sourcesamp' (a matrix, with
          markers in rows and sources in columns, of markers in the
          source samples)

    grad: function giving the gradient of the likelhood

start.type: starting values to use: 'equal' (equal contributions from
          each source); 'random' (multinomial sample with equal
          probabilities); 'rand2' (sample out of a transformed normal
          distribution); a number between 1 and the number of sources;
          that source starts with 0.95 contribution and the rest start
          with 0.05/(R-1); default 'lsolve', the linear solution to the
          problem

    fuzz: min. value (1-min is the max.) for starting contributions

  bounds: (bounds,1-bounds) are the lower and upper bounds for mle
          calculations

 ndepfac: factor for computing numerical derivatives; numerical
          derivative stepsize is computed as bounds/ndepfac [OBSOLETE
          with gradient function?]

  method: optimization method, to be passed to 'optim'

   debug: produce debugging output?

   lower: lower bound

   upper: upper bound

   ndeps: scaling factor for optimization

 control: other control arguments to 'optim'

  transf: (character) "full": use arctan transform to transform
          (-Inf,Inf) to (0,1) or vice versa; "part": don't; "none"; no
          transform

     ...: other arguments to 'mle' or 'optim' (e.g. 'hessian=FALSE' to
          suppress (slow) hessian calculation, etc.)

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

     By default, uses 'mle' which in turn uses 'optim' with
     'method="L-BFGS-B"' to do bounded optimization

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

     an object of class 'mixstock.est', containing the results of the
     fit

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

     Ben Bolker

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

     true.freq <- matrix(c(0.65,0.33,0.01,0.01,
                           0.33,0.65,0.01,0.01),ncol=2)
     true.contrib <- c(0.9,0.1)
     x <- simmixstock0(true.freq,true.contrib,50,100,1004)
     cml.est <- cml(x)
     cml.est

