uml                 package:mixstock                 R Documentation

_M_i_x_e_d _s_t_o_c_k _a_n_a_l_y_s_i_s _b_y _u_n_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 unconditional maximum likelihood estimate (jointly
     estimating marker frequencies in sources) of the contributions of
     different sources to a mixed stock, by either a direct-search or
     an expectation-maximization method

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

     uml(x, method="direct",optmethod="L-BFGS-B",...)
     uml.ds(x,grad=uml.grad,start.type="lsolve",fuzz=0,bounds=1e-4,ndepfac=1000,method="L-BFGS-B",debug=FALSE,control=NULL,
     transf=c("part","full","none"),...)
     uml.em(x,prec=1e-8,prior=1)

_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)

optmethod: to be passed to optim

    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'

  transf: transformation

   debug: produce debugging output?

 control: other control arguments to 'optim'

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

    prec: precision for determining convergence of EM algorithm

   prior: prior for EM algorithm

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

     'uml' uses either a direct-search algorithm or an EM algorithm to
     find the ML estimate

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

     an object of class 'mixstock.est', with elements 

     fit: information on the ML fit

resample: bootstrap information, if any

    data: original data used for estimate

       R: number of sources

       H: number of markers

  contin: estimation done on transformed proportions?

  method: optimization method

boot.method: resampling method

boot.data: raw resampling information

gandr.diag: Gelman-Rubin diagnostic information for MCMC estimates

   prior: Prior for MCMC estimates

      em: estimation done by EM algorithm?

_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)
     uml.est <- uml(x)
     uml.est
     uml.emest <- uml.em(x)
     uml.emest

