priorControl             package:mclust             R Documentation

_C_o_n_j_u_g_a_t_e _P_r_i_o_r _f_o_r _G_a_u_s_s_i_a_n _M_i_x_t_u_r_e_s.

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

     Specify a conjugate prior for Gaussian mixtures.

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

     priorControl(functionName = "defaultPrior", ...) 

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

functionName: The name of the function specifying the conjugate prior.
          The default function is 'defaultPrior', which can be used a
          template for alternative specification.   

     ...: Optional named arguments to the function specified in
          'functionName' together with their values. 

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

     'priorControl' is used to specify a conjugate prior   for EM
     within _MCLUST_.

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

     A list with the function name as the first component. The
     remaining components (if any) consist of a list of arguments to
     the function with assigned values.

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

     C. Fraley and A. E. Raftery (2002). Model-based clustering,
     discriminant analysis, and density estimation. _Journal of the
     American Statistical Association 97:611-631_. 

     C. Fraley and A. E. Raftery (2005). Bayesian regularization for
     normal mixture estimation and model-based clustering. Technical
     Report, Department of Statistics, University of Washington.

     C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal
     Mixture Modeling and Model-Based Clustering,  Technical Report no.
     504, Department of Statistics, University of Washington.

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

     'mclustBIC', 'me', 'mstep', 'defaultPrior'

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

     # default prior
     irisBIC <- mclustBIC(iris[,-5], prior = priorControl())
     summary(irisBIC, iris[,-5])

     # no prior on the mean; default prior on variance
     irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0))
     summary(irisBIC, iris[,-5])

