mdprefmx {lazy.mdpref}R Documentation

Finite Mixture Model for MDPREF

Description

Finite Mixture Model for MDPREF

Usage

mdprefmx(f, n, ij, subj, ndim = 2, nclass = 2, alpha = 1, init = 2,
  X = NULL, B = NULL, rho = NULL, estX = 1, estB = 1, estrho = 1,
  minp = 1e-07, lmax = 10, eps = 1e-06, lmax2 = 3, print = 1,
  plot = 0)

Arguments

f

vector consisting of the # of times that the left stimuli was chosen out of n trials.

n

vector consisting of the # of trials.

ij

matrix indicating the stimulus pair.

subj

vector indicating the subject.

ndim

# of dimensions

nclass

# of laten classes

alpha

hyper-parameter vector of size nclass for Dirichlet prior of rho

init

method for initial configuration
= 0 to use random number
= 1 to use Thurstome Case V, PCA and hclust = 2 to use mdprefml, PCA and hclust

X

initial configuration, if any

B

initial configuration, if any

rho

initial configuration, if any

estX

= 0 to skip the estimation of X and fix it to the value given in X.

estB

= 0 to skip the estimation of B and fix it to the value given in B.

estrho

= 0 to skip the estimation of rho and fix it to the value given in rho.

minp

minimum value of the probability

lmax

max # of iterations

eps

criterion for convergence

lmax2

max # of iterations for the inner iteration for X and Beta

print

= 1 to print the result

plot

= 1 to plot the result

Details

The quartet ( subj, n, f, ij ) contains the result of the paired comparison data.
Thoese four objects has the same length or # of rows.
The k-th elements of the quartet indicates that
subj[k] preferred stimulus ij[k,1] over ij[k,2] f[k] times when exposed to the pair n[k] times.
The paired comparizon for each subject does no have to be complete.

Examples

ij <- cbind( data_comp$ij.1, data_comp$ij.2)
mdprefmx( f=data_comp$f, n=data_comp$n, ij=ij, subj=data_comp$subj, plot=1 )

ij <- cbind( data_miss$ij.1, data_miss$ij.2)
mdprefmx( f=data_miss$f, n=data_miss$n, ij=ij, subj=data_miss$subj, plot=1 )

[Package lazy.mdpref version 0.1.2 Index]