loglk_ig {gmmsslm} | R Documentation |
Log likelihood for partially classified data with ingoring the missing mechanism
Description
Log likelihood for partially classified data with ingoring the missing mechanism
Usage
loglk_ig(dat, zm, pi, mu, sigma)
Arguments
dat |
An n×p matrix where each row represents an individual observation
|
zm |
An n-dimensional vector containing the class labels including the missing-label denoted as NA.
|
pi |
A g-dimensional vector for the initial values of the mixing proportions.
|
mu |
A p×g matrix for the initial values of the location parameters.
|
sigma |
A p×p covariance matrix,or a list of g covariance matrices with dimension p×p×g .
It is assumed to fit the model with a common covariance matrix if sigma is a p×p covariance matrix;
otherwise it is assumed to fit the model with unequal covariance matrices.
|
Details
The log-likelihood function for partially classified data with ingoring the missing mechanism can be expressed as
logLPC(ig)(θ)=∑j=1n[(1−mj)∑i=1gzij{logπi+logfi(yj;ωi)}+mjlog{∑i=1gπifi(yj;ωi)}],
where mj
is a missing label indicator, zij
is a zero-one indicator variable defining the known group of origin of each,
and fi(yj;ωi)
is a probability density function with parameters ωi
.
Value
[Package
gmmsslm version 1.1.5
Index]