cemco (version 0.2)

EStep: Calculate the E step of the CemCO algorithm with covariates effects on distributions means.

Description

Implements the expectation step of EM algorithm for parameterized Gaussian mixture models with covariates effects on the distribution means. It is also used to calculate the a posteriori probability of each observation belong to each cluster.

Usage

EStep(data, Y, phi, G)

Value

Returns a n x G numeric matrix where n represents the number of observations (number of rows of data) and G (the number of clusters). The value i, j represents the probability of the i-th observation belong to j-th cluster.

Arguments

data

A numeric vector, matrix, or data frame of observations. Non-numerical values should be converted to integer or float (e.g. dummies). If matrix or data frame, rows and columns correspond to observations (n) and variables (P).

Y

numeric matrix of data to use as covariates. Non-numerical values should be converted to integer or float (e.g. dummies).

phi

list of fitted parameters in the same format as the output of the CemCO function

G

An integer specifying the numbers of mixture components (clusters)

Author

Relvas, C. & Fujita, A.

Details

Calculate the a posteriori probability of each observation belong to each cluster given the data and the current parameters estimation.

References

Stage I non-small cell lung cancer stratification by using a model-based clustering algorithm with covariates, Relvas et al.

Examples

set.seed(42)
X = cbind(rnorm(60), rnorm(60))
Y = cbind(rnorm(60), rnorm(60))
K = 2

fit <- CemCO(X, Y, K, max_iter=10, n_start=1, cores=1)
prob <- EStep(X, Y, fit[[1]], K)