EStep | R Documentation |
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.
EStep(data, Y, phi, G)
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) |
Calculate the a posteriori probability of each observation belong to each cluster given the data and the current parameters estimation.
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.
Relvas, C. & Fujita, A.
Stage I non-small cell lung cancer stratification by using a model-based clustering algorithm with covariates, Relvas et al.
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)
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