mstep_cond: Conditional maximisation step for Expectation-Maximisation...
In markajoc/MBCbigP: Model-Based Clustering for High-Dimensional Data

Description Usage Arguments Value

Conditional maximisation step for Expectation-Maximisation algorithm. Maximising paramters for current batch of data conditional on the parameters from the previous batch.

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mstep_cond(x_A, x_B, mean_A, sigma_AA, sigma_AB = NULL, mean_B = NULL,
  sigma_BB = NULL, pro = NULL, groups, z, likelihood = FALSE,
  method_sigma_AB = c("analytic", "numeric"), updateA = FALSE)

`x_A`	A matrix of data for previous batch.
`x_B`	A matrix of data for the current batch.
`mean_A`	The mean vectors for the previous batch (considered fixed).
`sigma_AA`	The covariance matrices for the previous batch (considered fixed).
`sigma_AB`	Starting value for the covariance between batches A and B. If `NULL`, initialised at all zeros.
`mean_B`	Starting value for the mean vectors for the current batch.
`sigma_BB`	Starting value for the covariance matrix for the current batch.
`pro`	Starting value for mixing proportions.
`groups`	Optional, number of groups in the mixture, inferred from `z` if not supplied.
`z`	A matrix of cluster memberships/probabilities.
`likelihood`	Logical for calculating likelihood of these two batches.
`method_sigma_AB`	One of `"analytic"` (default) or `"numeric"`, to choose the method of estimating the between-batch covariance for a cluster.
`updateA`	Logical for updating the parameters of batch A after estimating the parameters for batch B.