mnemk {mnem} | R Documentation

## Learn the number of components K and optimize the mixture.

### Description

High level function for learning the number of components k, if unknown.

### Usage

    mnemk(D, ks = seq_len(5), man = FALSE, degree = 4, logtype = 2,
      pen = 2, useF = FALSE, Fnorm = FALSE, ...)

### Arguments

`D` | data with cells indexing the columns and features (E-genes) indexing the rows
`ks` | vector of number of components k to test
`man` | logical. manual data penalty, e.g. man=TRUE and pen=2 for an approximation of the Akaike Information Criterion
`degree` | different degree of penalty for complexity: positive entries of transitively reduced phis or phi^r (degree=0), phi^r and mixture components minus one k-1 (1), phi^r, k-1 and positive entries of thetas (2), positive entries of transitively closed phis or phi^t, k-1 (3), phi^t, theta, k-1 (4, default), all entries of phis, thetas and k-1 (5)
`logtype` | logarithm type of the data (e.g. 2 for log2 data or exp(1) for natural)
`pen` | penalty weight for the data (e.g. pen=2 for approximate Akaike Information Criterion)
`useF` | use F (see publication) as complexity instead of phi and theta
`Fnorm` | normalize complexity of F, i.e. if two components have the same entry in F, it is only counted once
`...` | additional parameters for the mnem main function

### Value

list containing the result of the best k as an mnem object and the raw and penalized log likelihoods

### Author(s)

Martin Pirkl

### Examples

    sim <- simData(Sgenes = 3, Egenes = 2, Nems = 2, mw = c(0.4,0.6))
    data <- (sim$data - 0.5)/0.5
    data <- data + rnorm(length(data), 0, 1)
    result <- mnemk(data, ks = seq_len(2), starts = 1)

[Package mnem version 1.0.0 Index]