optimal_rank {ccfindR} | R Documentation

## Determine optimal rank

### Description

Takes as main argument `scNMFSet` object containing factorized output and estimate the optimal rank.

### Usage

    optimal_rank(object, df = 10, BF.threshold = 3, type = NULL,
      m = NULL)

### Arguments

`object` | `scNMFSet` object containing factorization output, or data frame containing the rank-evidence profile.
`df` | Degrees of freedom for split fit. Upper bound is the total number of data points (number of rank values scanned).
`BF.threshold` | Bayes factor threshold for statistical threshold.
`type` | `c(1,2)`. Type 1 is where there is a clear maximum. Type 2 is where marginal likelihood reaches a maximal level and stays constant. If omitted, the type will be inferred from data.
`m` | Number of features (e.g., genes) in the count matrix. Only necessary when `object` is of type `data.frame`.

### Details

The input object is used along with Bayes factor threshold to determine the heterogeneity type (1 or 2) and the optimal rank. If evidence(rank 1)/evidence(rank2) > `BF.treshold`, rank 1 is favorable than rank 2.

### Value

List containing `type` and `ropt` (optimal rank).

### Examples

    set.seed(1)
    x <- simulate_whx(nrow=50, ncol=100, rank=5)
    s <- scNMFSet(x$x)
    s <- vb_factorize(s, ranks=seq(2,8), nrun=5)
    plot(s)
    optimal_rank(s)

[Package ccfindR version 1.4.2 Index]