multinomTrain: Training multinomial model
In microclass: Methods for Taxonomic Classification of Prokaryotes

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/multinom.R

Training the multinomial K-mer method on sequence data.

1	multinomTrain(sequence, taxon, K = 8, col.names = FALSE, n.pseudo = 100)

`sequence`	Character vector of 16S sequences.
`taxon`	Character vector of taxon labels for each sequence.
`K`	Word length (integer).
`col.names`	Logical indicating if column names should be added to the trained model matrix.
`n.pseudo`	Number of pseudo-counts to use (positive numerics, need not be integer). Special case -1 will only return word counts, not log-probabilities.

The training step of the multinomial method (Vinje et al, 2015) means counting K-mers on all sequences and compute the multinomial probabilities for each K-mer for each unique taxon. n.pseudo pseudo-counts are added, divided equally over all K-mers, before probabilities are estimated. The optimal choice of n.pseudo will depend on K and the training data set. The default value n.pseudo=100 has proven good for K=8 and the contax.trim data set (see the microcontax R-package).

Adding the actual K-mers as column names (col.names=TRUE) will slow down the computations.

The relative taxon sizes are also computed, and may be used as an empirical prior in the classification step (see "prior" below).

A list with two elements. The first element is Method, which is the text "multinom" in this case. The second element is Fitted, which is a matrix of probabilities with one row for each unique taxon and one column for each possible word of lengthK. The sum of each row is 1.0. No probabilities are 0 if n.pseudo>0.0.

The matrix Fitted has an attribute attr("prior",), that contains the relative taxon sizes.

Kristian Hovde Liland and Lars Snipen.

Vinje, H, Liland, KH, Almøy, T, Snipen, L. (2015). Comparing K-mer based methods for improved classification of 16S sequences. BMC Bioinformatics, 16:205.

KmerCount, multinomClassify.