classify {clst} | R Documentation

## classify

### Description

Functions to perform classification by local similarity threshold.

### Usage

    classify(dmat, groups, dvect, method = "mutinfo", minScore = 0.45,
             doffset = 0.5, dStart = NA, maxDepth = 10, minGroupSize = 2,
             objNames = names(dvect), keep.data = TRUE, ..., verbose =
             FALSE)

    classifyIter(dmat, groupTab, dvect, dStart = NA, multiple = FALSE,
                 keep.data = TRUE, ..., verbose = FALSE)

    classifier(dmat, groups, dvect, method = 'mutinfo', minScore = 0.45,
               doffset = 0.5, dStart = NA, minGroupSize = 2,
               objNames = names(dvect), keep.data = TRUE, ..., verbose = FALSE,
               depth = 1)

    pull(dmat, groups, index)

    pullTab(dmat, groupTab, index)

### Arguments

`dmat` | Square matrix of pairwise distances.
`groups` | Object coercible to a factor identifying group membership of objects corresponding to either edge of `dmat`.
`groupTab` | a data.frame representing a taxonomy, with columns in increasing order of specificity from left to right (ie, Kingdom –> Species). Column names are used to name taxonomic ranks. Rows correspond to margins of dmat.
`dvect` | numeric vector of distance from query sequence to each reference corresponding to margins of dmat.
`method` | The method for calculating the threshold; only 'mutinfo' is currently implemented.
`minScore` | Threshold value for the match score to define a match.
`doffset` | Offset used in the denominator of the expression to calculate match score to penalize very small groups of reference objects.
`dStart` | start with this value of `D`.
`multiple` | if TRUE, stops at the rank that yields at least one match; if FALSE, continues to perform classification until exactly one match is identified.
`maxDepth` | Maximum number of iterations that will be attempted to perform classification.
`minGroupSize` | The minimal number of members comprising at least one group required to attempt classification.
`objNames` | Optional character identifiers for objects corresponding to margin of `dmat`.
`keep.data` | Populates `thresh$distances` (see `findThreshold`) if TRUE.
`verbose` | Terminal output is produced if TRUE.
`index` | an integer specifying an element in `dmat`
`...` | see Details
`depth` | specifies iteration number (not meant to be user-defined)

### Details

`classify` performs iterative classification. See the vignette vignette for package clst for a description of the classification algorithm.

`classifier` performs non-iterative classification, and is typically not called directly by the user.

The functions `pull` and `pullTab` are used to remove a single element of `dmat` for the purpose of performing classification agains the remaining elements. The value of these two functions (a list) can be passed directly to `classify` or `classifyIter` directly (see examples).

### Value

`classify` and `classifyIter` return `x`, a list of lists, one for each iteration of the classifier. Each sub-list contains the following named elements:

`depth` | An integer indicating the number of the iteration (where x[[i]]$depth == i)
`tally` | a `data.frame` with one row for each group or reference objects. Columns `below` and `above` contain counts of reference objects with distance values greater than or less than D, respectively; `score`, containing match score S; `match` is 1 if S ≥ minScore, 0 otherwise; and the minimum, median, and maximum values of distances to all members of the indicated group.
`details` | a list of two matrices, named "below" and "above", itemizing each object with index i in the reference set with distances below or above the distance threshold D, respectively. Columns include `index`, the index i; `dist`, the distance between the object and the query; and `group`, indicating the classification of the object.
`matches` | Character vector naming groups to which query object belongs.
`thresh` | object returned by `findThreshold`
`params` | a list of input arguments and their values
`input` | list containing copies of `dvect` and `groups`

### Author(s)

Noah Hoffman

### See Also

`findThreshold`

### Examples

    ## illustrate classification using the Iris data set
    data(iris)
    dmat <- as.matrix(dist(iris[,1:4], method="euclidean"))
    groups <- iris$Species

    ## remove one element from the data set and perform classification using
    ## the remaining elements as the reference set
    ind <- 1
    cat(paste('class of "unknown" sample is Iris',groups[ind]),fill=TRUE)
    cc <- classify(dmat[-ind,-ind], groups[-ind], dvect=dmat[ind, -ind])
    printClst(cc)

    ## this operation can be performed conveinetly using the `pull` function
    ind <- 51
    cat(paste('class of "unknown" sample is Iris',groups[ind]),fill=TRUE)
    cc <- do.call(classify, pull(dmat, groups, ind))
    printClst(cc)
    str(cc)

[Package clst version 1.38.0 Index]