classify: classify
In clst: Classification by local similarity threshold

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

View source: R/classify.R

Functions to perform classification by local similarity threshold.

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)

`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)

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).

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`

Noah Hoffman

findThreshold

## 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)