proxy (version 0.4-16)

pr_DB: Registry of proximities

Description

Registry containing similarities and distances.

Usage

pr_DB
pr_DB$get_field(name)
pr_DB$get_fields()
pr_DB$get_field_names()
pr_DB$set_field(name, default = NA, type = NA, is_mandatory = FALSE, is_modifiable = TRUE, validity_FUN = NULL)
pr_DB$entry_exists(name)
pr_DB$get_entry(name)
pr_DB$get_entries(name = NULL, pattern = NULL)
pr_DB$get_entry_names(name)
pr_DB$set_entry(...)
pr_DB$modify_entry(...)
pr_DB$delete_entry(name)
"summary"(object, verbosity = c("short", "long"), ...)

Arguments

name

character string representing the name of an entry (case-insensitive).

pattern

regular expression to be matched to all fields of class "character" in all entries.

default

optional default value for the field.

type

optional character string specifying the class to be required for this field. If type is a character vector with more than two elements, the entries will be used as fixed set of alternatives. If type is not a character string or vector, the class will be inferred from the argument given.

is_mandatory

logical specifying whether new entries are required to have a value for this field.

is_modifiable

logical specifying whether entries can be changed with respect to that field.

validity_FUN

optional function or character string with the name of a function that checks the validity of a field entry. Such a function gets the value to be investigated as argument, and should stop with an error message if the value is not correct.

object

a registry object.

verbosity

controlling the verbosity of the output of the summary method for the registry. "short" gives just a list, "long" also gives the formulas.

...

for pr_DB$set_entry and pr_DB$modify_entry: named list of fields to be modified in or added to the registry (see details). This must include the index field ("names").

Details

pr_DB represents the registry of all proximity measures available. For each measure, it comprises meta-information that can be queried and extended. Also, new measures can be added. This is done using the following accessor functions of the pr_DB object: get_field_names() returns a character vector with all field names. get_field() returns the information for a specific field as a list with components named as described above. get_fields() returns a list with all field entries. set_field() is used to create new fields in the repository (the default value will be set in all entries).

get_entry_names() returns a character vector with (the first alias of) all entries. entry_exists() is a predicate checking if an entry with the specified alias exists in the registry. get_entry() returns the specified entry if it exists (and, by default, gives an error if it does not). get_entries() is used to query more than one entry: either those matching name exactly, or those where the regular expression in pattern matches any character field in an entry. By default, all values are returned. delete_entry removes an existing entry from the registry (note that only user-provided entries can be deleted). set_entry and modify_entry require a named list of arguments used as field entries. At least the names index field is required. set_entry will check for all other mandatory fields. If specified in the field meta data, each field entry and the entry as a whole is checked for validity. Note that only user-specified fields and/or entries can be modified, the data shipped with the package are read-only.

The registry fields currently available are as follows:

A function specified as FUN parameter has mandatory arguments x and y (if abcd is FALSE), and a, b, c, d, n otherwise. Additionally, it gets all optional parameters specified by the user in the ... argument of the dist and simil functions, possibly changed and/or complemented by the corresponding (optional) PREFUN function. It must return the (diss-)similarity value computed from the arguments. x and y are two vectors from the data matrix (matrices) supplied. If abcd is FALSE, it is assumed that binary measures will be used, and the number of all n concordant and discordant pairs (x\_k, y\_k) precomputed and supplied instead of x and y. a, b, c, and d are the counts of all (TRUE, TRUE), (TRUE, FALSE), (FALSE, TRUE), and (FALSE, FALSE) pairs, respectively.

A function specified as PREFUN parameter has mandatory arguments x, y, p, and reg_entry, with y and p possibly being NULL depending on the task at hand. x and y are the data objects, p is a (possibly empty) list with all specified proximity parameters, and reg_entry is the registry entry (a named list containing all information specified in reg_add). The preprocessing function is allowed to change all these information, and if so, is required to return *all* arguments as a named list in the same order.

A function specified as POSTFUN parameter has two mandatory arguments: result and p. result will contain the computed raw data, i.e. a vector of length $n * (n - 1) / 2$ for auto-distances (see dist for details on dist objects), or a matrix for cross-distances. p contains the specified proximity parameters. Post-processing functions need to return the result object (even if unmodified).

A function specified as convert parameter should preserve the type of its argument.

See Also

dist

Examples

## create a new distance measure
mydist <- function(x,y) x * y

## create a new entry in the registry with two aliases
pr_DB$set_entry(FUN = mydist, names = c("test", "mydist"))

## look it up (index is case insensitive):
pr_DB$get_entry("TEST")

## modify the content of the description field in the new entry
pr_DB$modify_entry(names = "test", description = "foo function")

## create a new field
pr_DB$set_field("New")

## look up the test entry again (two ways)
pr_DB$get_entry("test")
pr_DB[["test"]]

## show total number of entries
length(pr_DB)

## show all entries (short list)
pr_DB$get_entries(pattern = "foo")

## show more details
summary(pr_DB, "long")

## get all entries in a list (and extract first two ones)
pr_DB$get_entries()[1:2]

## get all entries as a data frame (select first 3 fields)
as.data.frame(pr_DB)[,1:3]

## delete test entry
pr_DB$delete_entry("test")

## check if it is really gone
pr_DB$entry_exists("test")