Package 'gower'

Gower's distance/similarity measure.

Description

A C-based implementation of Gower's distance.

Author(s)

Maintainer: Mark van der Loo [email protected]

Other contributors:

• David Turner [contributor]

See Also

Useful links:

• https://github.com/markvanderloo/gower

• Report bugs at https://github.com/markvanderloo/gower/issues

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Gower's distance

Description

Compute Gower's distance, pairwise between records in two data sets x and y. Records from the smallest data set are recycled over.

Usage

gower_dist(
  x,
  y,
  pair_x = NULL,
  pair_y = NULL,
  eps = 1e-08,
  weights = NULL,
  ignore_case = FALSE,
  nthread = getOption("gd_num_thread")
)

Arguments

x	[data.frame]
y	[data.frame]
pair_x	[numeric|character] (optional) Columns in x used for comparison. See Details below.
pair_y	[numeric|character] (optional) Columns in y used for comparison. See Details below.
eps	[numeric] (optional) Computed numbers (variable ranges) smaller than eps are treated as zero.
weights	[numeric] (optional) A vector of weights of length ncol(x) that defines the weight applied to each component of the gower distance.
ignore_case	[logical] Toggle ignore case when neither pair_x nor pair_y are user-defined.
nthread	Number of threads to use for parallelization. By default, for a dual-core machine, 2 threads are used. For any other machine n-1 cores are used so your machine doesn't freeze during a big computation. The maximum nr of threads are determined using omp_get_max_threads at C level.

Value

A numeric vector of length max(nrow(x),nrow(y)). When there are no columns to compare, a message is printed and both numeric(0) is returned invisibly.

Details

There are three ways to specify which columns of x should be compared with what columns of y. The first option is do give no specification. In that case columns with matching names will be used. The second option is to use only the pairs_y argument, specifying for each column in x in order, which column in y must be used to pair it with (use 0 to skip a column in x). The third option is to explicitly specify the columns to be matched using pair_x and pair_y.

Note

Gower (1971) originally defined a similarity measure ($s$, say) with values ranging from 0 (completely dissimilar) to 1 (completely similar). The distance returned here equals $1-s$.

References

Gower, John C. "A general coefficient of similarity and some of its properties." Biometrics (1971): 857-871.

See Also

gower_topn

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Find the top-n matches

Description

Find the top-n matches in y for each record in x.

Usage

gower_topn(
  x,
  y,
  pair_x = NULL,
  pair_y = NULL,
  n = 5,
  eps = 1e-08,
  weights = NULL,
  ignore_case = FALSE,
  nthread = getOption("gd_num_thread")
)

Arguments

x	[data.frame]
y	[data.frame]
pair_x	[numeric|character] (optional) Columns in x used for comparison. See Details below.
pair_y	[numeric|character] (optional) Columns in y used for comparison. See Details below.
n	The top-n indices and distances to return.
eps	[numeric] (optional) Computed numbers (variable ranges) smaller than eps are treated as zero.
weights	[numeric] (optional) A vector of weights of length ncol(x) that defines the weight applied to each component of the gower distance.
ignore_case	[logical] Toggle ignore case when neither pair_x nor pair_y are user-defined.
nthread	Number of threads to use for parallelization. By default, for a dual-core machine, 2 threads are used. For any other machine n-1 cores are used so your machine doesn't freeze during a big computation. The maximum nr of threads are determined using omp_get_max_threads at C level.

Value

A list with two array elements: index and distance. Both have size n X nrow(x). Each ith column corresponds to the top-n best matches of x with rows in y. When there are no columns to compare, a message is printed and both distance and index will be empty matrices; the list is then returned invisibly.

See Also

gower_dist

Examples

# find the top 4 best matches in the iris data set with itself.
x <- iris[1:3,]
lookup <- iris[1:10,]
gower_topn(x=x,y=lookup,n=4)

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