gowerD: Computes the extended Gower distance of two data sets

In VIM: Visualization and Imputation of Missing Values

Computes the extended Gower distance of two data sets

Description

The function gowerD is used by kNN to compute the distances for numerical, factor ordered and semi-continous variables.

Usage

gowerD(
  data.x,
  data.y = data.x,
  weights = rep(1, ncol(data.x)),
  numerical = colnames(data.x),
  factors = vector(),
  orders = vector(),
  mixed = vector(),
  levOrders = vector(),
  mixed.constant = rep(0, length(mixed)),
  returnIndex = FALSE,
  nMin = 1L,
  returnMin = FALSE,
  methodStand = "range"
)

Arguments

data.x	data frame
data.y	data frame
weights	numeric vector providing weights for the observations in x
numerical	names of numerical variables
factors	names of factor variables
orders	names of ordered variables
mixed	names of mixed variables
levOrders	vector with number of levels for each orders variable
mixed.constant	vector with length equal to the number of semi-continuous variables specifying the point of the semi-continuous distribution with non-zero probability
returnIndex	logical if TRUE return the index of the minimum distance
nMin	integer number of values with smallest distance to be returned
returnMin	logical if the computed distances for the indices should be returned
methodStand	character either "range" or "iqr", iqr is more robust for outliers

Details

Numerical and semi-continuous (mixed) variables are standardised by their range (or IQR, see methodStand) before the distance is computed; for mixed variables the point mass (mixed.constant) is excluded from the range estimate and scaled along with the data.

Missing values in a distance variable are replaced by a sentinel (the pooled column maximum plus one) after standardisation. Two records both missing the same variable therefore have distance 0 on it, and a missing-vs-observed pair contributes up to about 2 (rather than a variable's nominal maximum of 1). This sentinel convention departs from Gower's (1971) omit-and-renormalise rule and biases nearest-neighbour selection towards records that share the same missingness pattern.

The distance computation is parallelised with OpenMP where R was built with OpenMP support. The number of threads is getOption("VIM.ncores"); if the option is unset, at most 2 threads are used while the package is checked (⁠R CMD check⁠ sets ⁠_R_CHECK_LIMIT_CORES_⁠; CRAN policy allows at most two cores) and OpenMP's default – typically all cores, or OMP_NUM_THREADS – otherwise. Results do not depend on the number of threads.

returnIndex=FALSE: a numerical matrix n x m with the computed distances returnIndex=TRUE: a named list with "ind" containing the requested indices and "mins" the computed distances

Examples

data(sleep)
# all variables used as numerical
gowerD(sleep)

# split in numerical an
gowerD(sleep, numerical = c("BodyWgt", "BrainWgt", "NonD", "Dream", "Sleep", "Span", "Gest"),
  orders = c("Pred","Exp","Danger"), levOrders = c(5,5,5))

# as before but only returning the index of the closest observation
gowerD(sleep, numerical = c("BodyWgt", "BrainWgt", "NonD", "Dream", "Sleep", "Span", "Gest"),
  orders = c("Pred","Exp","Danger"), levOrders = c(5,5,5), returnIndex = TRUE)

VIM documentation built on Sept. 2, 2026, 5:07 p.m.