qom {nbpMatching}

Quality of Match

Description

Quality of matches show how well matched pairs differ. For each variable the average distance is generated. Each item in a pair is assigned a group and after several iterations the quantile of these average distances is returned.

Usage

qom(
  covariate,
  matches,
  iterations = 10000,
  probs = NA,
  use.se = FALSE,
  all.vals = FALSE,
  seed = 101,
  ...
)

Arguments

covariate

A data.frame object.

matches

A data.frame or nonbimatch object. Contains information on how to match the covariate data set.

iterations

An integer. Number of iterations to run, defaults to 10,000.

probs

A numeric vector. Probabilities to pass to the quantile function.

use.se

A logical value. Determines if the standard error should be computed. Default value of FALSE.

all.vals

A logical value. Determines if false matches should be included in comparison. Default value of FALSE.

seed

Seed provided for random-number generation. Default value of 101.

...

Additional arguments, not used at the moment.

Details

This fuction is useful for determining the effectiveness of your weights (when generating a distance matrix). Weighting a variable more will lower the average distance, but it could penalize the distance of the other variables. Calculating the standard error requires calling hdquantile from Hmisc. The quantiles may be slighly different when using hdquantile.

Value

a list object containing elements with quality of match information

q

data.frame with quantiles for each covariate

se

data.frame with standard error for each covariate

sd

vector with standard deviate for each covariate

Author(s)

Cole Beck

Examples

df <- data.frame(id=LETTERS[1:25], val1=rnorm(25), val2=rnorm(25))
df.dist <- gendistance(df, idcol=1)
df.mdm <- distancematrix(df.dist)
df.match <- nonbimatch(df.mdm)
qom(df.dist$cov, df.match)
qom(df.dist$cov, df.match$matches)