multivar_match: Matching by computing multivar_scores based on several...
In fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods

Description Usage Arguments Details Value

multivar_match computes a multivar_score between each pair of observations between datasets x and y using several variables, then executes a merge by picking the highest multivar_score pair for each observation in x.

multivar_match(
  data1,
  data2,
  by = NULL,
  by.x = NULL,
  by.y = NULL,
  unique_key_1,
  unique_key_2,
  logit = NULL,
  missing = FALSE,
  wgts = NULL,
  compare_type = "diff",
  blocks = NULL,
  blocks.x = NULL,
  blocks.y = NULL,
  nthread = 1,
  top = 1,
  threshold = NULL,
  suffixes = c("_1", "_2")
)

`data1`	data.frame. First to-merge dataset.
`data2`	data.frame. Second to-merge dataset.
`by`	character string. Variables to merge on (common across data 1 and data 2). See `merge`
`by.x`	character string. Variable to merge on in data1. See `merge`
`by.y`	character string. Variable to merge on in data2. See `merge`
`unique_key_1`	character vector. Primary key of data1 that uniquely identifies each row (can be multiple fields)
`unique_key_2`	character vector. Primary key of data2 that uniquely identifies each row (can be multiple fields)
`logit`	a glm or lm model as a result from a logit regression on a verified dataset. See details.
`missing`	boolean T/F, whether or not to treat missing (NA) observations as its own binary column for each column in by. See details.
`wgts`	rather than a lm model, you can supply weights to calculate multivar_score. Can be weights from `calculate_weights`.
`compare_type`	a vector with the same length as "by" that describes how to compare the variables. Options are "in", "indicator", "substr", "difference", "ratio", and "stringdist". See X for details.
`blocks`	variable present in both data sets to "block" on before computing scores. multivar_scores will only be computed for observations that share a block. See details.
`blocks.x`	name of blocking variables in x. cannot supply both blocks and blocks.x
`blocks.y`	name of blocking variables in y. cannot supply both blocks and blocks.y
`nthread`	integer. Number of cores to use when computing all combinations. See `parallel::makecluster()`
`top`	integer. Number of matches to return for each observation.
`threshold`	numeric. Minimum score for a match to be included in the result.
`suffixes`	see `merge`

The best way to understand this function is to see the vignette 'Multivar_matching'.

There are two ways of performing this match: either with or without a pre-trained logit. To use a logit, you must have a verified set of matches. The names of the variables in this set must match the names of the variables in the data you pass into multivar_match. Without a pre-trained logit, you must have a set of weights for each variable that you want in the comparison. These can either be made up ahead of time, or you can use a verified set of matches and calculate_weights.

a data.table, the resultant match, including columns from both data sets.

fedmatch documentation built on Nov. 23, 2021, 1:07 a.m.