R: Find Close Matches

find.matches {Hmisc} | R Documentation

## Find Close Matches

### Description

Compares each row in `x` against all the rows in `y`, finding rows in `y` with all columns within a tolerance of the values a given row of `x`. The default tolerance `tol` is zero, i.e., an exact match is required on all columns. For qualifying matches, a distance measure is computed. This is the sum of squares of differences between `x` and `y` after scaling the columns. The default scaling values are `tol`, and for columns with `tol=1` the scale values are set to 1.0 (since they are ignored anyway). Matches (up to `maxmatch` of them) are stored and listed in order of increasing distance.
The `summary` method prints a frequency distribution of the number of matches per observation in `x`, the median of the minimum distances for all matches per `x`, as a function of the number of matches, and the frequency of selection of duplicate observations as those having the smallest distance. The `print` method prints the entire `matches` and `distance` components of the result from `find.matches`.
`matchCases` finds all controls that match cases on a single variable `x` within a tolerance of `tol`. This is intended for prospective cohort studies that use matching for confounder adjustment (even though regression models usually work better).

### Usage

    find.matches(x, y, tol=rep(0, ncol(y)), scale=tol, maxmatch=10)
    ## S3 method for class 'find.matches'
    summary(object, ...)
    ## S3 method for class 'find.matches'
    print(x, digits, ...)

    matchCases(xcase,    ycase,    idcase=names(ycase),
               xcontrol, ycontrol, idcontrol=names(ycontrol),
               tol=NULL,
               maxobs=max(length(ycase),length(ycontrol))*10,
               maxmatch=20, which=c('closest','random'))

### Arguments

`x` | a numeric matrix or the result of `find.matches`
`y` | a numeric matrix with same number of columns as `x`
`xcase` |
`xcontrol` | vectors, not necessarily of the same length, specifying a numeric variable used to match cases and control
`ycase` |
`ycontrol` | vectors or matrices, not necessarily having the same number of rows, specifying a variable to carry along from cases and matching controls. If you instead want to carry along rows from a data frame, let `ycase` and `ycontrol` be non-overlapping integer subscripts of the donor data frame.
`tol` | a vector of tolerances with number of elements the same as the number of columns of `y`, for `find.matches`. For `matchCases` is a scalar tolerance.
`scale` | a vector of scaling constants with number of elements the same as the number of columns of `y`.
`maxmatch` | maximum number of matches to allow. For `matchCases`, maximum number of controls to match with a case (default is 20). If more than `maxmatch` matching controls are available, a random sample without replacement of `maxmatch` controls is used (if `which="random"`).
`object` | an object created by `find.matches`
`digits` | number of digits to use in printing distances
`idcase` |
`idcontrol` | vectors the same length as `xcase` and `xcontrol` respectively, specifying the id of cases and controls. Defaults are integers specifying original element positions within each of cases and controls.
`maxobs` | maximum number of cases and all matching controls combined (maximum dimension of data frame resulting from `matchControls`). Default is ten times the maximum of the number of cases and number of controls. `maxobs` is used to allocate space for the resulting data frame.
`which` | set to `"closest"` (the default) to match cases with up to `maxmatch` controls that most closely match on `x`. Set `which="random"` to use randomly chosen controls. In either case, only those controls within `tol` on `x` are allowed to be used.
`...` | unused

### Value

`find.matches` returns a list of class `find.matches` with elements `matches` and `distance`. Both elements are matrices with the number of rows equal to the number of rows in `x`, and with `k` columns, where `k` is the maximum number of matches (`<= maxmatch`) that occurred. The elements of `matches` are row identifiers of `y` that match, with zeros if fewer than `maxmatch` matches are found (blanks if `y` had row names). `matchCases` returns a data frame with variables `idcase` (id of case currently being matched), `type` (factor variable with levels `"case"` and `"control"`), `id` (id of case if case row, or id of matching case), and `y`.

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University
f.harrell@vanderbilt.edu

### References

Ming K, Rosenbaum PR (2001): A note on optimal matching with variable controls using the assignment algorithm. J Comp Graph Stat 10:455–463.

Cepeda MS, Boston R, Farrar JT, Strom BL (2003): Optimal matching with a variable number of controls vs. a fixed number of controls for a cohort study: trade-offs. J Clin Epidemiology 56:230-237. Note: These papers were not used for the functions here but probably should have been.

### See Also

`scale`, `apply`

### Examples

    y <- rbind(c(.1, .2),c(.11, .22), c(.3, .4), c(.31, .41), c(.32, 5))
    x <- rbind(c(.09,.21), c(.29,.39))
    y
    x
    w <- find.matches(x, y, maxmatch=5, tol=c(.05,.05))

    set.seed(111)       # so can replicate results
    x <- matrix(runif(500), ncol=2)
    y <- matrix(runif(2000), ncol=2)
    w <- find.matches(x, y, maxmatch=5, tol=c(.02,.03))
    w$matches[1:5,]
    w$distance[1:5,]
    # Find first x with 3 or more y-matches
    num.match <- apply(w$matches, 1, function(x)sum(x > 0))
    j <- ((1:length(num.match))[num.match > 2])[1]
    x[j,]
    y[w$matches[j,],]

    summary(w)

    # For many applications would do something like this:
    # attach(df1)
    # x <- cbind(age, sex) # Just do as.matrix(df1) if df1 has no factor objects
    # attach(df2)
    # y <- cbind(age, sex)
    # mat <- find.matches(x, y, tol=c(5,0)) # exact match on sex, 5y on age

    # Demonstrate matchCases
    xcase     <- c(1,3,5,12)
    xcontrol  <- 1:6
    idcase    <- c('A','B','C','D')
    idcontrol <- c('a','b','c','d','e','f')
    ycase     <- c(11,33,55,122)
    ycontrol  <- c(11,22,33,44,55,66)
    matchCases(xcase, ycase, idcase,
               xcontrol, ycontrol, idcontrol, tol=1)

    # If y is a binary response variable, the following code
    # will produce a Mantel-Haenszel summary odds ratio that
    # utilizes the matching.
    # Standard variance formula will not work here because
    # a control will match more than one case
    # WARNING: The M-H procedure exemplified here is suspect
    # because of the small strata and widely varying number
    # of controls per case.

    x    <- c(1, 2, 3, 3, 3, 6, 7, 12,  1, 1:7)
    y    <- c(0, 0, 0, 1, 0, 1, 1,  1,  1, 0, 0, 0, 0, 1, 1, 1)
    case <- c(rep(TRUE, 8), rep(FALSE, 8))
    id   <- 1:length(x)

    m <- matchCases(x[case],  y[case],  id[case],
                    x[!case], y[!case], id[!case], tol=1)
    iscase <- m$type=='case'
    # Note: the first tapply on insures that event indicators are
    # sorted by case id.  The second actually does something.
    event.case    <- tapply(m$y[iscase],  m$idcase[iscase],  sum)
    event.control <- tapply(m$y[!iscase], m$idcase[!iscase], sum)
    n.control     <- tapply(!iscase,      m$idcase,          sum)
    n             <- tapply(m$y,          m$idcase,          length)
    or <- sum(event.case * (n.control - event.control) / n) /
          sum(event.control * (1 - event.case) / n)
    or

    # Bootstrap this estimator by sampling with replacement from
    # subjects.  Assumes id is unique when combine cases+controls
    # (id was constructed this way above).  The following algorithms
    # puts all sampled controls back with the cases to whom they were
    # originally matched.

    ids <- unique(m$id)
    idgroups <- split(1:nrow(m), m$id)
    B   <- 50   # in practice use many more
    ors <- numeric(B)
    # Function to order w by ids, leaving unassigned elements zero
    align <- function(ids, w) {
      z <- structure(rep(0, length(ids)), names=ids)
      z[names(w)] <- w
      z
    }
    for(i in 1:B) {
      j <- sample(ids, replace=TRUE)
      obs <- unlist(idgroups[j])
      u <- m[obs,]
      iscase <- u$type=='case'
      n.case <- align(ids, tapply(u$type, u$idcase,
                                  function(v)sum(v=='case')))
      n.control <- align(ids, tapply(u$type, u$idcase,
                                     function(v)sum(v=='control')))
      event.case <- align(ids, tapply(u$y[iscase],  u$idcase[iscase],  sum))
      event.control <- align(ids, tapply(u$y[!iscase], u$idcase[!iscase], sum))
      n <- n.case + n.control
      # Remove sets having 0 cases or 0 controls in resample
      s             <- n.case > 0 & n.control > 0
      denom <- sum(event.control[s] * (n.case[s] - event.case[s]) / n[s])
      or <- if(denom==0) NA else
       sum(event.case[s] * (n.control[s] - event.control[s]) / n[s]) / denom
      ors[i] <- or
    }
    describe(ors)

[Package Hmisc version 4.0-3 Index]