Description Usage Arguments Details Value Author(s) References See Also

Deprecated in favor of `match_on`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
mdist(x, structure.fmla = NULL, ...)
## S3 method for class 'optmatch.dlist'
mdist(x, structure.fmla = NULL, ...)
## S3 method for class ''function''
mdist(x, structure.fmla = NULL, data = NULL, ...)
## S3 method for class 'formula'
mdist(x, structure.fmla = NULL, data = NULL, subset = NULL, ...)
## S3 method for class 'glm'
mdist(x, structure.fmla = NULL, standardization.scale = mad, ...)
## S3 method for class 'bigglm'
mdist(x, structure.fmla = NULL, data = NULL, standardization.scale = mad, ...)
## S3 method for class 'numeric'
mdist(x, structure.fmla = NULL, trtgrp = NULL, ...)
``` |

`x` |
The object to use as the basis for forming the mdist. Methods exist for formulas, functions, and generalized linear models. |

`structure.fmla` |
A formula denoting the treatment variable on
the left hand side and an optional grouping expression on the
right hand side. For example, |

`...` |
Additional method arguments. Most methods require a 'data' argument. |

`data` |
Data where the variables references in 'x' live. |

`subset` |
If non-NULL, the subset of 'data' to be used. |

`standardization.scale` |
A function to scale the distances; by default uses 'mad'. |

`trtgrp` |
Dummy variable for treatment group membership. |

The `mdist`

method provides three ways to construct a
matching distance (i.e., a distance matrix or suitably organized
list of such matrices): guided by a function, by a fitted model,
or by a formula. The class of the first argument given to
`mdist`

determines which of these methods is invoked.

The `mdist.function`

method takes a function of two
arguments. When called, this function will receive the treatment
observations as the first argument and the control observations as
the second argument. As an example, the following computes the raw
differences between values of `t1`

for treatment units (here,
nuclear plants with `pr==1`

) and controls (here, plants with
`pr==0`

), returning the result as a distance matrix:

```
sdiffs <- function(treatments, controls) {
abs(outer(treatments$t1, controls$t1, `-`))
}
```

The `mdist.function`

method does similar things as the
earlier optmatch function `makedist`

, although the interface
is a bit different.

The `mdist.formula`

method computes the squared Mahalanobis
distance between observations, with the right-hand side of the
formula determining which variables contribute to the Mahalanobis
distance. If matching is to be done within strata, the
stratification can be communicated using either the
`structure.fmla`

argument (e.g. `~ grp`

) or as part of
the main formula (e.g. `z ~ x1 + x2 | grp`

).

An `mdist.glm`

method takes an argument of class `glm`

as first argument. It assumes that this object is a fitted
propensity model, extracting distances on the linear propensity
score (logits of the estimated conditional probabilities) and, by
default, rescaling the distances by the reciprocal of the pooled
s.d. of treatment- and control-group propensity scores. (The
scaling uses `mad`

, for resistance to outliers, by default;
this can be changed to the actual s.d., or rescaling can be
skipped entirely, by setting argument
`standardization.scale`

to `sd`

or `NULL`

,
respectively.) A `mdist.bigglm`

method works analogously
with `bigglm`

objects, created by the `bigglm`

function
from package ‘biglm’, which can handle bigger data sets
than the ordinary glm function can. In contrast with
`mdist.glm`

it requires additional `data`

and
`structure.fmla`

arguments. (If you have enough data to
have to use `bigglm`

, then you'll probably have to subgroup
before matching to avoid memory problems. So you'll have to use
the `structure.fmla`

argument anyway.)

Object of class `optmatch.dlist`

, which is suitable
to be given as `distance`

argument to
`fullmatch`

or `pairmatch`

.

Mark M. Fredrickson

P.~R. Rosenbaum and D.~B. Rubin (1985),
‘Constructing a control group using multivariate matched
sampling methods that incorporate the propensity score’,
*The American Statistician*, **39** 33–38.

`fullmatch`

, `pairmatch`

,
`match_on`

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