Description Usage Arguments Details Value Author(s)

View source: R/rcbsubset-internal.R

These are internal rcbsubset methods not meant to be called directly by users. They are used to construct a network flow problem from the information about a matching problem that is passed to the `rcbsubset`

method.

1 2 3 4 5 6 7 8 9 10 11 | ```
dist2net(dist.struct, k, exclude.treated = FALSE, exclude.penalty = NULL, ncontrol = NULL,
tol = 1e-3)
dist2net.matrix(dist.struct, k, exclude.treated = FALSE, exclude.penalty = NULL,
tol = 1e-3)
add.layer(net.layers, new.layer)
penalty.update(net.layers, newtheta, newp = NA)
penalize.near.exact(net.layers, near.exact)
``` |

`dist.struct` |
an object specifying the sparsity structure of the match. For the dist2net method it is a list of vectors, and for the dist2net.matrix method it is a matrix or InfinitySparseMatrix. See rcbalance documentation for more details. |

`k` |
a nonnegative integer. The number of control units to which each treated unit will be matched. |

`exclude.treated` |
if |

`exclude.penalty` |
a parameter that gives the cost of excluding a treated unit. If left NULL it will be set to a very large value designed to ensure treated units are never excluded if they can be matched. Lower values may result in subsets of treated units being excluded. |

`ncontrol` |
the total number of controls in the matching problem. If left |

`tol` |
edge cost tolerance. This is the smallest tolerated difference between matching costs. It is used in these functions only when exclude.penalty is NULL, to choose a default penalty that is small enough not to cause integer overflows. |

`net.layers` |
a layered network object of the type produced by the dist2net function. |

`new.layer` |
a vector equal in length to the number of treated and control units in the matching problem. Each coordinate contains the value of a new fine balance variable for the corresponding unit. |

`newtheta` |
optional argument giving a new value for the theta field of the net.layers object (see value section for description of this field). |

`newp` |
optional argument giving a new value for the p field of the net.layers object (see value section for description of this field). |

`near.exact` |
a vector equal in length to the number of treated and control units in the matching problem. Edges between units with different values of this variable will be penalized. |

`dist2net`

and `dist2net.matrix`

take the distance structure given to `rcbalance`

encoding information about the matching problem and converts it into a network flow problem. `add.layer`

adds network structure to handle an individual fine balance variable (it can be called iteratively to add many such variables). `penalty.update`

is used to change the penalties for each layer (and the penalties for edges used to exclude treated units if they are present) and `penalize.near.exact`

is used to add penalties to the treated-control edges to allow near-exact matching. See the references for a detailed description of how the matching problem is transformed into a network.

A layered network object, formatted as a list with the following arguments (where narcs is the number of arcs and nnodes is the number of nodes in the network):

`startn` |
a vector of length narc containing the node numbers of the start nodes of each arc in the network. |

`endn` |
a vector of length narc containing the node numbers of the end nodes of each arc in the network. |

`ucap` |
a vector of length narc containing the (integer) upper capacity of each arc in the network. |

`cost` |
a vector of length narc containing the (integer) cost of each arc in the network. |

`b` |
a vector of length nnode containing the (integer) supply or demand of each node in the network. Supplies are given as positive numbers and demands as negative numbers. |

`tcarcs` |
an integer giving the total number of arcs between the treated and control nodes in the network. |

`layers` |
a list object containing information about the refined covariate balance layers of the network. |

`z` |
a vector of treatment indicators. |

`fb.structure` |
a matrix containing information about the membership of the treated and control units in the different classes of refined balance covariates. |

`penalties` |
a vector of integer penalties, one for each fine balance layer. |

`theta` |
a value no less than 1 giving the ratio by which the penalty is increased with each additional layer of fine balance. |

`p` |
a nonnegative value giving the penalty for the finest level of fine balance. |

Samuel D. Pimentel

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