Package 'rcbalance'

a vector of treatment and control indicators, 1 for treatment and 0 for control.

a data frame or a numeric or logical matrix containing covariate information for treated and control units. Its row count must be equal to the length of z.

an optional vector of the same length as z. If this argument is specified, treated units will only be allowed to match to control units that have equal values in the corresponding indices of the exact vector. For example, to match patients within hospitals only, one could set exact equal to a vector of hospital IDs for each patient.

one of ('propensity','user','none'). If 'propensity' is specified (the default option), the function estimates a propensity score via logistic regression of z on X and imposes a propensity score caliper. If 'user' is specified, the user must provide a vector of values on which a caliper will be enforced using the calip.cov argument. If 'none' is specified no caliper is used.

see calip.option.

gives the size of the caliper when the user specifies the calip.option argument as 'propensity' or 'calip.cov'.

if TRUE, prints output describing specific adjustments made in creating the distance objects.

a network flow problem, formatted as a list with the following arguments (where the network contains nnode nodes, numbered 1 through nnode and narc arcs):

• 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.

the name of the package used to solve the network flow optimization problem underlying the match, one of 'rlemon' (which uses the Lemon Optimization Library) and 'rrelaxiv' (which uses the RELAX-IV algorithm).

an integer equal to zero (included in order to support legacy versions of code).

an integer, equal to zero if the problem is infeasible and equal to 1 if it is feasible. A network with a supply/demand vector of all zeroes may also be flagged as infeasible.

a vector equal in length to the number of arcs in argument problem net, giving in each coordinate the number of units of flow passing across the corresponding edge in the optimal network flow.

a vector of treatment indicators. Must contain exactly 2 distinct values, one for treated and one for control.

a vector of categories of a potential blocking variable. Must be the same length as argument z.

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.

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

if TRUE, then when there is no feasible match using all treated units, a minimal number of treated units may be dropped so that a match can be formed. Specifying this argument adds penalized edges to the network so that such a match can be computed. NOTE: this argument is incompatible with values of k greater than 1.

the number of controls in the matching problem. If left NULL (the default value), the value will be intuited from the maximum control label in the sparsity object.

a layered network object of the type produced by the dist2net function.

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.

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

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

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.

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

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

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

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

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.

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

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

a vector of treatment indicators.

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

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

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

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

a list of vectors that encodes information about covariate distances between treated and control units. The list is equal in length to the number of treated units. Each vector corresponds to a treated unit and is equal in length to the number of control units to which it can be matched. It is assumed that there are a total of nc control units in the problem and that they are numbered from 1 to nc. The names of each vector in the list give the index (in the vector 1:nc) of the control units to which the treated unit in question can be matched, and the elements of each vector are the covariate distances between the treated unit and the corresponding control. Note that for a dense matching problem (in which each treated unit can be matched to any control), every vector in the list will have length nc and rownames 1 through nc.

Alternatively, this same information can be passed as a matrix or InfinitySparseMatrix with rows corresponding to treated units and columns corresponding to controls. Entries given as Inf correspond to pairs that cannot be matched.

an optional character vector specifying names of covariates for near-exact matching. This argument takes precedence over any refined covariate balance constraints, so the match will produce the best refined covariate balance subject to matching exactly on this variable wherever possible. If multiple covariates are named, near-exact matching will be done on their interaction.

an optional list of character vectors specifying covariates to be used for refined balance. Each element of the list corresponds to a level of refined covariate balance, and the levels are assumed to be in decreasing order of priority. Each character vector should contain one or more names of categorical covariates on which the user would like to enforce near fine balance. If multiple covariates are specified, an interaction is created between the categories of the covariates and near fine balance is enforced on the interaction. IMPORTANT: covariates or interactions coming later in the list must be nested within covariates coming earlier in the list; if this is not the case the function will stop with an error. An easy way to ensure that this occurs is to include in each character vector all the variables named in earlier list elements. If the fb.list argument is specified, the treated.info and control.info arguments must also be specified.

an optional data frame containing covariate information for the treated units in the problem. The row count of this data frame must be equal to the length of the distance.structure argument, and it is assumed that row i contains covariate information for the treated unit described by element i of distance.structure. In addition, the column count and column names must be identical to those of the control.info argument, and the column names must include all of the covariate names mentioned in the near.exact and fb.list arguments.

an optional data frame containing covariate information for the control units in the problem. The row count of this data frame must be no smaller than the maximum control index in the distance.structure argument, and it is assumed that row i contains the covariate information for the control indexed by i in distance.structure. In addition, the column count and column names must be identical to those of the treated.info argument.

if TRUE, then when there is no feasible match using all treated units, a minimal number of treated units will be dropped so that a match can be formed. The excluded treated units will be selected optimally so that the cost of the matching is reduced as much as possible. NOTE: exclude.treated = TRUE is incompatible with arguments to target.group and with values of k larger than 1.

an optional data frame of observations with the desired covariate distribution for the selected control group, if it differs from the covariate distribution of the treated units. This argument will be ignored unless fb.list, treated.info and control.info are also specified, and it must have the same dimensions as treated.info.

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

a value greater than 1. This is a tuning parameter that helps ensure the different levels of refined covariate balance are prioritized correctly. Setting the penalty higher tends to improve the guarantee of match optimality up to a point, but penalties above a certain level cause integer overflows and throw errors. Usually it is not recommended that the user change this parameter from its default value.

edge cost tolerance. This is the smallest tolerated difference between matching costs; cost differences smaller than this will be considered zero. Match distances will be scaled by inverse tolerance, so when matching with large edge costs or penalties the tolerance may need to be increased.

the name of the package used to solve the network flow optimization problem underlying the match, one of 'rlemon' (which uses the Lemon Optimization Library) and 'rrelaxiv' (which uses the RELAX-IV algorithm).

a nt by k matrix containing the matched sets produced by the algorithm (where nt is the number of treated units). The rownames of this matrix are the numbers of the treated units (indexed by their position in distance.structure), and the elements of each row contain the indices of the control units to which this treated unit has been matched.

a list of matrices, equal in length to the fb.list argument. Each matrix is a contingency table giving the counts among treated units and matched controls for each level of the categorical variable specified by the corresponding element of fb.list.