matchit               package:MatchIt               R Documentation

_M_a_t_c_h_i_t: _M_a_t_c_h_i_n_g _S_o_f_t_w_a_r_e _f_o_r _C_a_u_s_a_l _I_n_f_e_r_e_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

     _Matchit_ enables parametric models for causal inference to work
     better by selecting well-matched subsets of the original treated
     and control groups.  MatchIt implements the suggestions of Ho,
     Imai, King, and Stuart (2004) for improving parametric statistical
     models by preprocessing data with nonparametric matching methods. 
     MatchIt implements a wide range of sophisticated matching methods,
     making it possible to greatly reduce the dependence of causal
     inferences on hard-to-justify, but commonly made, statistical
     modeling assumptions.  The software also easily fits into existing
     research practices since, after preprocessing with MatchIt,
     researchers can use whatever parametric model they would have used
     without MatchIt, but produce inferences with substantially more
     robustness and less sensitivity to modeling assumptions.  Matched
     data sets created by MatchIt can be entered easily in Zelig (<URL:
     http://gking.harvard.edu/zelig>) for subsequent parametric
     analyses. Full documentation is available online at <URL:
     http://gking.harvard.edu/matchit>, and help for specific commands
     is available through 'help.matchit'.

_U_s_a_g_e:

     matchit <- matchit(formula, data, model="logit", discard=0, reestimate=FALSE, nearest=TRUE,
                      replace=FALSE, m.order=2, ratio=1, caliper=0, calclosest=FALSE,
                      subclass=0, sub.by="treat", mahvars=NULL, exact=FALSE, counter=TRUE, full=FALSE, full.options=list(),...)

_A_r_g_u_m_e_n_t_s:

 formula: (required).  Takes the form of 'T ~ X1 + X2', where 'T' is a
          binary treatment indicator and 'X1' and 'X2' are the
          pre-treatment covariates, and 'T', 'X1', and 'X2' are
          contained in the same data frame.  The '+' symbol means
          "inclusion" not "addition." You may also include interaction
          terms in the form if 'I(X1*X2)' or squared terms in the form
          of 'I(X1^2)'.

    data: (required).  Data frame containing the variables called in
          the 'formula'.   The dataframe should not include variables
          with the names 'psclass', 'psweights', or 'pscore', as these
          are expressly reserved in the output dataframe for MatchIt.

   model: Method used to estimate the propensity score.  May be "logit"
          (default), "probit", "nnet", "GAM", or "cart".

 discard: Whether to discard units that fall outside some measure of
          support of the distance score.  0 (default)=keep all units. 
          1=keep all units with common support.  2=discard only control
          units outside the support of the distance measure of the
          treated units.  3=discard only treated units outside the
          support of the distance measure of the control units.

reestimate: Specifies whether to reestimate the propensity score model
          after discarding units (default=FALSE).

 nearest: Whether to perform nearest-neighbor matching (default=TRUE).  

 replace: Whether to match with replacement (default=FALSE). 

 m.order: Order in which to match treated units with control units. 
          1=optimal (requires ``optmatch"  package), 2 (default)=from
          high to low, 3=from low to high, 4=random order.

   ratio: The number of control units to be matched to each treated
          unit (default=1).

 caliper: Standard deviations of the propensity score within which to
          draw control units (default=0).

calclosest: If 'caliper!=0', whether to take the nearest available
          match if no matches are available within 'caliper'
          (default=FALSE).

subclass: Either a scaler specifying the number of subclasses
          (default=0) or a vector of probabilities to create quantiles
          based on 'sub.by'.

  sub.by: If 'subclass!=0', by what criteria to subclassify.  "treat"
          (default) =by the number of treated units, "control"=by the
          number of control units, "all"=by the total number of units.

 mahvars: Variables on which to perform Mahalanobis matching within
          each caliper (default=NULL).  Should be entered as a vector
          of names of variables in 'data'.

   exact: "FALSE" (default)=no exact matching.  "TRUE"=exact matching
          on all variables in 'formula'.  A vector of variable names
          (that are in 'data' to indicate separate variables on which
          to exact match, in combination with matching on the
          propensity score.

 counter: Whether to display counter indicating the progress of the
          matching (default=TRUE).

    full: Whether to do full matching (default=FALSE).  Requires
          ``optmatch" package.

full.options: Additional options for full matching.

     ...: Additional arguments to be passed to 'matchit', depending on
          the model to be used.

_D_e_t_a_i_l_s:

     The matching is done using the 'matchit(treat ~ X, ...)' command,
     where 'treat' is the vector of treatment assignments and 'X' are
     the covariates to be used in the matching.  There are a number of
     matching options, detailed below.  The full syntax is
     'matchit(formula, data=NULL, discard=0, exact=FALSE,
     replace=FALSE, ratio=1, model="logit", reestimate=FALSE,
     nearest=TRUE, m.order=2, caliper=0, calclosest=FALSE,
     mahvars=NULL, subclass=0, sub.by="treat", counter=TRUE,
     full=FALSE, full.options=list(),  ...)' A summary of the results
     can be seen  graphically using 'plot(matchitobject)', or
     numerically using 'summary(matchitobject)'.  
     'print(matchitobject)' also prints out the output.

_V_a_l_u_e:

    call: The original 'matchit' call.

 formula: Formula used to specify the propensity score.

match.matrix: n1 by ratio data frame where the rows correspond to
          treated units and the columns store the names of the control
          units matched to each treated unit.  NA indicates that
          treated unit was not matched.

in.sample: Vector of length n showing whether each unit was eligible
          for matching due to common support restrictions with
          'discard'.

 matched: Vector of length n showing whether each unit was matched.

psweights: Vector of length n giving the weight assigned to each unit
          in the matching process.  Each weight is proportional to the
          number of times that unit was matched.

 psclass: Subclass index in an ordinal scale from 1 to the number of
          subclasses.   Unmatched units have subclass=0.

   q.cut: Subclass cut points.

assign.model: Output of the assignment model.

    data: The original data set, with 'psclass', 'psweights', and
          'pscore' (propensity scores) added.

   treat: The treatment indicator from 'data'.

covariates: Covariates used in the right-hand side of the assignment
          model.

_A_u_t_h_o_r(_s):

     Daniel Ho <deho@fas.harvard.edu>;  Kosuke Imai
     <kimai@princeton.edu>; Gary King <king@harvard.edu>; Elizabeth
     Stuart<stuart@stat.harvard.edu>

_R_e_f_e_r_e_n_c_e_s:

     Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart (2004)

_S_e_e _A_l_s_o:

     Please use 'help.matchit' to access the matchit reference manual. 
     The complete document is available online at <URL:
     http://gking.harvard.edu/matchit>.

