spacegraph {cem} | R Documentation |

Randomly compute many different matching solutions

spacegraph(treatment=NULL, data = NULL, R=list("cem"=50,"psm"=0,"mdm"=0,"matchit"=0), grouping = NULL, drop=NULL, L1.breaks = NULL, L1.grouping=NULL, fixed = NULL, minimal = 1, maximal = 15, M=100, raw.profile=NULL, keep.weights=FALSE, progress=TRUE, rgrouping=FALSE, groups=NULL, psmpoly=1, mdmpoly=1, other.matches=NULL, heuristic=FALSE, linear.pscore=FALSE, verbose=1)

`treatment` |
character, name of the treatment variable |

`data` |
a data.frame |

`drop` |
a vector of variable names in the data frame to ignore during matching. |

`R` |
a named list that gives the number of of possible random solutions for each matching method. Allowed methods are |

`grouping` |
named list, each element of which is a list of
groupings for a single categorical variable. For more details see |

`L1.breaks` |
list of cutpoints for the calculation of the L1 measure. |

`L1.grouping` |
as |

`fixed` |
vector of variable names which will not be relaxed. |

`minimal` |
the minimal number of intervals acceptable after relaxation. Should be a named list of positive integers or if a number, this is applied to all variables. |

`maximal` |
the maximal number of intervals acceptable after relaxation. Should be a named list of positive integers or if a number, this is applied to all variables. |

`M` |
number of possible random coarsening for the L1 measure |

`raw.profile` |
an object of class |

`keep.weights` |
if |

`rgrouping` |
Boolean, specifies whether levels of categorical variables should be randomly grouped together by CEM. |

`groups` |
named list, each element of which is a list of allowable groupings for a single categorical variable. |

`psmpoly` |
numeric, specifying the order of polynomials to include in the propensity score models. At the moment, only |

`mdmpoly` |
numeric, specifying the order of polynomials to include in Mahalanobis matching. At the moment, only |

`other.matches` |
This argument allows plotting of user-specified matching solutions. The solutions must be supplied in a specific format: as a list of data frames, where each data frame contains the observation IDs, observations weights, and the method. These must be provided in three columns of the data frame, with names (exactly) "id", "weight", "method". See the example. |

`heuristic` |
Boolean, if TRUE spacegraph uses a heuristic method to randomly select covariates for MDM and PSM rather than sampling from all possible combinations of covariates. The heuristic tends to select most of the main effects and a small number of interactions. This method is automatically applied with large numbers of covariates. |

`progress` |
show progress bars. |

`linear.pscore` |
does |

`verbose` |
integer, controls the level of output. |

Spacegraph is a tool to help the user to the search for optimal matching solutions by generating many matching solutions from a variety of matching algorithms (currently CEM, Mahalanobis distance matching, and propensity score matching are supported). The resulting object can be plotted with `plot()`

to show where each solution falls along the bais-variance tradeoff.

The `spacegraph`

function currently calculates two measures of balance for each solution: the L1 metric (see `L1.meas`

) and the difference in means of the covariates. Typically, analyists look at the difference in means seperately for each variable, but this can't be plotted on a two-dimensional graph. We summarize the difference in means by calculating the average difference in means for all of the covariates. Specifically, we calculate the difference in means for each variable as mean(treated)-mean(control)/sd(treated) and then average across all covariates.

`val` |
an object of class |

Richard Nielsen, Stefano Iacus, Gary King, and Giuseppe Porro

data(LL) set.seed(123) sp <- spacegraph("treated", LL, drop="re78", M=5, R=list(cem=5,psm=5, mdm=5)) plot(sp) ## ABOUT THE PLOTTING TOOL: ## The circled solution is the current selection. ## Solutions that are strictly better are also circled. ## The gui provides the exact call to re-run the selected matching solution. ## The call can also be edited, re-run by clicking the button, and ## automatically added to the existing spacegraph. ## CEM solutions can also be adjusted variable by variable and re-run. ## Some plotting parameters can be changed plot(sp, main="Comparison of Matching Methods", ylab="L1", xlim=c(300,50), ylim=c(0,.7)) ## You can specify whether the x-axis shows treated units, ## control units, or all units using the argument N, which ## can take the values "treated", "control", or "all". ## Default is "treated". plot(sp, N="all") ## You can specify how the x-axis is scaled. Setting scale.var=T ## gives you the scaling as 1/sqrt(n). Setting scale.var=F gives ## scales it linearly. Default is scale.var=TRUE. plot(sp, scale.var=FALSE) ## You can also specify which measure of balance to use ## by specifying the argument "balance.metric" as ## "L1", "mdiff" (Avg. standardized difference in means), ## or "mdisc" (Average Malanobis Discrepancy). Default is "L1". plot(sp, balance.metric="mdiff") plot(sp, balance.metric="mdisc") ## Matching solutions from other methods can be included in ## a spacegraph by using the argument "other.matches". ## First, Run a matching method. Here, propensity scores from MatchIt. library(MatchIt) m.out <- matchit(formula=treated ~ education+age, data=LL, method = "nearest") ## Put the required information into a list of data frames. ## Note, there are many ways to do this. mymatches <- list(data.frame(names(m.out$w))) names(mymatches[[1]])[1] <- "id" mymatches[[1]]$weight <- m.out$w mymatches[[1]]$method <- "matchit psm" sp <- spacegraph("treated", LL, drop="re78", M=5, R=list(cem=5,psm=5, mdm=5), other.matches=mymatches) plot(sp)

[Package *cem* version 1.1.29 Index]