L1.profile: Calculates L1 distance for different coarsenings

In cem: Coarsened Exact Matching

Calculates L1 distance for different coarsenings

Description

Calculates L1 distance for different coarsenings

Usage

L1.profile(group, data, drop = NULL, min.cut = 2, max.cut = 12, 
weights, plot = TRUE, add = FALSE, col = "red", 
lty = 1, M=100, useCP=NULL, grouping=NULL, progress=TRUE,
verbose=1)

Arguments

group	the group variable
data	the data
drop	a vector of variable names in the data frame to ignore
min.cut	minimum number of cut points per variable
max.cut	maximum number of cut points per variable
weights	weights
useCP	a list which elements is a list of cutpoints, usually passed from a previous instance of L1.profile. If not NULL these coarsenings are used instead of generating them randomly.
M	number of random coarsenings
plot	plot a graph?
add	add graph to an existing plot? Makes sense only if plot is TRUE
col	draw in specified color
lty	draw using specified lty
grouping	named list, each element of which is a list of groupings for a single categorical variable. See Details.
progress	if TRUE, feedback on progress is given. See Details.
verbose	integer, controls level of output.

Details

The L1 measure depends on the coarsening chosen to calculate it, and as such the comparison of different matching solutions may differ depending on this somewhat arbitrary choice. This function computes L1 for a random range of possible coarsenings. The point of this function is that if one matching solution has a lower L1 than another, then it dominates without regard to the choice of coarsening. A graphic display conveys the results succinctly. (The logic is similar to that for ROC curves used for classification algorithms.) (This degree of coarsening should remain fixed for different CEM runs.)

For each variables the function generates a random number of cutpoints between min.cut and max.cut in which to cut the support of each variable. This procedure is repeated M times. The out is sorted in increasing values of L1 just for graphical representation.

Non numeric variables are grouped randomly unless they appear specified in the grouping argument.

A plot method exists for the returned object.

Value

An invisible object of class L1profile which contains a named list of coarsenings and values of the L1 measure for each coarsening.

Author(s)

Stefano Iacus, Gary King, and Giuseppe Porro

References

Iacus, King, Porro (2011) doi: 10.1198/jasa.2011.tm09599

Iacus, King, Porro (2012) doi: 10.1093/pan/mpr013

Iacus, King, Porro (2019) doi: 10.1017/pan.2018.29

Examples

set.seed(123)
data(LL)
for(i in c(4:6,10:12))
 LL[[i]] <- factor(LL[[i]])

imb0 <- L1.profile(LL$treated,LL, drop=c("treated","re78"))

if(require(MatchIt)){
 m2 <- matchit(treated ~ black + hispanic + married + nodegree + 
  u74 + u75 + education + age + re74 + re75, data=LL, 
  distance="logit")

 m3 <- try(matchit(treated ~ black + hispanic + married + nodegree + 
  u74 + u75 + education + age + re74 + re75, data=LL, 
  distance="mahalanobis"), TRUE)
 
 L1.profile(LL$treated,LL, drop=c("treated","re78"), 
  weights=m2$w, add=TRUE, col="green", lty=2, useCP=imb0$CP)

 if(class(m3)[1]!="try-error"){
   L1.profile(LL$treated,LL, drop=c("treated","re78"), 
    weights=m3$w, add=TRUE, col="orange", lty=3, useCP=imb0$CP)
    }
}

m1 <- cem("treated", LL, drop="re78")

L1.profile(LL$treated,LL, drop=c("treated","re78"), 
 weights=m1$w>0, add=TRUE, col="blue", lty=4, useCP=imb0$CP)

legend(5, 0.9, legend=c("raw data", "pscore", "mahalanobis", "cem"), 
 lty=1:4, col=c("red", "green", "orange", "blue"))

cem documentation built on Sept. 8, 2022, 5:09 p.m.