L1.profile {cem}R Documentation

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=6, ref.cut=3, 
 weights, plot=TRUE, add=FALSE, col="red", lty=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
ref.cut reference number of cut points per variable
weights weights
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

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 very wide 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.)

This function first tries to move the cutpoints of the support of all variables together from min.cut to max.cut. Then, fixing a coarsening into ref.cut number of cut points on all variables, it changes the coarsening one variable at time, again from min.cut to max.cut.

Non numeric variables are not grouped, i.e. no coarsening occurs.

A plot method exists for the returned object.

Value

An invisible object of class L1profile which contains a named list of coarsening.

Author(s)

Stefano Iacus, Gary King, and Giuseppe Porro

References

Stefano Iacus, Gary King, Giuseppe Porro, ``Matching for Casual Inference Without Balance Checking: Coarsened Exact Matching,'' http://gking.harvard.edu/files/abs/cem-abs.shtml

Examples

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

L1.profile(LL$treated,LL, drop=c("treated","re78"), max.cut=10)

if(require(MatchIt)){
 m2 <- matchit(treated ~ black + hispanic + married + nodegree + u74 + u75 + education +
  age + re74 + re75, data=LL, distance="logit")
 
 L1.profile(LL$treated,LL, drop=c("treated","re78"), max.cut=10, 
  weights=m2$w, add=TRUE, col="green", lty=2)
}

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

L1.profile(LL$treated,LL, drop=c("treated","re78"), max.cut=10, 
 weights=m1$w, add=TRUE, col="blue", lty=3)

legend(5, 0.9, legend=c("raw data", "pscore", "cem"), lty=1:3, col=c("red", "green", "blue"))

[Package cem version 1.0.120 Index]