relax.cem | R Documentation |
Diagnostic tools for CEM
relax.cem(obj, data, depth=1, verbose = 1, L1.breaks=NULL, plot=TRUE, fixed=NULL, shifts=NULL, minimal=NULL, use.coarsened=TRUE, eval.imbalance=TRUE, use.weights=FALSE, ...) relax.plot(tab, group="1", max.terms=50, perc=.5, unique=FALSE, colors=TRUE)
obj |
an object of class cem. |
data |
the original data. |
verbose |
controls the level of verbosity. |
L1.breaks |
list of cutpoints for the calculation of the L1 measure. |
plot |
plot the solutions? |
tab |
the output table from |
fixed |
vector of variable names which will not be relaxed. |
max.terms |
plot only the last best results of |
shifts |
a vector of proportions of shifts. |
minimal |
the minimal number of intervals acceptable after relaxation. Should be a nameed list of positive integers. |
group |
character string denoting group id. Defaults to |
perc |
only plot if percentage of matched units is greater than |
unique |
only plot different solutions (in terms of matched units). |
depth |
if 1, relaxes up to dropping one var, if 2 relaxes (up to dropping) two vars, etc. |
use.coarsened |
used coarsened values for continuous variables. |
colors |
If |
eval.imbalance |
If |
use.weights |
If |
... |
passed to the |
relax.cem
starts from a cem solution (as given by
cem
) which has to be run with argument keep.all=TRUE
.
relax.cem
tries several relaxed coarsenings on the
variables. Coarsenings corresponds to dividing the support of each
variable into a decreasing number of intervals of the same length (even
if in the starting solution intervals are of different lengths).
Because CEM is MIB, the number of matched units increases as the
number of intervals decrease. All variables are coarsened into
k
intervals along a sequence which starts from the original
number of intervals and decreases to 10 intervals by 2,
then continues from 10 down to 1 intervals by 1. If
minimal
is specified, variables are coarsened down to that
minimal value.
To observe MIB property of CEM use.coarsened
(default) should be
set to TRUE
; otherwise the coarsening of the continuous variable
will be recalculated at each iteration and there is no guarantee of
monotonicity.
relax.cem
outputs a list of tables. Each table is named
Ggroup
where group
is the id of the group. Each
Ggroup
table is ordered in increasing order of matched units of
group group
. Columns PercGgroup
and Ggroup
report
percentage and absolute number of matched units for each group
.
Column Relaxed
indicates which relaxation has been done, with
something like "V1(4), V3(5)"
, which means "variable V1
has been split in 4 intervals of the same length and variable V3
into five intervals". Thus, the number of intervals is reported in
parenthases and if equal to 1 means that the corresponding variable is
excluded from affecting the match (i.e. all observations are assigned to
the same interval).
If shifts
is not null, each coarsening is shifted accordingly
(see shift.cem
for additional details). In case of
shifting “S:
” appears in the labels.
The relax.plot
, plot all the different relaxation in increasing
order of number of treated units matched. For each coarsening it also reports
the value of the L1 measure. The table generated by relax.cem
may
contain many entries. By default, only a portion of best coarsenings
are plotted (option max.terms
). In addition, the user can specify
to plot the corasening for which at least a certain percentage of
treated units have been matched (option perc
, by default 50
In addition, of several different coarsenings which lead to the same number of treated
units matched, the user can specify to plot only one of them using the
option unique
= TRUE
(default).
If L1.breaks
are NULL
they are taken from the cem
object
if available or calculated atumatically as in cem
.
Calling directly plot
on the output of cem.relax
has the same
effect of calling directly relax.plot
.
tab |
an invisible object containing the tabs and the L1breaks used |
Stefano Iacus, Gary King, and Giuseppe Porro
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
cem
data(LL) set.seed(123) mat <- cem(treatment="treated",data=LL, drop="re78", keep.all=TRUE) mat tab <- relax.cem(mat, LL, depth=1, plot=FALSE) relax.plot(tab, group="1") plot(tab, group="1") relax.plot(tab, group="1", unique=TRUE) relax.plot(tab, group="1", perc=0.6) relax.plot(tab, group="1", perc=0.6,unique=TRUE) tab1 <- relax.cem(mat, LL, depth=1, minimal=list(re74=6, age=3, education=3, re75=5)) tab2 <- relax.cem(mat, LL, depth=1, minimal=list(re74=6, age=3, education=3, re75=5), shifts=0.01) tab3 <- relax.cem(mat, LL, depth=1, minimal=list(age=3, education=3), fixed=c("re74","re75")) tab4 <- relax.cem(mat, LL, depth=2, minimal=list(age=4, education=3,re75=6),plot=FALSE, fixed="re74") relax.plot(tab4) relax.plot(tab4, unique=TRUE) relax.plot(tab4, perc=0.7)
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