Dear all,
I intend to use a matching procedure on a project where, because of the nature of my cases, weights of control units should be all kept at 1. Reading the manuals and articles I realized that the way to do it was using CEM and applying the function k2k.However, I couldn't get it to work as described. When I run the command matchit(x, ..., k2k=TRUE), it returns a matrix that, when extracted into a data-frame, still contains weights and a different number of treated and control units - even though no error messages are shown. This happens both to my data and when running the lalonde demo script. The result of the matching is exactly the same as that obtained when k2k=FALSE. I tried to go back to the "cem" package and run the demo script there on LL with k2k - using first the command cem(x,... k2k=TRUE) and also doing the matching first and applying k2k(cem.match,...) afterwards. In both cases there's the error message "In min(x, na.rm = TRUE) : no non-missing arguments to min; returning Inf", and the resulting matrix is still exactly the same as that obtained without setting k2k to TRUE.
Thank you in advance for the help.
Best regards,
Bruno Castanho e Silva
Dear all,
I am having problems with the optimal method and the use of a caliper.
What is happening is, despite my use of a caliper, and despite matchit recognising that it can't match every "Treated" with a "Control", no "Treated" observations are discarded.
For example, my syntax:
#--
>out<-matchit( formula, data=dat.final, method = "optimal", distance="linear.logit", ratio=1,caliper=0.1, m.order="random")
Warning messages:
1: In fullmatch(d, min.controls = ratio, max.controls = ratio, omit.fraction = (n0 - :
Without 'data' argument the order of the match is not guaranteed
to be the same as your original data.
2: In fullmatch.matrix(d, min.controls = ratio, max.controls = ratio, :
The problem is infeasible with the given constraints; some units were omitted to allow a match.
>summary(out)
Sample sizes:
Control Treated
All 5783 677
Matched 677 677
Unmatched 5106 0
Discarded 0 0
#--
Let me just reiterate that I am fine with some treated being omitted, but none are despite the warning suggesting some are omitted.
When I look at the match.matrix and extract the pairs, I can see that the difference in distances for some pairs are much greater than the 0.1*sd(distance measure)
I have also tried changing the option "omit.fraction" with no success. Also choosing discard="control" also does not work.
Am I doing something incorrect or is there a problem?
I have also noticed in the documentation that when method="nearest" and caliper!=0 then the documentation says (pg 26): "if a caliper is specified, a control unit within the caliper for a treated unit is randomly selected as the match for that treated unit"
Is this correct? This would seem to defeat the purpose of nearest neighbour matching. For instance, if my caliper was large, and almost all distances fell within the caliper then I am just randomly matching. Do they instead mean: if a caliper was specified, then the closest control unit within the caliper is selected as the match for that treated unit.
I also get problems when I use distance="linear.logit", method="nearest", m.order="random", with caliper=0.2 (I am trying to find 1:1 matches for 300 treated from 1600 controls). I also set verbose=TRUE to see what is going on.
The error that comes up is:
Error in matchedc[goodmatch == clabels] <- itert :
replacement has length zero
But also with verbose=TRUE
Nearest neighbor matching...
Matching Treated: 10%...20%...30%...40%...50%...60%...70%...80%...90%...100%...
When I calculate the logit.linear distance myself using a glm and plot the logit(propensity score) there is a large overlap. Rough calculations suggest I would immediately be unable to find matches for 3, but it is possible to find matches for all others. To add an extra level, it works for some random seeds, not others, and it works for all cases if I use m.order="largest".
To confuse the situation more, the distance "logit" works fine over 10000 simulations in 8 different scenarios!
In summary, there are many bugs. Or am I missing something??
Best,
Leesa.
Dear all,
I am having problems with the optimal method and the use of a caliper.
What is happening is, despite my use of a caliper, and despite matchit recognising that it can't match every "Treated" with a "Control", no "Treated" observations are discarded.
For example, my syntax:
#--
>out<-matchit( formula, data=dat.final, method = "optimal", distance="linear.logit", ratio=1,caliper=0.1, m.order="random")
Warning messages:
1: In fullmatch(d, min.controls = ratio, max.controls = ratio, omit.fraction = (n0 - :
Without 'data' argument the order of the match is not guaranteed
to be the same as your original data.
2: In fullmatch.matrix(d, min.controls = ratio, max.controls = ratio, :
The problem is infeasible with the given constraints; some units were omitted to allow a match.
>summary(out)
Sample sizes:
Control Treated
All 5783 677
Matched 677 677
Unmatched 5106 0
Discarded 0 0
#--
Let me just reiterate that I am fine with some treated being omitted, but none are despite the warning suggesting some are omitted.
When I look at the match.matrix and extract the pairs, I can see that the difference in distances for some pairs are much greater than the 0.1*sd(distance measure)
I have also tried changing the option "omit.fraction" with no success. Also choosing discard="control" also does not work.
Am I doing something incorrect or is there a problem?
I have also noticed in the documentation that when method="nearest" and caliper!=0 then the documentation says (pg 26): "if a caliper is specified, a control
unit within the caliper for a treated unit is randomly selected as the match for that treated unit"
Is this correct? This would seem to defeat the purpose of nearest neighbour matching. For instance, if my caliper was large, and almost all distances fell within the caliper then I am just randomly matching. Do you instead mean: if a caliper was specified, then the closest control unit within the caliper is selected as the match for that treated unit.
Best,
Leesa.
