the Matching() package by Jasjeet Sekhon does propensity score matching in a
very user friendly way. (as you said you don't want to reinvent the wheel...)
just feed it with the fitted values from a glm model (fitted$myglmmodel).
afaik, you may additionally match on some covariates directly.
HTH
m
ran2 wrote:
Frank E Harrell Jr wrote:
That is a high variance procedure as compared with covariate adjustment
using the propensity score, or stratification.
Frank Harrell
Ah, wait what if I got very high dimensional X ? Even with 20 binary
covariates i would end up with more than 1 mil
Ben Domingue wrote:
>
> I'm not quite sure what you mean. If all you need is propensity
> scores to run an IPW analysis, the fitted values should work. Having
> many binary covariates shouldn't be a problem, the whole point of the
> propensity score is boiling down many dimensions to a single
I'm not quite sure what you mean. If all you need is propensity
scores to run an IPW analysis, the fitted values should work. Having
many binary covariates shouldn't be a problem, the whole point of the
propensity score is boiling down many dimensions to a single one.
I use matchit() for my psm n
Frank E Harrell Jr wrote:
>
>
>
> That is a high variance procedure as compared with covariate adjustment
> using the propensity score, or stratification.
>
> Frank Harrell
>
Ah, wait what if I got very high dimensional X ? Even with 20 binary
covariates i would end up with more than 1 m
Frank E Harrell Jr wrote:
>
> That is a high variance procedure as compared with covariate adjustment
> using the propensity score, or stratification.
>
> Frank Harrell
>
Yes, I guess the foo$fitted.values was the syntax i missed. I know this
method is not optimal and that it yields high va
Bunny, lautloscrew.com wrote:
Hi all,
i am looking to built a simple example of a very basic propensity score
adjustment, just using the estimated propensity scores as inverse
probability weights (respectively 1-estimated weights for the
non-treated). As far as i understood, MLE predictions o
Bunny, lautloscrew.com lautloscrew.com> writes:
ix of some covariates.
>
> I wonder right now if te glm respectively summary(glm(...)) puts out
> something comparable to ML estimates that can be used as the estimated
> pscores, in such a way that there is one value for every observation.
>
Hi all,
i am looking to built a simple example of a very basic propensity
score adjustment, just using the estimated propensity scores as
inverse probability weights (respectively 1-estimated weights for the
non-treated). As far as i understood, MLE predictions of a logit model
can direct
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