Hi, I have a basic question for the use and interpretation of propensity
score (ps)
When I add de ps lika an independent variable for the multivariate
regression model, when can I translate its contribution? Must I use the
treatment and the ps in the same model and the rest coavariables using for
the ps estimation? If ps is significative in all model of multivariate
regression, it´s ps a confounding variable?
Thanks
Manuela
Hey all-
I've been having some trouble explaining, in plain English, where the
weight in full matching comes from and how it incorporates the
matching process into the final regression analysis. Any advice on
language would be greatly appreciated.
Thanks much,
-c
--
Casey A. Klofstad
University of Miami
Department of Political Science
Coral Gables, FL
klofstad(a)gmail.com
http://www.as.miami.edu/personal/cklofstad/
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Hey all-
I have two variables that I want to match on. Ideally, I'd like to
combine both treatment variables in the same model. Is there anyway to
do this? Perhaps by multiplying the weights from each match to get a
single summary weight?
Best,
-c
--
Casey A. Klofstad
University of Miami
Department of Political Science
Coral Gables, FL
klofstad(a)gmail.com
http://www.as.miami.edu/personal/cklofstad/
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MatchIt Software and Documentation: http://gking.harvard.edu/matchit/
Dear colleagues,
I am using MatchIt for my project. Cfst_std is a treatment variable. We want
to use “exact” matching in gender and use “nearest” matching for other
variables. However, I got the error message: Error in
weights.matrix(match.matrix, treat, discarded). What does this mean? And how
do I fix this issue?
Thanks so much,
> cfst_std <-yu$cfst_std
> table (cfst_std)
cfst_std
0=no 1=yes
25034 3074
> summary (yu$boy)
0=no 1=yes
14042 14066
>
>m.out <- matchit(cfst_std~math_s+old_grade, data=yu, exact=c("boy"),
method="nearest")
Error in weights.matrix(match.matrix, treat, discarded) :
No units were matched
In addition: Warning messages:
1: In max(pscore[treat == 0]) :
no non-missing arguments to max; returning -Inf
2: In max(pscore[treat == 1]) :
no non-missing arguments to max; returning -Inf
3: In min(pscore[treat == 0]) :
no non-missing arguments to min; returning Inf
4: In min(pscore[treat == 1]) :
no non-missing arguments to min; returning Inf
Yu
Dear colleagues,
I encountered a problem in using matchit; more particular in the
combination of matching using propensity scores including a caliper
score based on the mahalanobis distance. Below you find a fragment of my
R-output:
Problem:
> panel <- read.table("G:/data 25-03-09 na norm alleen catiwapi.dat",
header=T, quote="", dec=".")
> dim(panel)
[1] 4032 19
> tevredenm.out.cal <- matchit(catiwapi~
inkomen+stedelijk+V66+V63+V61+job+livalone
+ +zorgen+V44+V61*V63+V61*livalone+V66*job+
+ V61*job+V63*V61,data=panel,distance="mahalanobis",
+ mahvars = c("V61", "V66", "zorgen"), caliper=0.0005)
Error: subscript out of bounds
I found this error, despite many efforts to solve it. Apparantly, R
cannot extract the individual variables from the data matrix (despite
the fact I atta
In the end I found one workaround which didn't produce the error, but I
have no clue why this is. Did anyone else encounter this particular
error?
Solved by:
> panels <- read.table("G:/data 25-03-09 na norm alleen catiwapi.dat",
header=T, quote="", dec=".")
> panel <- panels[1:4032,1:19]
> tevredenm.out.cal <- matchit(catiwapi~
inkomen+stedelijk+V66+V63+V61+job+livalone
+ +zorgen+V44+V61*V63+V61*livalone+V66*job+
+ V61*job+V63*V61,data=panel,distance="mahalanobis",
+ mahvars = c("V61", "V66", "zorgen"), caliper=0.0005)
works as I want it
warm greeting from the Netherlands,
Peter Lugtig
Methoden en technieken/ methods and statistics
Utrecht University
