If a users preferred data analysis software is other than R, Optmatch can still easily be used to perform the matching while all other data analysis can be performed in the preferred software.
In general, the procedure will be
The two best ways to import the data back-and-forth are either using
comma separated value files (.csv files) or using the R package foreign.
For .csv files, sample R code may be
> externaldata <- read.csv("externaldata.csv", header=TRUE)
> externaldata$match <- fullmatch(..., data=externaldata)
> write.csv(externaldata, file="externaldata.matched.csv")
Examples of the use of foreign are included within the
software-specific sections below.
For this example, lets say we have some simple demographics. We will treat gender as the treatment indicator, and wish to match on a combination of a propensity score for gender (using both age and height) and age.
data people;
infile datalines dsd dlm=' ' missover;
input gender age height;
datalines;
0 25 62
0 41 68
0 38 63
0 22 62
1 33 70
1 35 71
1 47 68
1 23 64
;
run;
Now we can fit a logistic model to predict gender using age and height.
proc logistic data = people;
model gender (event='1') = age height;
output out = preddata p=ppty;
run;
Finally, since we want to match only on the new ppty propensity
score and age, we can drop height.
proc data newpeople;
set preddata;
keep gender age ppty;
run;
With this setup, we can now either pass it to R via a .csv file, or directly using the foreign package in R.
Save the file from SAS.
proc export data=newpeople;
outfile="C:\Users\myuser\Desktop\sasout.csv";
run;
Inside R, we can load this data.
> sasdata <- read.csv("C:/Users/myuser/Desktop/sasout.csv", header=TRUE)
(Depending on your version of Windows, you may need to use
> sasdata <- read.csv("C:\\Users\\myuser\\Desktop\\sasout.csv", header=TRUE)
instead.)
If you have string variables (e.g. race as “White”, “Hispanic”, etc),
you may need to include the argument stringsAsFactors=FALSE.
Now, perform matching as desired, saving the final match to
sasdata. For example,
> library(optmatch)
> f <- fullmatch(gender ~ age + ppty, data=sasdata)
> sasdata$match <- f
Save this data back to .csv as follows.
> write.csv(sasdata, "C:/Users/myuser/Desktop/rout.sas.csv", row.names=FALSE)
The use of row.names=FALSE stops R from including the row names
(likely 1, 2, 3, etc) as the first column in the data. If you
re-arranged the data at any point, you may need to set that to TRUE,
but keep in mind to handle it properly in SAS, as the default will be
to treat it as a variable.
Now, returning to SAS, we can read the new rout.sas.csv file in. The only
catch is that we want to ensure that the match is read as a string by
using $, since it may have values like 1.1 and 1.10,
representing two different matches, but which are identical if treated
as numeric.
data matchedpeople;
infile "C:/Users/myuser/Desktop/rout.sas.csv" dsd firstobs=2;
input gender age ppty match $;
run;
The argument firstobs=2 skips the variable names; alternatively you
could pass col.names=FALSE to R's write.csv, but then the
rout.sas.csv file lacks any variable information, which may be useful
to have.
As an alternative to using R's write.csv, you can use the R package
foreign to generate both the data and SAS code needed.
> library(foreign)
> write.foreign(sasdata, 'C:/Users/myuser/Desktop/rout.sas.txt',
+ 'C:/Users/myuser/Desktop/rout.code.sas', package = 'SAS')
Opening the rout.code.sas file will give you the SAS code to read in the data.
For this example, we will start with the built-in auto data set in Stata.
sysuse auto.dta
We will treat foreign, whether a car is domestic or foreign, as the
treatment indicator. (Not to be confused with the R package foreign!)
We will estimate propensity scores using all other variables
(excluding make which is unique per row), and wish to match on the
estimated propensity score as well as price and mpg.
First, lets fit the logistic regression model.
logit foreign price mpg rep78 headroom trunk weight length turn displacement gear_ratio
predict ppty
Now, we can export a .csv file. Note that in addition to the treatment
indicator and variables to match on, we need to include a unique
identifier. In this case, we can use make. If no such identifier exists, you can use
gen case_id = _n
to generate ID's 1, 2, etc. These will be needed to merge the match information back in.
outsheet make foreign price mpg ppty using C:\Users\myuser\Desktop\stataout.csv, comma
Turning to R, this can be read in similar to the SAS example, using
> statadata <- read.csv("C:/Users/myuser/Desktop/stataout.csv", header=TRUE)
(Again, depending on your version of Windows, you may need to use
> statadata <- read.csv("C:\\Users\\myuser\\Desktop\\stataout.csv", header=TRUE)
instead.)
If you have string variables (e.g. race as “White”, “Hispanic”, etc),
you may need to include the argument stringsAsFactors=FALSE.
Now, perform matching as desired, saving the final match to
statadata. For example,
> library(optmatch)
> f <- fullmatch(foreign ~ price + mpg + ppty, data=statadata, max.controls=3)
> statadata$match <- f
We do not recommend using .csv files to transfer the data back to
Stata, though the write.csv file would be similar to that for
SAS. Instead, we recommend using foreign.
> write.dta(statadata, "C:/Users/myuser/Desktop/rout.stata.dta")
Back in Stata, you can merge this into the existing auto.dta by the
following commands.
sort make
merge 1:1 make using "C:/Users/myuser/Desktop/rout.stata.dta", force
The force option is necessary to overcome type
differences. Additional tweaks may be necessary here if you have
special variable types.