R Dataset / Package mediation / boundsdata
On this Picostat.com statistics page, you will find information about the boundsdata data set which pertains to Example Data for the Design Functions. The boundsdata data set is found in the mediation R package. Try to load the boundsdata data set in R by issuing the following command at the console data("boundsdata"). This may load the data into a variable called boundsdata. If R says the boundsdata data set is not found, you can try installing the package by issuing this command install.packages("mediation") and then attempt to reload the data with library("mediation") followed by data("boundsdata"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing boundsdata at the command-line which should display the entire dataset.
If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the boundsdata R data set. The size of this file is about 14,412 bytes.
Example Data for the Design Functions
A random subsample of the simulated data used in Imai, Tingley, Yamamoto (2012). The data contains 1000 rows and 7 columns with no missing values.
A data frame containing the following variables, which are interpreted as results from a hypothetical randomized trial. See the source for a full description.
Conditioning on 'manip' = 0 will simulate a randomized trial under the single experiment design, where 'out' and 'med' equal observed outcome and mediator values, respectively.
Unconditionally, using 'out', 'med', 'ttt' and 'manip' will simulate an experiment under the parallel design.
The 'out.enc' and 'med.enc' variables represent the outcome and mediator values observed when subjects received the encouragement indicated in 'enc'. Therefore, using 'out.enc', 'med.enc', 'ttt' and 'enc' will simulate an experiment under the parallel encouragement design.
Note that all the observed responses are generated from an underlying distribution of potential outcomes and mediators (not shown in this dataset) satisfying the assumptions described in Imai, Tingley and Yamamoto (2012). The full simulation code is available as a companion replication archive for the article.
Imai, K., Tingley, D. and Yamamoto, T. (2012) Experimental Designs for Identifying Causal Mechanisms. Journal of the Royal Statistical Society, Series A (Statistics in Society)
Dataset imported from https://www.r-project.org.
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