R package for the analysis of perceptual independence using general recognition theory.
grtools provides functions for the following analyses:
- Model-based analyses of separability and independence with GRT-wIND (Soto et al., 2015) for the 2x2 identification experiment.
- Model-based analyses of separability and independence with traditional GRT models for the 2x2 identification experiment (Ashby & Soto, 2015).
- Summary statistics analysis (i.e. Kadlec's MSDA; see Kadlec & Townsend, 1992) for the 2x2 identification experiment.
- Summary statistics analysis for the 2x2 Garner filtering task (Ashby & Maddox, 1994).
A tutorial introduction to GRT analyses using grtools can be found in this arXiv paper.
Note that this package is still under development. This is a pre-release version that has not been extensively tested. We welcome your comments, feature requests, bug reports, etc.
1. Installing pre-requisites
grtools requires Rcpp to work, which in turn requires a development environment with a suitable compiler. If you already have this, go to step 2.
If you do not have a C++ compiler installed, see the Rcpp FAQ, particularly points 1.2 and 1.3. More detailed instructions on how to install the compiler can be found here for Mac OS X (this page belongs to a different project that uses the same pre-requisites as grtools; please ignore instructions to install RStan), and here for Windows.
2. Installing grtools
The easiest way to install grtools and its dependencies is using devtools. Open RStudio or R, and in the console type:
install.packages("devtools") devtools::install_github("fsotoc/grtools", dependencies="Imports")
After installation, type the following in the R console:
This will open a document that includes links to help documentation for each of the main analyses included in grtools (including examples). Sometimes the command
?grtools produces an error instead of displaying the documentation. Simply quitting and re-opening R typically solves this problem.
Ashby, F. G., & Maddox, W. T. (1994). A response time theory of separability and integrality in speeded classification. Journal of Mathematical Psychology, 38(4), 423-466.
Ashby, F. G., & Soto, F. A. (2015). Multidimensional signal detection theory. In J. R. Busemeyer, J. T. Townsend, Z. J. Wang, & A. Eidels (Eds.), Oxford handbook of computational and mathematical psychology (pp. 13-34). Oxford University Press: New York, NY.
Kadlec, H., & Townsend, J. T. (1992). Signal detection analyses of multidimensional interactions. In F. G. Ashby (Ed.), Multidimensional models of perception and cognition (pp. 181–231). Hillsdale, NJ: Erlbaum.
Soto, F. A., Musgrave, R., Vucovich, L., & Ashby, F. G. (2015). General recognition theory with individual differences: A new method for examining perceptual and decisional interactions with an application to face perception. Psychonomic Bulletin & Review, 22(1), 88-111.