This has been replaced by revealPref
.
rAtte(
menu,
choice,
pref_list = NULL,
method = "GMS",
nCritSimu = 2000,
BARatio2MS = 0.1,
BARatio2UB = 0.1,
MNRatioGMS = NULL,
RAM = TRUE,
AOM = TRUE,
limDataCorr = TRUE,
attBinary = 1
)
Summary statistics, generated by sumData
.
Matrices of constraints, generated by genMat
.
Test statistic.
Critical values.
P-values (only available for GMS
, LF
and PI
).
Method for constructing critical value.
Numeric matrix of 0s and 1s, the collection of choice problems.
Numeric matrix of 0s and 1s, the collection of choices.
Numeric matrix, each row corresponds to one preference. For example, c(2, 3, 1)
means
2 is preferred to 3 and to 1. When set to NULL
, the default, c(1, 2, 3, ...)
,
will be used.
String, the method for constructing critical values. Default is GMS
(generalized moment selection).
Other available options are LF
(least favorable model), PI
(plug-in method), 2MS
(two-step moment selection),
2UB
(two-step moment upper bound), or ALL
(report all critical values).
Integer, number of simulations used to construct the critical value. Default is 2000
.
Numeric, beta-to-alpha ratio for two-step moment selection method. Default is 0.1
.
Numeric, beta-to-alpha ratio for two-step moment upper bound method. Default is 0.1
.
Numeric, shrinkage parameter. Default is sqrt(1/log(N))
, where N is the sample size.
Boolean, whether the restrictions implied by the RAM of
Cattaneo et al. (2020) should be incorporated, that is, their monotonic attention assumption (default is TRUE
).
Boolean, whether the restrictions implied by the AOM of
Cattaneo et al. (2022) should be incorporated, that is, their attention overload assumption (default is TRUE
).
Boolean, whether assuming limited data (default is TRUE
). When set to
FALSE
, will assume all choice problems are observed. This option only applies when RAM
is set to TRUE
.
Numeric, between 1/2 and 1 (default is 1
), whether additional restrictions (on the attention rule)
should be imposed for binary choice problems (i.e., attentive at binaries).
Matias D. Cattaneo, Princeton University. cattaneo@princeton.edu.
Paul Cheung, University of Maryland. hycheung@umd.edu
Xinwei Ma (maintainer), University of California San Diego. x1ma@ucsd.edu
Yusufcan Masatlioglu, University of Maryland. yusufcan@umd.edu
Elchin Suleymanov, Purdue University. esuleyma@purdue.edu
M. D. Cattaneo, X. Ma, Y. Masatlioglu, and E. Suleymanov (2020). A Random Attention Model. Journal of Political Economy 128(7): 2796-2836. tools:::Rd_expr_doi("10.1086/706861")
M. D. Cattaneo, P. Cheung, X. Ma, and Y. Masatlioglu (2022). Attention Overload. Working paper.