genMat
generates constraint matrices for a range of preference orderings according to
(i) the monotonic attention assumption proposed by Cattaneo, Ma, Masatlioglu, and Suleymanov (2020),
(ii) the attention overload assumption proposed by Cattaneo, Cheung, Ma, and Masatlioglu (2021),
and (iii) the attentive-at-binaries restriction.
This function is embedded in revealPref
.
genMat(
sumMenu,
sumMsize,
pref_list = NULL,
RAM = TRUE,
AOM = TRUE,
limDataCorr = TRUE,
attBinary = 1
)
Matrices of constraints, stacked vertically.
The number of constraints for each preference, used to extract from R
individual matrices of constraints.
Numeric matrix, summary of choice problems, returned by sumData
.
Numeric matrix, summary of choice problem sizes, returned by sumData
.
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.
Boolean, whether the restrictions implied by the random attention model of
Cattaneo, Ma, Masatlioglu, and Suleymanov (2020) should be incorporated, that is, their monotonic attention assumption (default is TRUE
).
Boolean, whether the restrictions implied by the attention overload model of
Cattaneo, Cheung, Ma, and Masatlioglu (2021) 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.
# Load data
data(ramdata)
# Generate summary statistics
summaryStats <- sumData(ramdata$menu, ramdata$choice)
# Generate constraint matrices
constraints <- genMat(summaryStats$sumMenu, summaryStats$sumMsize)
constraints$ConstN
constraints$R[1:10, 1:10]
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