Estimates RxC vote transfer matrices (ecological contingency tables) with tslphom_dual
tslphom_dual(
votes_election1,
votes_election2,
integers = FALSE,
solver = "lp_solve",
integers.solver = "symphony",
...
)A list with the following components
The matrix of order JxK with the estimated cross-distribution of votes of elections 1 and 2, attained weighting the two dual solutions using as weights the corresponding HTEe estimates.
The array of order JxKxI with the local estimated cross-distributions of votes of elections 1 and 2 by unit, attained weighting the two dual solutions using as weights the corresponding HTEe estimates.
The matrix of order JxK with the estimated cross-distribution of votes of elections 1 and 2, attained simple averaging the two dual solutions.
The matrix of order JxKxI with the estimated cross-distributions of votes of elections 1 and 2 by unit, attained weighting the two dual solutions using as weights the corresponding HTEe estimates.
Estimated heterogeneity index associated to the VTM.votes.w solution.
Estimated heterogeneity index associated to the VTM.votes.a solution.
The matrix of order JxK with the estimated row-standardized proportions of vote transitions from election 1
to election 2 associated to the VTM.votes.w solution.
The matrix of order KxJ with the estimated row-standardized proportions of vote transitions from election 2
to election 1 associated to the VTM.votes.w solution.
The matrix of order JxK with the estimated row-standardized proportions of vote transitions from election 1
to election 2 associated to the VTM.votes.a solution.
The matrix of order KxJ with the estimated row-standardized proportions of vote transitions from election 2
to election 1 associated to the VTM.votes.a solution.
The output of the tslphom function attained solving the problem X --> Y,
that is, mapping votes_election1 to rows and votes_election2 to columns.
The output of the tslphom function attained solving the problem Y --> X,
that is, mapping votes_election2 to rows and votes_election1 to columns.
A list containing all the objects with the values used as arguments by the function.
data.frame (or matrix) of order IxJ with the counts to be initially
mapped to rows. When estimating vote transfer matrices, the votes gained by
the J political options competing on election 1 (or origin) in the I
territorial units considered. The sum by rows of votes_election1 and
votes_election2 must coincide.
data.frame (or matrix) of order IxK with the counts to be initially mapped
to columns. When estimating vote transfer matrices, the votes gained by
the K political options competing on election 2 (or destination) in the I
territorial units considered. The sum by rows of votes_election1 and
votes_election2 must coincide.
A TRUE/FALSE value that indicates whether the problem is solved in integer values
in both iterations: zero (lphom) and final (including unit) solutions. If TRUE, the LP matrices
are approximated to the closest integer solution solving the corresponding Integer Linear Program.
Default, FALSE.
A character string indicating the linear programming solver to be used, only
lp_solve and symphony are allowed. By default, lp_solve. The package Rsymphony
needs to be installed for the option symphony to be used.
A character string indicating the linear programming solver to be used to approximate
to the closest integer solution, only symphony and lp_solve are allowed.
By default, symphony. The package Rsymphony needs to be installed for the option symphony
to be used. Only used when integers = TRUE.
Other arguments to be passed to the function. Not currently used.
Jose M. Pavia, pavia@uv.es
Rafael Romero rromero@eio.upv.es
Pavia, JM and Romero, R (2024). Symmetry estimating RxC vote transfer matrices from aggregate data. Journal of the Royal Statistical Society, Series A – Statistics in Society, 187(4), 919-943. tools:::Rd_expr_doi("10.1093/jrsssa/qnae013")
tslphom lphom_dual nslphom_dual lphom_joint tslphom_joint nslphom_joint
Other linear programing ecological inference functions:
lclphom(),
lp_apriori(),
lphom_dual(),
lphom_joint(),
lphom(),
nslphom_dual(),
nslphom_joint(),
nslphom(),
rslphom(),
tslphom_joint(),
tslphom()
x <- France2017P[, 1:8]
y <- France2017P[, 9:12]
y[,1] <- y[,1] - (rowSums(y) - rowSums(x))
mt <- tslphom_dual(x, y)
mt$VTM.votes.w
mt$HETe.w
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