lphom_dual: Implements lphom_dual algorithm

In lphom: Ecological Inference by Linear Programming under Homogeneity

Implements lphom_dual algorithm

Description

Estimates RxC vote transfer matrices (ecological contingency tables) with lphom_dual

Usage

lphom_dual(
  votes_election1,
  votes_election2,
  integers = FALSE,
  solver = "lp_solve",
  integers.solver = "symphony",
  ...
)

Arguments

votes_election1	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.
votes_election2	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. In general, The sum by rows of votes_election1 and votes_election2 must coincide.
integers	A TRUE/FALSE value that indicates whether the LP solution of counts (votes) must be approximate to the closest integer solution using ILP. Default, FALSE.
solver	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.
integers.solver	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.

Value

A list with the following components

VTM.votes.w	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.
VTM.votes.a	The matrix of order JxK with the estimated cross-distribution of votes of elections 1 and 2, attained simple averaging the two dual solutions.
HETe.w	Estimated heterogeneity index associated to the VTM.votes.w solution.
HETe.a	Estimated heterogeneity index associated to the VTM.votes.a solution.
VTM12.w	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.
VTM21.w	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.
VTM12.a	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.
VTM21.a	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.
lphom.object.12	The output of the lphom function attained solving the problem X –> Y. That is, mapping votes_election1 to rows and votes_election2 to columns.
lphom.object.21	The output of the lphom function attained solving the problem Y –> X. That is, mapping votes_election2 to rows and votes_election1 to columns.
inputs	A list containing all the objects with the values used as arguments by the function.

Author(s)

Jose M. Pavia, [e-mail]

Rafael Romero [e-mail]

References

Pavia, JM and Romero, R (2021). Symmetry estimating R×C vote transfer matrices from aggregate data, mimeo.

See Also

lphom tslphom_dual nslphom_dual lphom_joint tslphom_joint nslphom_joint

Other linear programing ecological inference functions: lclphom(), lp_apriori(), lphom_joint(), lphom(), nslphom_dual(), nslphom_joint(), nslphom(), tslphom_dual(), tslphom_joint(), tslphom()

Examples

x <- France2017P[, 1:8]
y <- France2017P[, 9:12]
y[,1] <- y[,1]  - (rowSums(y) - rowSums(x))
mt <- lphom_dual(x, y)
mt$VTM.votes.w
mt$HETe.w

lphom documentation built on March 21, 2022, 9:09 a.m.