tslphom_joint: Implements the tslphom_joint algorithm

In lphom: Ecological Inference by Linear Programming under Homogeneity

Implements the tslphom_joint algorithm

Description

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

Usage

tslphom_joint(
  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 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.
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	A matrix of order JxK with the estimated cross-distribution of votes of elections 1 and 2.
HETe	The estimated heterogeneity index associated to the VTM.votes solution.
VTM12	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 solution.
VTM21	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 solution.
VTM.votes.units	An array of order JxKxI with the estimated matrix of cross-distributions of votes of elections 1 and 2 attained for each unit after congruently adjusting the lphom_joint() initial estimate.
EHet12	A matrix of order IxK measuring in each unit a distance to the homogeneity hypothesis. That is, the differences under the homogeneity hypothesis between the actual recorded results and the expected results in each territorial unit for each option of election two. The matrix Eik.
EHet21	A matrix of order IxJ measuring in each unit a distance to the homogeneity hypothesis. That is, the differences under the homogeneity hypothesis between the actual recorded results and the expected results in each territorial unit for each option of election one. The matrix Eij.
inputs	A list containing all the objects with the values used as arguments by the function.
solution_init	A list with the main outputs produced by lphom_joint().

Author(s)

Jose M. Pavia, [e-mail]

Rafael Romero [e-mail]

References

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

See Also

tslphom lphom_dual tslphom_dual nslphom_dual lphom_joint nslphom_joint

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

Examples

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

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