Posetic Tools in the Social Sciences: A Tutorial Exposition
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AT
BE
BU
HR
CY
CZ
DK
EE
FI
FR
DE
GR
HU
IE
IT
LV
LT
LU
MT
NL
PO
PT
RO
SK
SI
ES
SE
UK
Fig. 1 Hasse diagram for the data reported in Table 1
library(parsec)
% data <- data.frame(
% "GDP per inhabitant" = c(38100, 35000, ... ),
% "deficit/surplus" = c(-0.8, -0.9, ...),
% "gross debt" = c(-78.3, -103.4, ...)
% )
% rowames(data) <- c("AT", "BE", ...)
% The above instructions are commented,
% since data are to be completed according to Table 1
X <- rownames(data)
r <- function(x, y) all(data[x,] <= data[y,])
r <- Vectorize(r)
Z <- outer(X, X, FUN = r)
dimnames(Z) <- list(X, X)
% Function Vectorize() produces a wrapper of r
% so as to pass elements one-by-one in the
computation of Z
Z <- validate.partialorder.incidence(Z) % checking
whether Z
% actually represents a partial order relation
M <- MRP(Z) % MRP matrix computation
(function MRP depends upon package netrankr Schoch (2017) that provides
different approaches to evaluate the MRP matrix, namely exact, sampled and