Description Usage Arguments Details Value Note Author(s) References See Also Examples
Nash Optimal Party Positions
1 2 3 4 |
start |
initial party positions. Numerical vector. Optional. |
model |
the |
data |
the data set |
tolerance |
tolerance in the convergence of Nash equilibrium. Default |
max.iter |
max iteration to convergence in Nash equilibrium. Default |
coal |
a |
alpha |
the weight of coalition vote-share in party utility function. Default = 0. See Details. |
margin |
a |
fixed |
a |
gamma |
the weight among nash and fixed arty position. Default=0. See Details. |
boot |
number of boostrap replications. See Details. |
MC |
number of Monte Carlo replications. See Details. |
self.var |
|
prox.var |
|
position |
a named |
votes |
a named |
quadratic |
a logical value: if |
See vignette.
an object of class nash.eq
See the vignette for detailed explanations and other working examples.
Luigi Curini, Stefano M. Iacus
Adams, James F., Samuel Merrill III, and Bernard Grofman (2005). A Unified Theory of Party Competition. Cambridge: Cambridge University Press
Merrill, Samuel III, and James Adams (2001), Computing Nash Equilibria in Probabilistic, Multiparty Spatial Models with Nonpolicy Components, Political Analysis, 9, 347–61
See Also as plot.nash.eq
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | ## Not run:
data(italy2006)
str(italy2006)
italy2006[1:2,1:14]
election <- mlogit.data(italy2006 , shape="wide", choice="vote",
varying=c(5:14), sep="_")
str(election)
m <- mlogit(vote~prox+partyID | gov_perf+sex+age+education,
election, reflevel = "UL")
summary(m)
true.pos <- list(FI=7.59, UL=3.50, RC=1.95, AN=8.08, UDC=5.66)
true.votes <- list(FI=.24, UL=.40, RC=.10, AN=.18, UDC=.08)
# model 1: comparison against true votes and party positions
nash.eq <- equilibrium(model=m, data=election, pos=true.pos,
votes=true.votes)
nash.eq
par(mfrow=c(3,1))
plot(nash.eq)
par(mfrow=c(1,1))
# model 2: colation behaviours
coal1 <- list(FI=1, UL=2, RC=2, AN=1, UDC=1)
alpha1 <- list(FI=0.5, UL=0.5, RC=0.5, AN=0.5, UDC=0.5)
nash.eq <- equilibrium(model=m, data=election, coal=coal1,
alpha=alpha1)
nash.eq
# model 3: colation behaviours
coal1 <- list(FI=1, UL=2, RC=2, AN=1, UDC=1)
alpha1 <- list(FI=0.7, UL=0.8, RC=0.1, AN=0.5, UDC=0.9)
nash.eq <- equilibrium(model=m, data=election, coal=coal1,
alpha=alpha1)
nash.eq
# model 4: rivals tends to separate each other
nash.eq <- equilibrium(model=m, data=election, margin=list(FI="UL", UL="FI"))
nash.eq
# model 5: fixed position averaged with Nash equilibrium solution
nash.eq <- equilibrium(model=m, data=election, fixed=list(RC=1), gamma=0.2)
nash.eq
# model 6: rivals tends to separate each other with
# fixed position averaged with Nash equilibrium solution
nash.eq <- equilibrium(model=m, data=election,
margin=list(FI="UL", UL="FI"), fixed=list(RC=1), gamma=0.2)
nash.eq
# model 7: coalition and fixed position averaged with
# Nash equilibrium solution
coal1 <- list(FI=1, UL=2, RC=2, AN=1, UDC=1)
alpha1 <- list(FI=0.7, UL=0.8, RC=0.5, AN=0.5, UDC=0.5)
nash.eq <- equilibrium(model=m, data=election, coal=coal1,
alpha=alpha1, fixed=list(RC=1), gamma=0.2)
nash.eq
# model 8: Bootstrap analysis
nash.eq <- equilibrium(model=m, data=election, boot=10)
nash.eq
# model 9: Monte Carlo simulation
nash.eq <- equilibrium(model=m, data=election, MC=10)
nash.eq
## End(Not run)
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