library(StratTourn) library(xtable) library(ggplot2) library(reshape2) library(googleVis)
library(StratTourn) library(xtable) library(Shiny) setwd("D:/libraries/StratTourn") setwd("D:/libraries/StratTourn/studies") tourn = load.tournament("Tourn_Noisy_PD_20141110_054429.tou") # Data for each match md = tourn$dt md = add.other.var(md,c("strat","u")) md$delta.u = md$u - md$other.u # Names of all strategies strats = unique(md$strat) rank.dt = strat.rank.from.matches(md) # Data for each round file = tourn$rs.file rd = fread(file) rd = as.tbl(as.data.frame(rd)) rd = add.other.var(rd,c("strat","u")) # Names of all strategies strats = unique(rd$strat) # Perhaps select a subset of strategies used.strats = strats ard = rd if (!identical(used.strats,strats)) { rows = rd$strat %in% used.strats & rd$other.strat %in% used.strats rd = ard[rows,] }
end.t = 50 start.t = 1
td = summarise(group_by(rd,strat,other.strat,t), u=mean(u), num.obs = length(t)) t.seq = 1:min(max(td$t),end.t) tsd = do(group_by(td, strat, other.strat), get.smoothed.vals(.,xout=t.seq, xvar="t",yvar="u", wvar="num.obs", spar=0.2)) # Order strategies according to their rank rank.strats = rank.dt$strat tsd$strat = factor(tsd$strat, rank.strats, ordered=TRUE) tsd$other.strat = factor(tsd$other.strat, rank.strats, ordered=TRUE) mean.u = mean(rd$u) qplot(data=tsd, x=t, y=u, color=strat, group=strat, size=I(1), geom="line") + facet_grid(other.strat~strat) + geom_hline(yintercept=I(mean.u), size=0.5, col="black",alpha=0.5)
cat("\n")
The plot shows the average payoffs of the column strategy against the row strategy as a function of the period t.
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