library(StratTourn) library(xtable) library(ggplot2) library(reshape2) library(googleVis) library(dplyr) library(tidyr)
setwd("D:/libraries/StratTourn") setwd("D:/libraries/StratTourn/studies") tourn = load.tournament("Tourn_trade_20141123_082001.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$other.strat %in% used.strats rd = ard[rows,] }
rd = add.other.var(rd,c("cost","type")) rd$cost.pair = paste0(rd$type,"_",rd$other.type) sd <- summarise(group_by(rd,strat,other.strat, cost.pair), diff=mean(u-other.u), mean=mean(u)) rank.strats = rank.dt$strat sd$strat = factor(sd$strat, rank.strats, ordered=TRUE) sd$other.strat = factor(sd$other.strat, rank.strats, ordered=TRUE)
ggplot(data=sd, aes(cost.pair, fill=strat, y=mean)) + geom_bar(stat="identity",position="identity") +facet_grid(other.strat~strat) + geom_hline(yintercept=0, size=0.5, col="black",alpha=0.5) cat("\n")
The plot shows the total mean utility for the cost distributions High/High, High/Low, Low/High, Low/Low respectively for each strategy pair.
ggplot(data=sd, aes(cost.pair, fill=strat, y=diff)) + geom_bar(stat="identity",position="identity") +facet_grid(other.strat~strat) + geom_hline(yintercept=0, size=0.5, col="black",alpha=0.5) cat("\n")
The plot shows the mean difference in utility for the cost distributions High/High, High/Low, Low/High, Low/Low respectively for each strategy pair.
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