simulations/analysis2.R

In chrisroberts2112/msProject: Functions for evaluating Rivest Estimator

#
# phi0 <- na.omit(read.csv("simulations/results_phi0d.csv"))
# phi0 <- phi0[abs(phi0$pctDiff) < 20,]
# phi0_means <- data.frame(Ni = seq(10, 1000, by = 10),
#                          Ttrue = tapply(phi0[,"Ttrue"], INDEX = factor(phi0$i), FUN = mean),
#                          That = tapply(phi0[,"That"], INDEX = factor(phi0$i), FUN = mean))
#
#
# phi1 <- na.omit(read.csv("simulations/results_phi1d.csv"))
# phi1 <- phi1[abs(phi1$pctDiff) < 20,]
# phi1_means <- data.frame(Ni = seq(10, 1000, by = 10),
#                          Ttrue = tapply(phi1[,"Ttrue"], INDEX = factor(phi1$i), FUN = mean),
#                          That = tapply(phi1[,"That"], INDEX = factor(phi1$i), FUN = mean))
#
# phi2 <- na.omit(read.csv("simulations/results_phi2d.csv"))
# phi2 <- phi2[abs(phi2$pctDiff) < 20,]
# phi2_means <- data.frame(Ni = seq(10, 1000, by = 10),
#                          Ttrue = tapply(phi2[,"Ttrue"], INDEX = factor(phi2$i), FUN = mean),
#                          That = tapply(phi2[,"That"], INDEX = factor(phi2$i), FUN = mean))
#
# phi3 <- na.omit(read.csv("simulations/results_phi3d.csv"))
# phi3 <- phi3[abs(phi3$pctDiff) < 20,]
# phi3_means <- data.frame(Ni = seq(10, 1000, by = 10),
#                          Ttrue = tapply(phi3[,"Ttrue"], INDEX = factor(phi3$i), FUN = mean),
#                          That = tapply(phi3[,"That"], INDEX = factor(phi3$i), FUN = mean))
#
# phi4 <- na.omit(read.csv("simulations/results_phi4d.csv"))
# phi4 <- phi4[abs(phi4$pctDiff) < 20,]
# phi4_means <- data.frame(Ni = seq(10, 1000, by = 10),
#                          Ttrue = tapply(phi4[,"Ttrue"], INDEX = factor(phi4$i), FUN = mean),
#                          That = tapply(phi4[,"That"], INDEX = factor(phi4$i), FUN = mean))
#
#
# phi5 <- na.omit(read.csv("simulations/results_phi5d.csv"))
# phi5 <- phi5[abs(phi5$pctDiff) < 20,]
# phi5_means <- data.frame(Ni = seq(10, 1000, by = 10),
#                          Ttrue = tapply(phi5[,"Ttrue"], INDEX = factor(phi5$i), FUN = mean),
#                          That = tapply(phi5[,"That"], INDEX = factor(phi5$i), FUN = mean))
#
# tab <- data.frame(phi = 0:5, SSE = c(
#                   sum((phi0$Ttrue - phi0$That)^2),
#                   sum((phi1$Ttrue - phi1$That)^2),
#                   sum((phi2$Ttrue - phi2$That)^2),
#                   sum((phi3$Ttrue - phi3$That)^2),
#                   sum((phi4$Ttrue - phi4$That)^2),
#                   sum((phi5$Ttrue - phi5$That)^2))
# )
#
# xtable::xtable(tab)
#
# wilcox.test(c(abs(phi0_means$That - phi0_means$Ttrue)),
#             c(abs(phi2_means$That - phi2_means$Ttrue)), paired = TRUE)
#
# mat <- matrix(NA, nrow = 6, ncol = 6)
# t(mat)
# for(i in 1:6){
#   for(j in 1:6){
#     if(i == j) next
#     mat[i,j] <- wilcox.test(c(eval(parse(text = paste0("phi", i-1, "_means$That"))) - eval(parse(text = paste0("phi", i-1, "_means$Ttrue")))),
#                             c(eval(parse(text = paste0("phi", j-1, "_means$That"))) - eval(parse(text = paste0("phi", j-1, "_means$Ttrue")))),
#                               paired = TRUE)$p.value
#   }
# }
#
# xtable::xtable(t(mat), digits = 4)
#
#
#