20130616

.Rhistory

@@ -1,38 +1,3 @@
-passTdsMultiplier <- 4
-passIntMultiplier <- -3 #-2
-rushYdsMultiplier <- 10
-rushTdsMultiplier <- 6
-recMultiplier <- 0
-recYdsMultiplier <- 8 #10
-recTdsMultiplier <- 6
-#twoPtsMultiplier <- 2
-fumbleMulitplier <- -3 #-2
-maxRisk <- 5
-numQBs <- 1
-numRBs <- 2
-numWRs <- 2
-numTEs <- 1
-numWRTEs <- 0
-numWRRBs <- 0
-numWRRBTEs <- 1
-numQBWRRBTEs <- 0
-#Apply 10% price premium to 33 players with highest projected points, apply 10% price premium for players lower than rank 66
-shinyData$projectedCost[shinyData$overallRank <= 33] <- ceiling(shinyData$cost[shinyData$overallRank <= 33] * (leagueCap/200) * 1.1)
-shinyData$projectedCost[shinyData$overallRank >= 34 & shinyData$overallRank <= 66] <- ceiling(shinyData$cost[shinyData$overallRank >= 34 & shinyData$overallRank <= 66] * (leagueCap/200) * 1.0)
-shinyData$projectedCost[shinyData$overallRank >= 67] <- ceiling(shinyData$cost[shinyData$overallRank >= 67] * (leagueCap/200) * 0.9)
-shinyData$projectedCost[is.na(shinyData$projectedCost)==TRUE] <- 1
-shinyData$projectedCost[shinyData$projectedCost==0] <- 1
-minQBs <- numQBs
-maxQBs <- numQBs + numQBWRRBTEs
-minRBs <- numRBs
-maxRBs <- numRBs + numWRRBs + numWRRBTEs + numQBWRRBTEs
-minWRs <- numWRs
-maxWRs <- numWRs + numWRTEs + numWRRBs + numWRRBTEs + numQBWRRBTEs
-minTEs <- numTEs
-maxTEs <- numTEs + numWRTEs + numWRRBTEs + numQBWRRBTEs
-numStarters <- numQBs + numRBs + numWRs + numTEs + numWRRBs + numWRTEs + numWRRBTEs + numQBWRRBTEs
-maxAvailable <- leagueCap - (numTotalPlayers - numStarters)
-passYdsMultiplier <- 1/passYdsMultiplier
 rushYdsMultiplier <- 1/rushYdsMultiplier
 recYdsMultiplier <- 1/recYdsMultiplier
 shinyData$passYdsPts <- shinyData$passYds*passYdsMultiplier
@@ -510,3 +475,38 @@ load_or_install("fortunes")
 fortune("nls")
 library("shiny")
 runApp("./shinyapp")
+load_or_install("lme4")
+load_or_install("lme4")
+pitch.dat <- read.csv('http://www2.ntupsychology.net/seriousstats/pitch.csv')
+head(pitch.dat)
+library(lme4)
+pitch.me <- lmer(pitch ~ base + attract + (1|Face) + (1|Participant), data=pitch.dat)
+pitch.me
+load_or_install(c("lme4","pbkrtest"))
+load_or_install(c("lme4","pbkrtest"))
+pitch.red <- lmer(pitch ~ base + (1|Face) + (1|Participant), data=pitch.dat)
+KRmodcomp(pitch.me, pitch.red)
+pitch.mcmc <- mcmcsamp(pitch.me, n = 25000)
+pitch.mcmc
+xyplot(pitch.mcmc)
+HPDinterval(pitch.mcmc)
+?MCMCglmm
+load_or_install(c("lme4","pbkrtest","MCMCglmm"))
+load_or_install(c("lme4","pbkrtest","MCMCglmm"))
+m2 <- MCMCglmm(pitch ~ base + attract, random = ~ Face + Participant, data=pitch.dat, verbose=F)
+summary(m2)
+m2 <- MCMCglmm(pitch ~ base + attract, random = ~ Face + Participant, data=pitch.dat, verbose=TRUE)
+?MCMCglmm
+m2 <- MCMCglmm(pitch ~ base + attract, random = ~ Face + Participant, data=pitch.dat, family = "gaussian", nitt=25000)
+summary(m2)
+pitch.mcmc <- mcmcsamp(pitch.me, n = 50000)
+xyplot(pitch.mcmc)
+HPDinterval(pitch.mcmc)
+HPDinterval(as.mcmc(as.matrix(pitch)))
+HPDinterval(as.mcmc(as.matrix(pitch.mcmc)))
+HPDinterval(as.mcmc(as.matrix(pitch.mcmc)))
+m2 <- MCMCglmm(pitch ~ base + attract, random = ~ Face + Participant, data=pitch.dat, family = "gaussian", nitt=50000)
+summary(m2)
+HPDinterval(as.mcmc(as.matrix(pitch.mcmc)))
+summary(m2)
+plot(m2)