###GJSTL5 N=1000, T=1, t=5 assign(paste("ESTS5.",m,sep=""),colMeans(get(paste("tableESTS5.",m,sep="")))) assign(paste("ESTSrecycled5.",m,sep=""),colMeans(get(paste("tableESTSrecycled5.",m,sep="")))) assign(paste("SE5.",m,sep=""),colMeans(get(paste("tableSE5.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled5.",m,sep=""),colMeans(get(paste("tableSErecycled5.",m,sep="")),na.rm=TRUE)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("ESTS6.",m,sep=""),colMeans(get(paste("tableESTS6.",m,sep="")))) assign(paste("ESTSrecycled6.",m,sep=""),colMeans(get(paste("tableESTSrecycled6.",m,sep="")))) assign(paste("SE6.",m,sep=""),colMeans(get(paste("tableSE6.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled6.",m,sep=""),colMeans(get(paste("tableSErecycled6.",m,sep="")),na.rm=TRUE)) m=m+1 if(m>27){break} } ##Reads in data and creates matrices of estimates and standard errors for 4 GJSTL situation for N=1000,100000 and T=1,0.5 ## tableESTS{GJSTL}.{modelnum} - matrix of estimates without recycled individuals for {GJSTL} and {modelnum} ## tableESTSrecycled{GJSTL}.{modelnum} - matrix of estimates with recycled individuals for {GJSTL} and {modelnum} ## tableSE{GJSTL}.{modelnum} - matrix of standard errors without recycled individuals for {GJSTL} and {modelnum} ## tableESTSrecycled{GJSTL}.{modelnum} - matrix of standard erros with recycled individuals for {GJSTL} and {modelnum} m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("tableESTS1.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000/",m,".ests.txt",sep=""), sep=" ")) assign(paste("tableESTSrecycled1.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000/",m,".estsRECYCLED.txt",sep=""), sep=" ")) assign(paste("tableSE1.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000/",m,".se.txt",sep=""), sep=" ")) assign(paste("tableSErecycled1.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000/",m,".seRECYCLED.txt",sep=""), sep=" ")) ###GSTL2 N=100000, T=1 assign(paste("tableESTS2.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000/",m,".ests.txt",sep=""), sep=" ")) assign(paste("tableESTSrecycled2.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000/",m,".estsRECYCLED.txt",sep=""), sep=" ")) assign(paste("tableSE2.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000/",m,".se.txt",sep=""), sep=" ")) assign(paste("tableSErecycled2.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000/",m,".seRECYCLED.txt",sep=""), sep=" ")) ###GJSTL3 N=100000, T=0.5 assign(paste("tableESTS3.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000HALF/",m,".ests.txt",sep=""), sep=" ")) assign(paste("tableESTSrecycled3.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000HALF/",m,".estsRECYCLED.txt",sep=""), sep=" ")) assign(paste("tableSE3.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000HALF/",m,".se.txt",sep=""), sep=" ")) assign(paste("tableSErecycled3.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000HALF/",m,".seRECYCLED.txt",sep=""), sep=" ")) ###GJSTL4 N=1000, T=0.5 assign(paste("tableESTS4.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000HALF/",m,".ests.txt",sep=""), sep=" ")) assign(paste("tableESTSrecycled4.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000HALF/",m,".estsRECYCLED.txt",sep=""), sep=" ")) assign(paste("tableSE4.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000HALF/",m,".se.txt",sep=""), sep=" ")) assign(paste("tableSErecycled4.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000HALF/",m,".seRECYCLED.txt",sep=""), sep=" ")) ###GJSTL5 N=1000, T=1, t=5 assign(paste("tableESTS5.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time5/",m,".ests.txt",sep=""), sep=" ")) assign(paste("tableESTSrecycled5.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time5/",m,".estsRECYCLED.txt",sep=""), sep=" ")) assign(paste("tableSE5.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time5/",m,".se.txt",sep=""), sep=" ")) assign(paste("tableSErecycled5.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time5/",m,".seRECYCLED.txt",sep=""), sep=" ")) ###GJSTL6 N=1000, T=1, t=7 assign(paste("tableESTS6.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time7/",m,".ests.txt",sep=""), sep=" ")) assign(paste("tableESTSrecycled6.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time7/",m,".estsRECYCLED.txt",sep=""), sep=" ")) assign(paste("tableSE6.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time7/",m,".se.txt",sep=""), sep=" ")) assign(paste("tableSErecycled6.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations1000time7/",m,".seRECYCLED.txt",sep=""), sep=" ")) m=m+1 if(m>27){break} } #compute column means for estimates and standard errors for all parameter combinations m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("ESTS1.",m,sep=""),colMeans(get(paste("tableESTS1.",m,sep="")))) assign(paste("ESTSrecycled1.",m,sep=""),colMeans(get(paste("tableESTSrecycled1.",m,sep="")))) assign(paste("SE1.",m,sep=""),colMeans(get(paste("tableSE1.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled1.",m,sep=""),colMeans(get(paste("tableSErecycled1.",m,sep="")),na.rm=TRUE)) ###GSTL2 N=100000, T=1 assign(paste("ESTS2.",m,sep=""),colMeans(get(paste("tableESTS2.",m,sep="")))) assign(paste("ESTSrecycled2.",m,sep=""),colMeans(get(paste("tableESTSrecycled2.",m,sep="")))) assign(paste("SE2.",m,sep=""),colMeans(get(paste("tableSE2.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled2.",m,sep=""),colMeans(get(paste("tableSErecycled2.",m,sep="")),na.rm=TRUE)) ###GJSTL3 N=100000, T=0.5 assign(paste("ESTS3.",m,sep=""),colMeans(get(paste("tableESTS3.",m,sep="")))) assign(paste("ESTSrecycled3.",m,sep=""),colMeans(get(paste("tableESTSrecycled3.",m,sep="")),na.rm=TRUE)) assign(paste("SE3.",m,sep=""),colMeans(get(paste("tableSE3.",m,sep="")))) assign(paste("SErecycled3.",m,sep=""),colMeans(get(paste("tableSErecycled3.",m,sep="")),na.rm=TRUE)) ###GJSTL4 N=1000, T=0.5 assign(paste("ESTS4.",m,sep=""),colMeans(get(paste("tableESTS4.",m,sep="")))) assign(paste("ESTSrecycled4.",m,sep=""),colMeans(get(paste("tableESTSrecycled4.",m,sep="")))) assign(paste("SE4.",m,sep=""),colMeans(get(paste("tableSE4.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled4.",m,sep=""),colMeans(get(paste("tableErecycled4.",m,sep="")),na.rm=TRUE)) ###GJSTL5 N=1000, T=1, t=5 assign(paste("ESTS5.",m,sep=""),colMeans(get(paste("tableESTS5.",m,sep="")))) assign(paste("ESTSrecycled5.",m,sep=""),colMeans(get(paste("tableESTSrecycled5.",m,sep="")))) assign(paste("SE5.",m,sep=""),colMeans(get(paste("tableSE5.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled5.",m,sep=""),colMeans(get(paste("tableSErecycled5.",m,sep="")),na.rm=TRUE)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("ESTS6.",m,sep=""),colMeans(get(paste("tableESTS6.",m,sep="")))) assign(paste("ESTSrecycled6.",m,sep=""),colMeans(get(paste("tableESTSrecycled6.",m,sep="")))) assign(paste("SE6.",m,sep=""),colMeans(get(paste("tableSE6.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled6.",m,sep=""),colMeans(get(paste("tableSErecycled6.",m,sep="")),na.rm=TRUE)) m=m+1 if(m>27){break} } #compute column means for estimates and standard errors for all parameter combinations m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("ESTS1.",m,sep=""),colMeans(get(paste("tableESTS1.",m,sep="")))) assign(paste("ESTSrecycled1.",m,sep=""),colMeans(get(paste("tableESTSrecycled1.",m,sep="")))) assign(paste("SE1.",m,sep=""),colMeans(get(paste("tableSE1.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled1.",m,sep=""),colMeans(get(paste("tableSErecycled1.",m,sep="")),na.rm=TRUE)) ###GSTL2 N=100000, T=1 assign(paste("ESTS2.",m,sep=""),colMeans(get(paste("tableESTS2.",m,sep="")))) assign(paste("ESTSrecycled2.",m,sep=""),colMeans(get(paste("tableESTSrecycled2.",m,sep="")))) assign(paste("SE2.",m,sep=""),colMeans(get(paste("tableSE2.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled2.",m,sep=""),colMeans(get(paste("tableSErecycled2.",m,sep="")),na.rm=TRUE)) ###GJSTL3 N=100000, T=0.5 assign(paste("ESTS3.",m,sep=""),colMeans(get(paste("tableESTS3.",m,sep="")))) assign(paste("ESTSrecycled3.",m,sep=""),colMeans(get(paste("tableESTSrecycled3.",m,sep="")),na.rm=TRUE)) assign(paste("SE3.",m,sep=""),colMeans(get(paste("tableSE3.",m,sep="")))) assign(paste("SErecycled3.",m,sep=""),colMeans(get(paste("tableSErecycled3.",m,sep="")),na.rm=TRUE)) ###GJSTL4 N=1000, T=0.5 assign(paste("ESTS4.",m,sep=""),colMeans(get(paste("tableESTS4.",m,sep="")))) assign(paste("ESTSrecycled4.",m,sep=""),colMeans(get(paste("tableESTSrecycled4.",m,sep="")))) assign(paste("SE4.",m,sep=""),colMeans(get(paste("tableSE4.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled4.",m,sep=""),colMeans(get(paste("tablerecycled4.",m,sep="")),na.rm=TRUE)) ###GJSTL5 N=1000, T=1, t=5 assign(paste("ESTS5.",m,sep=""),colMeans(get(paste("tableESTS5.",m,sep="")))) assign(paste("ESTSrecycled5.",m,sep=""),colMeans(get(paste("tableESTSrecycled5.",m,sep="")))) assign(paste("SE5.",m,sep=""),colMeans(get(paste("tableSE5.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled5.",m,sep=""),colMeans(get(paste("tableSErecycled5.",m,sep="")),na.rm=TRUE)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("ESTS6.",m,sep=""),colMeans(get(paste("tableESTS6.",m,sep="")))) assign(paste("ESTSrecycled6.",m,sep=""),colMeans(get(paste("tableESTSrecycled6.",m,sep="")))) assign(paste("SE6.",m,sep=""),colMeans(get(paste("tableSE6.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled6.",m,sep=""),colMeans(get(paste("tableSErecycled6.",m,sep="")),na.rm=TRUE)) m=m+1 if(m>27){break} } #compute column means for estimates and standard errors for all parameter combinations m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("ESTS1.",m,sep=""),colMeans(get(paste("tableESTS1.",m,sep="")))) assign(paste("ESTSrecycled1.",m,sep=""),colMeans(get(paste("tableESTSrecycled1.",m,sep="")))) assign(paste("SE1.",m,sep=""),colMeans(get(paste("tableSE1.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled1.",m,sep=""),colMeans(get(paste("tableSErecycled1.",m,sep="")),na.rm=TRUE)) ###GSTL2 N=100000, T=1 assign(paste("ESTS2.",m,sep=""),colMeans(get(paste("tableESTS2.",m,sep="")))) assign(paste("ESTSrecycled2.",m,sep=""),colMeans(get(paste("tableESTSrecycled2.",m,sep="")))) assign(paste("SE2.",m,sep=""),colMeans(get(paste("tableSE2.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled2.",m,sep=""),colMeans(get(paste("tableSErecycled2.",m,sep="")),na.rm=TRUE)) ###GJSTL3 N=100000, T=0.5 assign(paste("ESTS3.",m,sep=""),colMeans(get(paste("tableESTS3.",m,sep="")))) assign(paste("ESTSrecycled3.",m,sep=""),colMeans(get(paste("tableESTSrecycled3.",m,sep="")),na.rm=TRUE)) assign(paste("SE3.",m,sep=""),colMeans(get(paste("tableSE3.",m,sep="")))) assign(paste("SErecycled3.",m,sep=""),colMeans(get(paste("tableSErecycled3.",m,sep="")),na.rm=TRUE)) ###GJSTL4 N=1000, T=0.5 assign(paste("ESTS4.",m,sep=""),colMeans(get(paste("tableESTS4.",m,sep="")))) assign(paste("ESTSrecycled4.",m,sep=""),colMeans(get(paste("tableESTSrecycled4.",m,sep="")))) assign(paste("SE4.",m,sep=""),colMeans(get(paste("tableSE4.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled4.",m,sep=""),colMeans(get(paste("tablerecycled4.",m,sep="")),na.rm=TRUE)) ###GJSTL5 N=1000, T=1, t=5 assign(paste("ESTS5.",m,sep=""),colMeans(get(paste("tableESTS5.",m,sep="")))) assign(paste("ESTSrecycled5.",m,sep=""),colMeans(get(paste("tableESTSrecycled5.",m,sep="")))) assign(paste("SE5.",m,sep=""),colMeans(get(paste("tableSE5.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled5.",m,sep=""),colMeans(get(paste("tableSErecycled5.",m,sep="")),na.rm=TRUE)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("ESTS6.",m,sep=""),colMeans(get(paste("tableESTS6.",m,sep="")))) assign(paste("ESTSrecycled6.",m,sep=""),colMeans(get(paste("tableESTSrecycled6.",m,sep="")))) assign(paste("SE6.",m,sep=""),colMeans(get(paste("tableSE6.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled6.",m,sep=""),colMeans(get(paste("tableSErecycled6.",m,sep="")),na.rm=TRUE)) m=m+1 if(m>27){break} } #compute column means for estimates and standard errors for all parameter combinations m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("ESTS1.",m,sep=""),colMeans(get(paste("tableESTS1.",m,sep="")))) assign(paste("ESTSrecycled1.",m,sep=""),colMeans(get(paste("tableESTSrecycled1.",m,sep="")))) assign(paste("SE1.",m,sep=""),colMeans(get(paste("tableSE1.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled1.",m,sep=""),colMeans(get(paste("tableSErecycled1.",m,sep="")),na.rm=TRUE)) ###GSTL2 N=100000, T=1 assign(paste("ESTS2.",m,sep=""),colMeans(get(paste("tableESTS2.",m,sep="")))) assign(paste("ESTSrecycled2.",m,sep=""),colMeans(get(paste("tableESTSrecycled2.",m,sep="")))) assign(paste("SE2.",m,sep=""),colMeans(get(paste("tableSE2.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled2.",m,sep=""),colMeans(get(paste("tableSErecycled2.",m,sep="")),na.rm=TRUE)) ###GJSTL3 N=100000, T=0.5 assign(paste("ESTS3.",m,sep=""),colMeans(get(paste("tableESTS3.",m,sep="")))) assign(paste("ESTSrecycled3.",m,sep=""),colMeans(get(paste("tableESTSrecycled3.",m,sep="")),na.rm=TRUE)) assign(paste("SE3.",m,sep=""),colMeans(get(paste("tableSE3.",m,sep="")))) assign(paste("SErecycled3.",m,sep=""),colMeans(get(paste("tableSErecycled3.",m,sep="")),na.rm=TRUE)) ###GJSTL4 N=1000, T=0.5 assign(paste("ESTS4.",m,sep=""),colMeans(get(paste("tableESTS4.",m,sep="")))) assign(paste("ESTSrecycled4.",m,sep=""),colMeans(get(paste("tableESTSrecycled4.",m,sep="")))) assign(paste("SE4.",m,sep=""),colMeans(get(paste("tableSE4.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled4.",m,sep=""),colMeans(get(paste("tableSErecycled4.",m,sep="")),na.rm=TRUE)) ###GJSTL5 N=1000, T=1, t=5 assign(paste("ESTS5.",m,sep=""),colMeans(get(paste("tableESTS5.",m,sep="")))) assign(paste("ESTSrecycled5.",m,sep=""),colMeans(get(paste("tableESTSrecycled5.",m,sep="")))) assign(paste("SE5.",m,sep=""),colMeans(get(paste("tableSE5.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled5.",m,sep=""),colMeans(get(paste("tableSErecycled5.",m,sep="")),na.rm=TRUE)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("ESTS6.",m,sep=""),colMeans(get(paste("tableESTS6.",m,sep="")))) assign(paste("ESTSrecycled6.",m,sep=""),colMeans(get(paste("tableESTSrecycled6.",m,sep="")))) assign(paste("SE6.",m,sep=""),colMeans(get(paste("tableSE6.",m,sep="")),na.rm=TRUE)) assign(paste("SErecycled6.",m,sep=""),colMeans(get(paste("tableSErecycled6.",m,sep="")),na.rm=TRUE)) m=m+1 if(m>27){break} } tableESTS1 tableESTS1.1 dim(tableESTS1.1) dim(tableESTS1.4) dim(tableESTS1.5) dim(tableESTS1.6) #compute sd for all parameter combinations m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("SD1.",m,sep=""),sapply(get(paste("tableESTS1.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled1.",m,sep=""),sapply(get(paste("tableESTSrecycled1.",m,sep=""))[1:24],sd)) ###GSTL2 N=100000, T=1 assign(paste("SD2.",m,sep=""),sapply(get(paste("tableESTS2.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled2.",m,sep=""),sapply(get(paste("tableESTSrecycled2.",m,sep=""))[1:24],sd)) ###GJSTL3 N=100000, T=0.5 assign(paste("SD3.",m,sep=""),sapply(get(paste("tableESTS3.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled3.",m,sep=""),sapply(get(paste("tableESTSrecycled3.",m,sep=""))[1:24],sd)) ###GJSTL4 N=1000, T=0.5 assign(paste("SD4.",m,sep=""),sapply(get(paste("tableESTS4.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled4.",m,sep=""),sapply(get(paste("tableESTSrecycled4.",m,sep=""))[1:24],sd)) ###GJSTL5 N=1000, T=1, t=5 assign(paste("SD5.",m,sep=""),sapply(get(paste("tableESTS5.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled5.",m,sep=""),sapply(get(paste("tableESTSrecycled5.",m,sep=""))[1:24],sd)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("SD6.",m,sep=""),sapply(get(paste("tableESTS6.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled6.",m,sep=""),sapply(get(paste("tableESTSrecycled6.",m,sep=""))[1:24],sd)) m=m+1 if(m>27){break} } tableESTS5.1 dim(tableESTS4.1) dim(tableESTS5.1) tableESTS4.1[,1] tableESTS4.1[1,] tableESTS5.1[1,] tableESTS6.1[1,] #compute sd for all parameter combinations m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("SD1.",m,sep=""),sapply(get(paste("tableESTS1.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled1.",m,sep=""),sapply(get(paste("tableESTSrecycled1.",m,sep=""))[1:24],sd)) ###GSTL2 N=100000, T=1 assign(paste("SD2.",m,sep=""),sapply(get(paste("tableESTS2.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled2.",m,sep=""),sapply(get(paste("tableESTSrecycled2.",m,sep=""))[1:24],sd)) ###GJSTL3 N=100000, T=0.5 assign(paste("SD3.",m,sep=""),sapply(get(paste("tableESTS3.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled3.",m,sep=""),sapply(get(paste("tableESTSrecycled3.",m,sep=""))[1:24],sd)) ###GJSTL4 N=1000, T=0.5 assign(paste("SD4.",m,sep=""),sapply(get(paste("tableESTS4.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled4.",m,sep=""),sapply(get(paste("tableESTSrecycled4.",m,sep=""))[1:24],sd)) ###GJSTL5 N=1000, T=1, t=5 assign(paste("SD5.",m,sep=""),sapply(get(paste("tableESTS5.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled5.",m,sep=""),sapply(get(paste("tableESTSrecycled5.",m,sep=""))[1:14],sd)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("SD6.",m,sep=""),sapply(get(paste("tableESTS6.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled6.",m,sep=""),sapply(get(paste("tableESTSrecycled6.",m,sep=""))[1:18],sd)) m=m+1 if(m>27){break} } #compute sd for all parameter combinations m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("SD1.",m,sep=""),sapply(get(paste("tableESTS1.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled1.",m,sep=""),sapply(get(paste("tableESTSrecycled1.",m,sep=""))[1:24],sd)) ###GSTL2 N=100000, T=1 assign(paste("SD2.",m,sep=""),sapply(get(paste("tableESTS2.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled2.",m,sep=""),sapply(get(paste("tableESTSrecycled2.",m,sep=""))[1:24],sd)) ###GJSTL3 N=100000, T=0.5 assign(paste("SD3.",m,sep=""),sapply(get(paste("tableESTS3.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled3.",m,sep=""),sapply(get(paste("tableESTSrecycled3.",m,sep=""))[1:24],sd)) ###GJSTL4 N=1000, T=0.5 assign(paste("SD4.",m,sep=""),sapply(get(paste("tableESTS4.",m,sep=""))[1:24],sd)) assign(paste("SDrecycled4.",m,sep=""),sapply(get(paste("tableESTSrecycled4.",m,sep=""))[1:24],sd)) ###GJSTL5 N=1000, T=1, t=5 assign(paste("SD5.",m,sep=""),sapply(get(paste("tableESTS5.",m,sep=""))[1:14],sd)) assign(paste("SDrecycled5.",m,sep=""),sapply(get(paste("tableESTSrecycled5.",m,sep=""))[1:14],sd)) ###GJSTL6 N=1000, T=1, t=7 assign(paste("SD6.",m,sep=""),sapply(get(paste("tableESTS6.",m,sep=""))[1:18],sd)) assign(paste("SDrecycled6.",m,sep=""),sapply(get(paste("tableESTSrecycled6.",m,sep=""))[1:18],sd)) m=m+1 if(m>27){break} } #compute MSE for all parameter combinations ColCalcRMSE<-function(A,b){ RMSE=vector(length=length(b)) for(i in 1:length(b)){ RMSE[i]=sqrt(mean((A[,i]-b[i])^2)) } RMSE } m=1 repeat{ ###GSTL1 N=1000, T=1 assign(paste("RMSE1.",m,sep=""),ColCalcRMSE(get(paste("tableESTS1.",m,sep=""))[1:24],parameters2[m,])) assign(paste("RMSErecycled1.",m,sep=""),ColCalcRMSE(get(paste("tableESTSrecycled1.",m,sep=""))[1:24],parameters2[m,])) ###GSTL2 N=100000, T=1 assign(paste("RMSE2.",m,sep=""),ColCalcRMSE(get(paste("tableESTS2.",m,sep=""))[1:24],parameters[m,])) assign(paste("RMSErecycled2.",m,sep=""),ColCalcRMSE(get(paste("tableESTSrecycled2.",m,sep=""))[1:24],parameters[m,])) ###GJSTL3 N=100000, T=0.5 assign(paste("RMSE3.",m,sep=""),ColCalcRMSE(get(paste("tableESTS3.",m,sep=""))[1:24],parameters[m,])) assign(paste("RMSErecycled3.",m,sep=""),ColCalcRMSE(get(paste("tableESTSrecycled3.",m,sep=""))[1:24],parameters[m,])) ###GJSTL4 N=1000, T=0.5 assign(paste("RMSE4.",m,sep=""),ColCalcRMSE(get(paste("tableESTS4.",m,sep=""))[1:24],parameters[m,])) assign(paste("RMSErecycled4.",m,sep=""),ColCalcRMSE(get(paste("tableESTSrecycled4.",m,sep=""))[1:24],parameters[m,])) ###GJSTL5 N=1000, T=1, t=5 assign(paste("RMSE5.",m,sep=""),ColCalcRMSE(get(paste("tableESTS5.",m,sep=""))[1:14],parameters[m,])) assign(paste("RMSErecycled5.",m,sep=""),ColCalcRMSE(get(paste("tableESTSrecycled5.",m,sep=""))[1:14],parameters[m,])) ###GJSTL6 N=1000, T=1, t-7 assign(paste("RMSE6.",m,sep=""),ColCalcRMSE(get(paste("tableESTS6.",m,sep=""))[1:18],parameters[m,])) assign(paste("RMSErecycled6.",m,sep=""),ColCalcRMSE(get(paste("tableESTSrecycled6.",m,sep=""))[1:18],parameters[m,])) m=m+1 if(m>27){break} } #compute MSE for all parameter combinations ColCalcRMSE<-function(A,b){ RMSE=vector(length=length(b)) for(i in 1:length(b)){ RMSE[i]=sqrt(mean((A[,i]-b[i])^2)) } RMSE } m=1 repeat{ assign(paste("tableESTS.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000/",m,".ests.txt",sep=""), sep=" ")) assign(paste("tableESTSrecycled.",m,sep=""),read.table(paste("~/Dropbox/Project/Simulations100000/",m,".estsRECYCLED.txt",sep=""), sep=" ")) m=m+1 if(m>27){break} } par(mfrow=c(3,3),oma=c(3,5,2,4),mai=c(0.1,0.1,0.1,0.1)) #1 combine <- list(tableESTSrecycled.27[,1],tableESTS.27[,1],tableESTSrecycled.24[,1],tableESTS.24[,1],tableESTSrecycled.21[,1],tableESTS.21[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() axis(side=2, las=2, cex=0.7) abline(h=0.2) mtext(side = 3, outer=FALSE, text=bquote(paste(lambda," = ",0.2)), line = 1, cex=0.7) #2 combine <- list(tableESTSrecycled.26[,1],tableESTS.26[,1],tableESTSrecycled.23[,1],tableESTS.23[,1],tableESTSrecycled.20[,1],tableESTS.20[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() abline(h=0.2) mtext(side = 3, outer=FALSE, text=bquote(paste(lambda," = ",0.5)), line = 1, cex=0.7) #3 combine <- list(tableESTSrecycled.25[,1],tableESTS.25[,1],tableESTSrecycled.22[,1],tableESTS.22[,1],tableESTSrecycled.19[,1],tableESTS.19[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() abline(h=0.2) mtext(side = 3, outer=FALSE, text=bquote(paste(lambda," = ",0.9)), line = 1, cex=0.7) mtext(side = 4, las=1, outer=FALSE, text=bquote(paste(phi," = ",0.2)), line = 1, cex=0.7) #4 combine <- list(tableESTSrecycled.18[,1],tableESTS.18[,1],tableESTSrecycled.15[,1],tableESTS.15[,1],tableESTSrecycled.12[,1],tableESTS.12[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() axis(side=2, las=2, cex=0.7) abline(h=0.5) #5 combine <- list(tableESTSrecycled.17[,1],tableESTS.17[,1],tableESTSrecycled.14[,1],tableESTS.14[,1],tableESTSrecycled.11[,1],tableESTS.11[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() abline(h=0.5) #6 combine <- list(tableESTSrecycled.16[,1],tableESTS.16[,1],tableESTSrecycled.13[,1],tableESTS.13[,1],tableESTSrecycled.10[,1],tableESTS.10[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() abline(h=0.5) mtext(side = 4, las=1, outer=FALSE, text=bquote(paste(phi," = ",0.5)), line = 1, cex=0.7) #7 combine <- list(tableESTSrecycled.9[,1],tableESTS.9[,1],tableESTSrecycled.6[,1],tableESTS.6[,1],tableESTSrecycled.3[,1],tableESTS.3[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() axis(side=2, las=2, cex=0.7) abline(h=0.9) mtext(text=bquote(paste("p=0.2"," ","p=0.5", " ","p=0.9")), side = 1, line = 0.5, cex=0.7) #8 combine <- list(tableESTSrecycled.8[,1],tableESTS.8[,1],tableESTSrecycled.5[,1],tableESTS.5[,1],tableESTSrecycled.2[,1],tableESTS.2[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() abline(h=0.9) mtext(text=bquote(paste("p=0.2"," ","p=0.5", " ","p=0.9")), side = 1, line = 0.5, cex=0.7) #9 combine <- list(tableESTSrecycled.7[,1],tableESTS.7[,1],tableESTSrecycled.4[,1],tableESTS.4[,1],tableESTSrecycled.1[,1],tableESTS.1[,1]) # create a list of vectors to be bound max.length <- max(sapply(combine, length)) matrix=do.call(cbind, lapply(combine, function(.vec){ c(.vec, rep(NA, max.length - length(.vec))) })) boxplot(matrix, col=(c(colors[2],colors[3],colors[2],colors[3], colors[2],colors[3])),ylim=c(0,1), axes=FALSE) box() abline(h=0.9) mtext(side = 4, las=1, outer=FALSE, text=bquote(paste(phi," = ",0.9)), line = 1, cex=0.7) mtext(text=bquote(paste("p=0.2"," ","p=0.5", " ","p=0.9")), side = 1, line = 0.5, cex=0.7) mtext(side = 2, outer=TRUE, "Survival Estimates", line = 2, cex=0.7) par(fig = c(0, 1, 0, 1), oma = c(0, 0, 0, 0), mar = c(0, 0, 0, 0), new = TRUE) plot(0, 0, type = "n", bty = "n", xaxt = "n", yaxt = "n") legend("bottom", c("With Recycled","Without Recycled"), xpd = TRUE, horiz = TRUE, inset = c(0, 0), bty = "n", pch = c(15,15), col = c(colors[2],colors[3]), cex = 1) parameters2 parameters[3,] nsample=5 b=numeric(length=nsample) #probability of birth/immigration (sum to 1) bstar=numeric(length=nsample) # expected fraction of population remaining entering the population b[1:nsample]=1/nsample bstar_to_b<-function(samp_time,std_bstar,nsample) { if(samp_time==1){b=std_bstar[samp_time]} else{ b=1; for(u in 1:(samp_time-1)) { b=b*(1-std_bstar[u]);} b=b*std_bstar[samp_time]; } } parameters3<-matrix(0,nrow=27,ncol=3+10+10+1) parameters3[,1]=globalPhi parameters3[,2]=globalP parameters3[,3]=globalLambda for(i in 1:5){ parameters3[,i+3]=rep(b[i],27) } parameters3 parameters3<-matrix(0,nrow=27,ncol=14) parameters3[,1]=globalPhi parameters3[,2]=globalP parameters3[,3]=globalLambda for(i in 1:5){ parameters3[,i+3]=rep(b[i],27) } parameters3[,9]=1000*b[1] for(j in 1:27){ for(i in 2:5){ parameters3[j,i+8]=parameters3[j,i+7]*globalPhi[j]+1000*b[i] } } parameters3 parameters3[,14]=1000 nsample=7 b=numeric(length=nsample) #probability of birth/immigration (sum to 1) bstar=numeric(length=nsample) # expected fraction of population remaining entering the population b[1:nsample]=1/nsample bstar_to_b<-function(samp_time,std_bstar,nsample) { if(samp_time==1){b=std_bstar[samp_time]} else{ b=1; for(u in 1:(samp_time-1)) { b=b*(1-std_bstar[u]);} b=b*std_bstar[samp_time]; } } parameters4<-matrix(0,nrow=27,ncol=18) parameters4[,1]=globalPhi parameters4[,2]=globalP parameters4[,3]=globalLambda for(i in 1:7){ parameters4[,i+3]=rep(b[i],27) } parameters4<-matrix(0,nrow=27,ncol=18) parameters4[,1]=globalPhi parameters4[,2]=globalP parameters4[,3]=globalLambda for(i in 1:7){ parameters4[,i+3]=rep(b[i],27) } parameters4[,11]=1000*b[1] for(j in 1:27){ for(i in 2:5){ parameters4[j,i+10]=parameters4[j,i+9]*globalPhi[j]+1000*b[i] } } parameters4[,18]=1000 parameters4 parameters4<-matrix(0,nrow=27,ncol=18) parameters4[,1]=globalPhi parameters4[,2]=globalP parameters4[,3]=globalLambda for(i in 1:7){ parameters4[,i+3]=rep(b[i],27) } parameters4[,11]=1000*b[1] for(j in 1:27){ for(i in 2:7){ parameters4[j,i+10]=parameters4[j,i+9]*globalPhi[j]+1000*b[i] } } parameters4[,18]=1000 parameters4