Read in data

the.data <- as.matrix(read.table(“Journal-Ranking-0101_mod_4.txt”))

Convert to Matrix

(mydata) <- the.data[sample(1:161,100), c(1,3:10,12,13)]

Journal_ID <- (mydata[,1]) Number_of_citations <- (mydata[,2]) Impact_factor <- (mydata[,3])?sh Five_yr_impact_factor <- (mydata[,4]) ii <- (mydata[,5]) Articles <- (mydata[,6]) Half_life <- (mydata[,7]) Eigenfactor <- (mydata[,8]) Art_i <- (mydata[,9]) Y_Rank_ID <- (mydata[,10]) Number <-(mydata[,11])

plot(Y_Rank_ID,Number) title (“Scatterplot of Variable 10’ vs Variable 11”)

plot(Y_Rank_ID,Y_Rank_ID) title (“Scatterplot of Variable 10 vs Variable 10”)

plot(Y_Rank_ID,Art_i) title (“Scatterplot of Variable 10 vs Variable 9”)

plot(Y_Rank_ID,Eigenfactor) title (“Scatterplot of Variable 10 vs Variable 8”)

plot(Y_Rank_ID,Half_life) title (“Scatterplot of Variable 10 vs Variable 7”)

plot(Y_Rank_ID,Articles) title (“Scatterplot of Variable 10 vs Variable 6”)

plot(Y_Rank_ID,ii) title (“Scatterplot of Variable 10 vs Variable 5”)

plot(Y_Rank_ID,Five_yr_impact_factor) title (“Scatterplot of Variable 10 vs Variable 4”)

plot(Y_Rank_ID,Impact_factor) title (“Scatterplot of Variable 10 vs Variable 3”)

plot(Y_Rank_ID,Number_of_citations) title (“Scatterplot of Variable 10 vs Variable 2”)

plot(Y_Rank_ID,Journal_ID) title (“Scatterplot of Variable 10 vs Variable 1”)

Histograms

hist(mydata[,1], main=“Histogram of Variable 1”) hist(mydata[,2], main=“Histogram of Variable 2”) hist(mydata[,3], main=“Histogram of Variable 3”) hist(mydata[,4], main=“Histogram of Variable 4”) hist(mydata[,5], main=“Histogram of Variable 5”) hist(mydata[,6], main=“Histogram of Variable 6”) hist(mydata[,7], main=“Histogram of Variable 7”) hist(mydata[,8], main=“Histogram of Variable 8”) hist(mydata[,9], main=“Histogram of Variable 9”) hist(mydata[,10], main=“Histogram of Variable 10”) hist(mydata[,11], main=“Histogram of Variable 11”)

Transformations

datatransform2 <- log(mydata[,2]) datatransform3 <- log(mydata[,3]) datatransform4 <- log(mydata[,4]) datatransform7 <- log(mydata[,7]) datatransform10 <-log(mydata[,10])

Scaling

scale01 <- function(x){(x-min(x))/(max(x)-min(x))}

datatransformed <- data.frame(Citations =c(datatransform2), IF =c(datatransform3), FIVEYRIF =c(datatransform4), Half_life =c(datatransform7), Rank =c(datatransform10))

fulltransformed <- scale01(datatransformed)

Write to file

write.table(fulltransformed,“zacharygancarz-transformed.txt”,)

Graphing Transformations

fulltransformed <- mapply (fulltransformed, FUN=as.numeric) transformed_citations <- (fulltransformed[,1]) transformed_if <- (fulltransformed[,2]) transformed_fiveif <- (fulltransformed[,3]) transformed_half_life <- (fulltransformed[,4]) transformed_rank <- (fulltransformed[,5]) output <- (fulltransformed[,6])

plot(transformed_rank, transformed_citations) title (“Scatterplot of transformed (LOG & Scalar [0,1]) Rank vs Citations”) plot(transformed_rank, transformed_if) title (“Scatterplot of transformed (LOG & Scalar [0,1]) Rank vs Impact Factor”) plot(transformed_rank, transformed_fiveif) title (“Scatterplot of transformed (LOG & Scalar [0,1]) Rank vs 5 Year Impact Factor”) plot(transformed_rank, transformed_half_life) title (“Scatterplot of transformed (LOG & Scalar [0,1]) Rank vs Half-Life”) plot(transformed_rank, transformed_rank) title (“Scatterplot of transformed (LOG & Scalar [0,1]) Rank vs Rank”)

hist(transformed_citations, main=“Transformed (LOG & Scalar [0,1]) Histogram of Citations”) hist(transformed_if,main=“Transformed (LOG & Scalar [0,1]) Histogram of Impact Factor”) hist(transformed_fiveif, main=“Transformed (LOG & Scalar [0,1]) Histogram of 5 Year Impact Factor”) hist(transformed_half_life, main=“Transformed (LOG & Scalar [0,1]) Histogram of Half-Life”) hist(transformed_rank, main=“Transformed (LOG & Scalar [0,1]) Histogram of Rank”)

Import Extra Functions

source(“AggWaFit718.R”)

Apply Models

fit.OWA(fulltransformed,“output.fileOWA.txt”,“output.statsOWA.txt”) #to find Weights fit.QAM(fulltransformed,“output.fileQAM05.txt”,“output.statsQAM05.txt”, g=PM05,g.inv=invPM05) #to find PM 0.5. fit.QAM(fulltransformed,“output.fileQAM05.txt”,“output.statsQAM05.txt”, g=PM2,g.inv=invPM2) #to find PM 2 - broken - RMSE can be used as PM=2 fit.choquet(fulltransformed,“output.filechoc.txt”,“output.statschoc.txt”)

Write models to file

lastq <- as.matrix(read.table(“output.filechoc.txt”))