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Figure_6.R
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##################################################################
# This is a r file to do put all regression results in one figure.
# (+ Rensink's regression)
##################################################################
# change this path for need
pdf("Figure_6.pdf", height = 7, width = 7)
######################functions and parameters#########################
data <- read.csv("data/master.csv", header = T)
plot1 <- c(F)
plot2 <- c(F)
plot3 <- c(F)
plot4 <- c(F)
plot5 <- c(F)
lWidth <- 2
# define an array of vis names
visLevels <- c("ordered_line","line","radar","stackedline","stackedarea","stackedbar","donut","scatterplot","parallelCoordinates")
dirLevels <- levels(data$rdirection)
abLevels <- levels(data$approach)
# define colors
colors1 <- c(
"#fb8072",
"#8dd3c7",
"#80b1d3",
"#fdb462",
"#b3de69",
"#fccde5",
"#d9d9d9",
"#bc80bd",
"#ccebc5"
)
colors <- colors1[9:1]
visLevels1 <- c("ordered line","line","radar","stackedline","stackedarea","stackedbar","donut","scatterplot","parallel coordinates")
# define legend texts
visAll <- c(visLevels1, "scatterplot, Rensink","","positive","negative")
# define legend colors
colorAll <- c(colors, "gold","white","black","black")
par(cex.main = 0.8, cex.axis = 0.8 , xaxs = 'i' , yaxs = 'i')
borderCol <- c("gray90")
exp_lim <- 0.45
mean_col <- c("red")
median_col <- c("black")
offset <- 0.01
lwdv <- 0.1
# all coefficiences are treated as -k
getY <- function(b, k, x) return (-1 * abs(k) * x + b)
plotWhite <- function(title){
# plot something
plot(-1, -1, xlim = c(0, 1), ylim = c(0, 0.6) , xlab = "ra" , ylab = "JND", main = title , axes = F, cex.main = 1.5)
# draw ceiling and floor line
abline(h = exp_lim, col = borderCol, lty = 2)
abline(a = 1 , b = -1 , col = borderCol , lty = 2)
rlist <- c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1)
axis(side = 1, at = c(rlist) , lwd = 0.7) # x axis
axis(side = 2, at = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8), lwd = 0.7) # y axis
segments(0, getY(0.25, -0.22 , 0) , 1 , getY(0.25, -0.22, 1) , col = "gold", lwd = lWidth)
legend("topright", visAll , col = colorAll, lty = c(1,1,1,1,1,1,1,1,1,1,1,1,2), cex = 0.9 , box.col = "white" , ncol = 3, lwd = 2);
}
filter <- function(medians, mads, dataset){
dataframe <- data.frame(col.names = c("jnd","rbase","sign","approach"))
for(i in 1:length(mads$jnd)){
medianv <- medians$jnd[i]
madv <- mads$jnd[i]
rbasev <- mads$rbase[i]
approachv <- mads$approach[i]
signv <- mads$sign[i]
subdata <- subset(dataset, sign == signv & rbase == rbasev & approach == approachv & (abs(jnd - medianv) <= 3 * madv))
all_subdata <- subset(dataset, sign == signv & rbase == rbasev & approach == approachv)
compl_subdata <- subset(dataset, sign == signv & rbase == rbasev & approach == approachv & (abs(jnd - medianv) > 3 * madv))
if(length(dataframe$jnd) == 0){
dataframe <- subdata
} else {
dataframe <- rbind(dataframe, subdata)
}
}
return (dataframe)
}
######################run#######################
plotWhite("model fit results")
for(visid in 1:length(visLevels)){
jnd <- subset(data, data$vis == visLevels[visid])$jnd
rbase <- subset(data, data$vis == visLevels[visid])$rbase
sign <- subset(data, data$vis == visLevels[visid])$sign
approach <- subset(data, data$vis == visLevels[visid])$approach
# get the sub dataset of a specific vis
subdata <- data.frame(jnd, rbase, visLevels[visid], approach, sign)
medians <- aggregate(jnd ~ rbase*approach*sign, subdata, median)
mads <- aggregate(jnd ~ rbase*approach*sign, subdata, function(x){
return (mad(x, constant = 1))
})
f_data <- filter(medians, mads, subdata)
subdata <- aggregate(jnd ~ rbase * approach * sign, data = f_data, mean)
# get mean of this condition (junk line)
subdata_mean <- aggregate(jnd ~ rbase*sign*approach, subdata, mean)
# get adjusted r values for above approach
adj_a <- aggregate(jnd ~ factor(rbase)*factor(sign), subdata, mean)
adj_a_save <- subset(subdata_mean, approach == "above")
adj_a_save$rbase <- (adj_a_save$rbase + 0.5 * adj_a$jnd) # adjust
# get adjusted r values for below approach
adj_b <- aggregate(jnd ~ rbase*sign, subdata, mean)
adj_b_save <- subset(subdata_mean, approach == "below")
adj_b_save$rbase <- (adj_b_save$rbase - 0.5 * adj_b$jnd) # adjust
# merge above and below approach
adj_ab <- rbind(adj_a_save , adj_b_save)
#get positive
adj_p <- subset(adj_ab, sign == 1)
#get negative
adj_n <- subset(adj_ab, sign == -1)
# do regression on positive
regression_p <- lm(jnd ~ rbase, adj_p)
# computer correlation coefficient r
regression_p_r <- cor(adj_p$jnd, adj_p$rbase)
# do regression on negative
regression_n <- lm(jnd ~ rbase, adj_n)
# computer correlation coefficient r
regression_n_r <- cor(adj_n$jnd, adj_n$rbase)
if(visLevels[visid] == "line"||visLevels[visid] == "ordered_line"){
segments(0 , getY(regression_p$coefficients[1], regression_p$coefficients[2] , 0) ,
1 , getY(regression_p$coefficients[1], regression_p$coefficients[2] , 1) ,
col = colors[visid], lwd = lWidth)
if(visLevels[visid] == "ordered_line"){
segments(0 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 0) ,
1 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 1) ,
col = colors[visid], lty = 2, lwd = lWidth)}
}
if(visLevels[visid] == "line" || visLevels[visid] == "radar") {
segments(0 , getY(regression_p$coefficients[1], regression_p$coefficients[2] , 0) ,
1 , getY(regression_p$coefficients[1], regression_p$coefficients[2] , 1) ,
col = colors[visid], lwd = lWidth)
}
if(visLevels[visid] == "stackedline" || visLevels[visid] == "stackedarea" || visLevels[visid] == "stackedbar"){
segments(0 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 0) ,
1 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 1) ,
col = colors[visid], lty = 2, lwd = lWidth)
}
if(visLevels[visid] == "stackedbar"||visLevels[visid] == "donut"){
segments(0 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 0) ,
1 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 1) ,
col = colors[visid], lty = 2, lwd = lWidth)
}
if(visLevels[visid] == "scatterplot" || visLevels[visid] == "parallelCoordinates"){
segments(0 , getY(regression_p$coefficients[1], regression_p$coefficients[2] , 0) ,
1 , getY(regression_p$coefficients[1], regression_p$coefficients[2] , 1) ,
col = colors[visid], lwd = lWidth)
segments(0 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 0) ,
1 , getY(regression_n$coefficients[1], regression_n$coefficients[2] , 1) ,
col = colors[visid], lty = 2, lwd = lWidth)
}
}
dev.off()