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alexchinco/plot--corrected-lambda-estimate--simulated-data--24jan2016.R

Created January 24, 2016 22:43
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Create plots for blog post on the characteristic scale of house-price variation.
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plot--corrected-lambda-estimate--simulated-data--24jan2016.R
###########################################################################
###########################################################################
## @purpose: Estimate corrected lambda in simulated data.
## ------------------------------------------------------------------------
## @author: Alex Chinco
## @date: 24-JAN-2016
###########################################################################
###########################################################################
###########################################################################
###########################################################################
## @section: Prep workspace
###########################################################################
###########################################################################
options(width=120, digits=6, digits.secs=6)
rm(list=ls())
library(foreign)
library(grid)
library(plyr)
library(ggplot2)
library(reshape)
library(R.matlab)
library(lubridate)
library(tikzDevice)
print(options('tikzLatexPackages'))
options(tikzLatexPackages =
c("\\usepackage{tikz}\n",
"\\usepackage[active,tightpage,psfixbb]{preview}\n",
"\\PreviewEnvironment{pgfpicture}\n",
"\\setlength\\PreviewBorder{0pt}\n",
"\\usepackage{amsmath}\n",
"\\usepackage{pxfonts}\n",
"\\usepackage{bm}\n",
"\\usepackage{xfrac}\n"
)
)
setTikzDefaults(overwrite = FALSE)
print(options('tikzLatexPackages'))
library(foreach)
library(doMC)
registerDoMC(2)
set.seed(123456)
scl.str.DAT_DIR <- "~/Dropbox/research/persistence_and_dispersion/data/"
scl.str.FIG_DIR <- "~/Dropbox/research/persistence_and_dispersion/figures/"
###########################################################################
###########################################################################
## @section: Define parameters
###########################################################################
###########################################################################
scl.int.H <- 2^8
vec.int.N <- c(2^2, 2^4, 2^6)
scl.int.NUM_N <- length(vec.int.N)
vec.flt.SIG_N <- sqrt(seq(0.05, 0.95, by = 0.05))
vec.flt.SIG_H <- sqrt(1 - vec.flt.SIG_N^2)
scl.int.NUM_BREAKS <- length(vec.flt.SIG_N)
scl.int.NUM_ITER <- 10^3
###########################################################################
###########################################################################
## @section: Estimate corrected lambda
###########################################################################
###########################################################################
if (TRUE == FALSE) {
mat.flt.PLOT <- foreach(g=1:scl.int.NUM_N, .combine = "rbind") %do% {
## for (g in 1:scl.int.NUM_N) {
mat.flt.OUTTER <- foreach(b=1:scl.int.NUM_BREAKS, .combine = "rbind") %do% {
## for (b in 1:scl.int.NUM_BREAKS) {
print(paste("N = ", vec.int.N[g], ", ", round(b/scl.int.NUM_BREAKS*100,2), "%: ", round(Sys.time()), sep = ""))
mat.flt.INNER <- foreach(i=1:scl.int.NUM_ITER, .combine = "rbind") %dopar% {
## for (i in 1:scl.int.NUM_ITER) {
mat.df.TEMP <- data.frame(n = 1:vec.int.N[g], mu = rnorm(vec.int.N[g], 0, vec.flt.SIG_N[b]))
mat.df.TEMP <- foreach(l=1:(scl.int.H/vec.int.N[g]), .combine = "rbind") %do% { return(mat.df.TEMP) }
mat.df.TEMP <- mat.df.TEMP[order(mat.df.TEMP$n), ]
mat.df.TEMP$h <- 1:scl.int.H
mat.df.TEMP$tet <- rnorm(scl.int.H, 0, vec.flt.SIG_H[b])
mat.df.TEMP$p <- mat.df.TEMP$mu + mat.df.TEMP$tet
mat.df.TEMP <- mat.df.TEMP[, c("h", "n", "mu", "tet", "p")]
mat.flt.RHS_N <- matrix(rep(0, (vec.int.N[g]/2) * scl.int.H), nrow = scl.int.H)
mat.flt.RHS_H <- matrix(rep(0, (scl.int.H/2) * scl.int.H), nrow = scl.int.H)
scl.int.L <- (scl.int.H/vec.int.N[g])
for (n in 1:vec.int.N[g]) {
vec.int.TEMP <- 1:scl.int.L
for (l in 1:(scl.int.L/2)) {
vec.int.SAMPLE <- sample(vec.int.TEMP, 2)
vec.int.TEMP <- vec.int.TEMP[-which(vec.int.TEMP %in% vec.int.SAMPLE)]
mat.flt.RHS_H[(n-1)*scl.int.L + vec.int.SAMPLE[1], (n-1)*scl.int.L/2 + l] <- sqrt(scl.int.H-1)/sqrt(2*2)
mat.flt.RHS_H[(n-1)*scl.int.L + vec.int.SAMPLE[2], (n-1)*scl.int.L/2 + l] <- -sqrt(scl.int.H-1)/sqrt(2*2)
}
}
vec.int.TEMP <- 1:vec.int.N[g]
for (n in 1:(vec.int.N[g]/2)) {
vec.int.SAMPLE <- sample(vec.int.TEMP, 2)
vec.int.TEMP <- vec.int.TEMP[-which(vec.int.TEMP %in% vec.int.SAMPLE)]
mat.flt.RHS_N[(vec.int.SAMPLE[1]-1)*scl.int.L + 1:scl.int.L, n] <- sqrt(scl.int.H-1)/sqrt(2*2*scl.int.L)
mat.flt.RHS_N[(vec.int.SAMPLE[2]-1)*scl.int.L + 1:scl.int.L, n] <- -sqrt(scl.int.H-1)/sqrt(2*2*scl.int.L)
}
obj.lm.REG <- summary(lm(mat.df.TEMP$p ~ 1 + mat.flt.RHS_H + mat.flt.RHS_N))
vec.flt.COEF <- obj.lm.REG$coef[-c(1),1]
scl.flt.VAR_H_TIL <- sum(head(vec.flt.COEF, scl.int.H/2)^2)
scl.flt.VAR_N_TIL <- sum(tail(vec.flt.COEF, vec.int.N[g]/2)^2) - scl.flt.VAR_H_TIL/(scl.int.H/vec.int.N[g])
return(c(scl.flt.VAR_N_TIL, scl.flt.VAR_H_TIL))
}
return(c(vec.int.N[g], vec.flt.SIG_N[b]^2, apply(mat.flt.INNER, 2, mean)))
}
return(mat.flt.OUTTER)
}
mat.df.PLOT <- data.frame(mat.flt.PLOT)
names(mat.df.PLOT) <- c("N", "varN", "varNtil", "varHtil")
mat.df.PLOT$ratStar <- mat.df.PLOT$varN
mat.df.PLOT$rat2Sample <- mat.df.PLOT$varNtil/(mat.df.PLOT$varNtil + mat.df.PLOT$varHtil)
save(mat.df.PLOT, file = paste(scl.str.DAT_DIR, "data--corrected-lambda-estimate--simulated-data--24jan2016.Rdata", sep = ""))
}
load(paste(scl.str.DAT_DIR, "data--corrected-lambda-estimate--simulated-data--24jan2016.Rdata", sep = ""))
###########################################################################
###########################################################################
## @section: Plot results
###########################################################################
###########################################################################
mat.df.PLOT1 <- mat.df.PLOT[, c("ratStar", "N", "rat2Sample")]
names(mat.df.PLOT1) <- c("ratStar", "variable", "value")
mat.df.PLOT1$variable <- paste("$N = ", mat.df.PLOT1$variable, "$", sep = "")
mat.df.PLOT1$variable <- factor(mat.df.PLOT1$variable, levels = c("$N = 4$", "$N = 16$", "$N = 64$"))
mat.df.PLOT2 <- mat.df.PLOT[, c("ratStar", "N", "rat2Sample")]
names(mat.df.PLOT2) <- c("ratStar", "variable", "value")
mat.df.PLOT2$value <- mat.df.PLOT2$value/mat.df.PLOT2$ratStar - 1
mat.df.PLOT2$variable <- paste("$N = ", mat.df.PLOT2$variable, "$", sep = "")
mat.df.PLOT2$variable <- factor(mat.df.PLOT2$variable, levels = c("$N = 4$", "$N = 16$", "$N = 64$"))
obj.vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
theme_set(theme_bw())
scl.str.RAW_FILE <- 'plot--corrected-lambda-estimate--simulated-data--24jan2016'
scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='')
scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='')
scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='')
scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='')
scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='')
tikz(file = scl.str.TEX_FILE, height = 2, width = 7, standAlone=TRUE)
## Left panel
obj.gg2.PLOT1 <- ggplot(data = mat.df.PLOT1)
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + scale_colour_brewer(palette = "Set1")
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + annotate("segment",
x = 0.05,
xend = 0.95,
y = 0.05,
yend = 0.95,
colour = "black",
linetype = 4,
size = 1
)
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + geom_path(aes(x = ratStar,
y = value,
group = variable,
colour = variable
),
size = 2,
alpha = 0.75
)
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + coord_cartesian(xlim = c(0.00, 1.00), ylim = c(0.02, 0.98))
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + scale_x_continuous(breaks = c(0.05, 0.20, 0.50, 0.80, 0.95))
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + scale_y_continuous(breaks = c(0.05, 0.20, 0.50, 0.80, 0.95))
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + xlab("$\\lambda = \\sfrac{\\sigma_N^2}{(\\sigma_N^2 + \\sigma_H^2)}$")
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + ylab('$\\lambda^{\\scriptscriptstyle \\text{2-Sample}}$')
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + theme(plot.margin = unit(c(0,0.15,0.15,0.15), "lines"),
axis.text = element_text(size = 10),
axis.title = element_text(size = 10),
plot.title = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none"
)
## Right panel
obj.gg2.PLOT2 <- ggplot(data = mat.df.PLOT2)
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_colour_brewer(palette = "Set1")
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + annotate("segment",
x = 0.05,
xend = 0.95,
y = 0.00,
yend = 0.00,
colour = "black",
linetype = 4,
size = 1
)
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + geom_path(aes(x = ratStar,
y = value,
group = variable,
colour = variable
),
size = 2,
alpha = 0.75
)
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + xlab("$\\lambda = \\sfrac{\\sigma_N^2}{(\\sigma_N^2 + \\sigma_H^2)}$")
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + ylab('$(\\lambda^{\\scriptscriptstyle \\text{2-Sample}} - \\lambda)/\\lambda$')
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + labs(colour = "")
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + coord_cartesian(xlim = c(0.00, 1.00), ylim = c(-0.25,2.50))
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_x_continuous(breaks = c(0.05, 0.20, 0.50, 0.80, 0.95))
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_y_continuous(breaks = c(0, 0.75, 1.5, 2.25))
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + theme(plot.margin = unit(c(0,0.15,0.15,0.15), "lines"),
axis.text = element_text(size = 10),
axis.title = element_text(size = 10),
plot.title = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.75, 0.75),
legend.background = element_blank()
)
## Generate output
pushViewport(viewport(layout = grid.layout(5, 2)))
grid.text('Corrected Estimates of Neighborhood-Level Variance', vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2), vjust = 0.20, gp = gpar(fontsize=15))
print(obj.gg2.PLOT1, vp = obj.vplayout(2:5,1))
print(obj.gg2.PLOT2, vp = obj.vplayout(2:5,2))
dev.off()
system(paste('lualatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE)
system(paste('rm ', scl.str.TEX_FILE, sep = ''))
system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = ''))
system(paste('rm ', scl.str.AUX_FILE, sep = ''))
system(paste('rm ', scl.str.LOG_FILE, sep = ''))
Raw
plot--naive-lambda-estimate--simulated-data--22jan2016.R
###########################################################################
###########################################################################
## @purpose: Estimate naive lambda in simulated data.
## ------------------------------------------------------------------------
## @author: Alex Chinco
## @date: 22-JAN-2016
###########################################################################
###########################################################################
###########################################################################
###########################################################################
## @section: Prep workspace
###########################################################################
###########################################################################
options(width=120, digits=6, digits.secs=6)
rm(list=ls())
library(foreign)
library(grid)
library(plyr)
library(ggplot2)
library(reshape)
library(R.matlab)
library(lubridate)
library(tikzDevice)
print(options('tikzLatexPackages'))
options(tikzLatexPackages =
c("\\usepackage{tikz}\n",
"\\usepackage[active,tightpage,psfixbb]{preview}\n",
"\\PreviewEnvironment{pgfpicture}\n",
"\\setlength\\PreviewBorder{0pt}\n",
"\\usepackage{amsmath}\n",
"\\usepackage{pxfonts}\n",
"\\usepackage{bm}\n",
"\\usepackage{xfrac}\n"
)
)
setTikzDefaults(overwrite = FALSE)
print(options('tikzLatexPackages'))
library(foreach)
library(doMC)
registerDoMC(2)
set.seed(123456)
scl.str.DAT_DIR <- "~/Dropbox/research/persistence_and_dispersion/data/"
scl.str.FIG_DIR <- "~/Dropbox/research/persistence_and_dispersion/figures/"
###########################################################################
###########################################################################
## @section: Define parameters
###########################################################################
###########################################################################
scl.int.H <- 2^8
vec.int.N <- c(2^2, 2^4, 2^6)
scl.int.NUM_N <- length(vec.int.N)
vec.flt.SIG_TET <- sqrt(seq(0.05, 0.95, by = 0.05))
vec.flt.SIG_EP <- sqrt(1 - vec.flt.SIG_TET^2)
scl.int.NUM_BREAKS <- length(vec.flt.SIG_TET)
scl.int.NUM_ITER <- 10^3
###########################################################################
###########################################################################
## @section: Estimate characteristic scale
###########################################################################
###########################################################################
if (TRUE == FALSE) {
mat.flt.PLOT <- foreach(g=1:scl.int.NUM_N, .combine = "rbind") %do% {
## for (g in 1:scl.int.NUM_N) {
mat.flt.OUTTER <- foreach(b=1:scl.int.NUM_BREAKS, .combine = "rbind") %do% {
## for (b in 1:scl.int.NUM_BREAKS) {
print(paste("N = ", vec.int.N[g], ", ", round(b/scl.int.NUM_BREAKS*100,2), "%: ", round(Sys.time()), sep = ""))
mat.flt.INNER <- foreach(i=1:scl.int.NUM_ITER, .combine = "rbind") %dopar% {
## for (i in 1:scl.int.NUM_ITER) {
mat.df.TEMP <- data.frame(n = 1:vec.int.N[g], tet = rnorm(vec.int.N[g], 0, vec.flt.SIG_TET[b]))
mat.df.TEMP <- foreach(l=1:(scl.int.H/vec.int.N[g]), .combine = "rbind") %do% { return(mat.df.TEMP) }
mat.df.TEMP <- mat.df.TEMP[order(mat.df.TEMP$n), ]
mat.df.TEMP$l <- rep(1:(scl.int.H/vec.int.N[g]), vec.int.N[g])
mat.df.TEMP$ep <- rnorm(scl.int.H, 0, vec.flt.SIG_EP[b])
mat.df.TEMP$p <- mat.df.TEMP$tet + mat.df.TEMP$ep
mat.df.TEMP <- mat.df.TEMP[, c("l", "n", "tet", "ep", "p")]
mat.df.NAIVE <- ddply(mat.df.TEMP, c("n"), function(X)mean(X$p))
names(mat.df.NAIVE) <- c("n", "pBar")
scl.flt.VAR_TET <- (vec.int.N[g]/(vec.int.N[g] - 1)) * var(mat.df.NAIVE$pBar)
scl.flt.VAR_P <- (scl.int.H/(scl.int.H - 1)) * var(mat.df.TEMP$p)
return(c(scl.flt.VAR_TET, scl.flt.VAR_P))
}
return(c(vec.int.N[g], vec.flt.SIG_TET[b]^2, apply(mat.flt.INNER, 2, mean)))
}
return(mat.flt.OUTTER)
}
mat.df.PLOT <- data.frame(mat.flt.PLOT)
names(mat.df.PLOT) <- c("N", "varTetStar", "varTet", "varP")
mat.df.PLOT$ratStar <- mat.df.PLOT$varTetStar
mat.df.PLOT$ratNaive <- mat.df.PLOT$varTet/mat.df.PLOT$varP
save(mat.df.PLOT, file = paste(scl.str.DAT_DIR, "data--naive-lambda-estimate--simulated-data--22jan2016.Rdata", sep = ""))
}
load(paste(scl.str.DAT_DIR, "data--naive-lambda-estimate--simulated-data--22jan2016.Rdata", sep = ""))
###########################################################################
###########################################################################
## @section: Plot results
###########################################################################
###########################################################################
mat.df.PLOT1 <- mat.df.PLOT[, c("ratStar", "N", "ratNaive")]
names(mat.df.PLOT1) <- c("ratStar", "variable", "value")
mat.df.PLOT1$variable <- paste("$N = ", mat.df.PLOT1$variable, "$", sep = "")
mat.df.PLOT1$variable <- factor(mat.df.PLOT1$variable, levels = c("$N = 4$", "$N = 16$", "$N = 64$"))
mat.df.PLOT2 <- mat.df.PLOT[, c("ratStar", "N", "ratNaive")]
names(mat.df.PLOT2) <- c("ratStar", "variable", "value")
mat.df.PLOT2$value <- mat.df.PLOT2$value/mat.df.PLOT2$ratStar - 1
mat.df.PLOT2$variable <- paste("$N = ", mat.df.PLOT2$variable, "$", sep = "")
mat.df.PLOT2$variable <- factor(mat.df.PLOT2$variable, levels = c("$N = 4$", "$N = 16$", "$N = 64$"))
obj.vplayout <- function(x, y) viewport(layout.pos.row = x, layout.pos.col = y)
theme_set(theme_bw())
scl.str.RAW_FILE <- 'plot--naive-lambda-estimate--simulated-data--22jan2016'
scl.str.TEX_FILE <- paste(scl.str.RAW_FILE,'.tex',sep='')
scl.str.PDF_FILE <- paste(scl.str.RAW_FILE,'.pdf',sep='')
scl.str.PNG_FILE <- paste(scl.str.RAW_FILE,'.png',sep='')
scl.str.AUX_FILE <- paste(scl.str.RAW_FILE,'.aux',sep='')
scl.str.LOG_FILE <- paste(scl.str.RAW_FILE,'.log',sep='')
tikz(file = scl.str.TEX_FILE, height = 2, width = 7, standAlone=TRUE)
## Left panel
obj.gg2.PLOT1 <- ggplot(data = mat.df.PLOT1)
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + scale_colour_brewer(palette = "Set1")
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + annotate("segment",
x = 0.05,
xend = 0.95,
y = 0.05,
yend = 0.95,
colour = "black",
linetype = 4,
size = 1
)
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + geom_path(aes(x = ratStar,
y = value,
group = variable,
colour = variable
),
size = 2,
alpha = 0.75
)
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + coord_cartesian(xlim = c(0.00, 1.00), ylim = c(0.02, 0.98))
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + scale_x_continuous(breaks = c(0.05, 0.20, 0.50, 0.80, 0.95))
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + scale_y_continuous(breaks = c(0.05, 0.20, 0.50, 0.80, 0.95))
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + xlab("$\\lambda = \\sfrac{\\sigma_N^2}{(\\sigma_N^2 + \\sigma_H^2)}$")
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + ylab('$\\lambda^{\\scriptscriptstyle \\text{Na\\"{i}ve}}$')
obj.gg2.PLOT1 <- obj.gg2.PLOT1 + theme(plot.margin = unit(c(0,0.15,0.15,0.15), "lines"),
axis.text = element_text(size = 10),
axis.title = element_text(size = 10),
plot.title = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "none"
)
## Right panel
obj.gg2.PLOT2 <- ggplot(data = mat.df.PLOT2)
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_colour_brewer(palette = "Set1")
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + annotate("segment",
x = 0.05,
xend = 0.95,
y = 0.00,
yend = 0.00,
colour = "black",
linetype = 4,
size = 1
)
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + geom_path(aes(x = ratStar,
y = value,
group = variable,
colour = variable
),
size = 2,
alpha = 0.75
)
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + xlab("$\\lambda = \\sfrac{\\sigma_N^2}{(\\sigma_N^2 + \\sigma_H^2)}$")
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + ylab('$(\\lambda^{\\scriptscriptstyle \\text{Na\\"{i}ve}} - \\lambda)/\\lambda$')
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + labs(colour = "")
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + coord_cartesian(xlim = c(0.00, 1.00), ylim = c(-0.25,2.50))
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_x_continuous(breaks = c(0.05, 0.20, 0.50, 0.80, 0.95))
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + scale_y_continuous(breaks = c(0, 0.75, 1.5, 2.25))
obj.gg2.PLOT2 <- obj.gg2.PLOT2 + theme(plot.margin = unit(c(0,0.15,0.15,0.15), "lines"),
axis.text = element_text(size = 10),
axis.title = element_text(size = 10),
plot.title = element_blank(),
panel.grid.minor = element_blank(),
legend.position = c(0.75, 0.75),
legend.background = element_blank()
)
## Generate output
pushViewport(viewport(layout = grid.layout(5, 2)))
grid.text('Bias in $\\text{Na\\"{i}ve}$ Estimates of Neighborhood-Level Variance', vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2), vjust = 0.20, gp = gpar(fontsize=15))
print(obj.gg2.PLOT1, vp = obj.vplayout(2:5,1))
print(obj.gg2.PLOT2, vp = obj.vplayout(2:5,2))
dev.off()
system(paste('lualatex', file.path(scl.str.TEX_FILE)), ignore.stdout = TRUE)
system(paste('rm ', scl.str.TEX_FILE, sep = ''))
system(paste('mv ', scl.str.PDF_FILE, ' ', scl.str.FIG_DIR, sep = ''))
system(paste('rm ', scl.str.AUX_FILE, sep = ''))
system(paste('rm ', scl.str.LOG_FILE, sep = ''))
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