prepared by Volkan OBAN Advanced Data Visualization in R- Somes Examples.

1. Prepared by Volkan OBAN
Advanced Data Visualization in R- Somes Examples.
geomorph package in R....
Example:
Code:
>library(geomorph)
> data(scallopPLY)
> ply <- scallopPLY$ply
> digitdat <- scallopPLY$coords
> plotspec(spec=ply,digitspec=digitdat,fixed=16, centered =TRUE)
Example:
> data(scallops)
> Y.gpa<-gpagen(A=scallops$coorddata, curves=scallops$curvslide,
surfaces=scallops$surfslide)
> ref<-mshape(Y.gpa$coords)
> plotRefToTarget(ref,Y.gpa$coords[,,1],method="TPS", mag=3)

2. Reference:
http://www.public.iastate.edu/~dcadams/PDFPubs/Quick%20Guide%20to%20Geomorph%20v2.0.pdf
Example:
> boxplot.ej <- function(y, xloc = 1, width.box = 0.25, lwd.box = 2, width
.hor = 0.25,
+ lwd.hor = 2, range.wisk = 1.5, lwd.wisk = 2, pch.
box = 16, cex.boxpoint = 2,
+ plot.outliers = FALSE, pch.out = 1, cex.out = 1,
color = "black") {
+
+ # makes boxplot with dot as median and solid whisker Interquartile r
ange =
+ # (.75 quantile) - (.25 quantile). Note: Wiskers are not always sym
metrical;
+ # top wisker extends up to max(y) constrained by y <= (.75 quantile)
+
+ # range.wisk*Interquartile range bottom whisker is determined by min
(y)

3. + # constrained by y >= (.25 quantile) - range.wisk*Interquartile rang
e
+
+ Q <- quantile(y, c(0.25, 0.5, 0.75))
+ names(Q) <- NULL # gets rid of percentages
+ IQ.range <- Q[3] - Q[1]
+ low <- Q[1] - range.wisk * IQ.range
+ high <- Q[3] + range.wisk * IQ.range
+ index <- which((y >= low) & (y <= high))
+ wisk.low <- min(y[index])
+ wisk.high <- max(y[index])
+ outliers <- y[which((y < low) | (y > high))]
+
+ # plot median:
+ points(xloc, Q[2], pch = pch.box, cex = cex.boxpoint, col = color)
+
+ # plot box:
+ xleft <- xloc - width.box/2
+ xright <- xloc + width.box/2
+ ybottom <- Q[1]
+ ytop <- Q[3]
+ rect(xleft, ybottom, xright, ytop, lwd = lwd.box, border = color)
+
+ # plot whiskers:
+ segments(xloc, wisk.low, xloc, Q[1], lwd = lwd.wisk, col = color)
+ segments(xloc, Q[3], xloc, wisk.high, lwd = lwd.wisk, col = color)
+
+ # plot horizontal segments:
+ x0 <- xloc - width.hor/2
+ x1 <- xloc + width.hor/2
+ segments(x0, wisk.low, x1, wisk.low, lwd = lwd.hor, col = color)
+ segments(x0, wisk.high, x1, wisk.high, lwd = lwd.hor, col = color)
+
+ # plot outliers:
+ if (plot.outliers == TRUE) {
+ xloc.p <- rep(xloc, length(outliers))
+ points(xloc.p, outliers, pch = pch.out, cex = cex.out, col = col
or)
+ }
+ }
>
> RT.hf.sp <- rnorm(1000, mean = 0.41, sd = 0.008)
> RT.lf.sp <- rnorm(1000, mean = 0.43, sd = 0.01)
> RT.vlf.sp <- rnorm(1000, mean = 0.425, sd = 0.012)
> RT.hf.ac <- rnorm(1000, mean = 0.46, sd = 0.008)
> RT.lf.ac <- rnorm(1000, mean = 0.51, sd = 0.01)
> RT.vlf.ac <- rnorm(1000, mean = 0.52, sd = 0.012)
>
> ps <- 1 # size of boxpoint
> par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.l
ab = 1.5,
+ font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
> x <- c(1, 2, 3, 4)
> plot(x, c(-10, -10, -10, -10), type = "p", ylab = " ", xlab = " ", cex =
1.5,
+ ylim = c(0.3, 0.6), xlim = c(1, 4), lwd = 2, pch = 5, axes = FALSE,
main = " ")
> axis(1, at = c(1.5, 2.5, 3.5), labels = c("HF", "LF", "VLF"))
> mtext("Word Frequency", side = 1, line = 3, cex = 1.5, font = 2)
> axis(2, pos = 1.1)
> par(las = 0)
> mtext("Group Mean M", side = 2, line = 2.9, cex = 1.5, font = 2)
>
> x <- c(1.5, 2.5, 3.5)
> boxplot.ej(RT.hf.sp, xloc = 1.5, cex.boxpoint = ps)
> boxplot.ej(RT.hf.ac, xloc = 1.5, cex.boxpoint = ps, color = "grey")
> boxplot.ej(RT.lf.sp, xloc = 2.5, cex.boxpoint = ps)
> boxplot.ej(RT.lf.ac, xloc = 2.5, cex.boxpoint = ps, color = "grey")
> boxplot.ej(RT.vlf.sp, xloc = 3.5, cex.boxpoint = ps)

4. > boxplot.ej(RT.vlf.ac, xloc = 3.5, cex.boxpoint = ps, color = "grey")
>
> text(2.5, 0.35, "Speed", cex = 1.4, font = 1, adj = 0.5)
> text(2.5, 0.57, "Accuracy", cex = 1.4, font = 1, col = "grey", adj = 0.5
)
>
>
"
> RT.hf.sp <- rnorm(1000, mean = 0.41, sd = 0.008)
> RT.lf.sp <- rnorm(1000, mean = 0.43, sd = 0.01)
> RT.vlf.sp <- rnorm(1000, mean = 0.425, sd = 0.012)
> RT.hf.ac <- rnorm(1000, mean = 0.46, sd = 0.008)
> RT.lf.ac <- rnorm(1000, mean = 0.51, sd = 0.01)
> RT.vlf.ac <- rnorm(1000, mean = 0.52, sd = 0.012)
>
> library(sm)
> # by Henrik Singmann customized violinplot function (singmann.org) the
> # original violinplot function stems from the 'vioplot' package Copyrigh
t (c)
> # 2004, Daniel Adler. All rights reserved. Redistribution and use in so
urce
> # and binary forms, with or without modification, are permitted provided
that
> # the following conditions are met: * Redistributions of source code mus
t
> # retain the above copyright notice, this list of conditions and the
> # following disclaimer. * Redistributions in binary form must reproduce
the
> # above copyright notice, this list of conditions and the following
> # disclaimer in the documentation and/or other materials provided with t
he
> # distribution. * Neither the name of the University of Goettingen nor
the
> # names of its contributors may be used to endorse or promote products
> # derived from this software without specific prior written permission.
THIS
> # SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 'AS IS'
AND
> # ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
> # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PUR
POSE
> # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS
BE
> # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
> # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
> # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINE
SS
> # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER I
N
> # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE
)
> # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
THE
> # POSSIBILITY OF SUCH DAMAGE.
>
> vioplot.singmann <- function(x, ..., range = 1.5, h = NULL, ylim = NULL,
names = NULL,
+ horizontal = FALSE, col = NULL, border = "b
lack", lty = 1, lwd = 1, rectCol = "black",
+ colMed = "white", pchMed = 19, at, add = FA
LSE, wex = 1, mark.outlier = TRUE,
+ pch.mean = 4, ids = NULL, drawRect = TRUE,
yaxt = "s") {
+
+ # process multiple datas
+ datas <- list(x, ...)
+ n <- length(datas)
+ if (missing(at))

5. + at <- 1:n
+ # pass 1 - calculate base range - estimate density setup parameters
for
+ # density estimation
+ upper <- vector(mode = "numeric", length = n)
+ lower <- vector(mode = "numeric", length = n)
+ q1 <- vector(mode = "numeric", length = n)
+ q3 <- vector(mode = "numeric", length = n)
+ med <- vector(mode = "numeric", length = n)
+ base <- vector(mode = "list", length = n)
+ height <- vector(mode = "list", length = n)
+ outliers <- vector(mode = "list", length = n)
+ baserange <- c(Inf, -Inf)
+
+ # global args for sm.density function-call
+ args <- list(display = "none")
+
+ if (!(is.null(h)))
+ args <- c(args, h = h)
+ for (i in 1:n) {
+ data <- datas[[i]]
+ if (!is.null(ids))
+ names(data) <- ids
+ if (is.null(names(data)))
+ names(data) <- as.character(1:(length(data)))
+
+ # calculate plot parameters 1- and 3-quantile, median, IQR, uppe
r- and
+ # lower-adjacent
+ data.min <- min(data)
+ data.max <- max(data)
+ q1[i] <- quantile(data, 0.25)
+ q3[i] <- quantile(data, 0.75)
+ med[i] <- median(data)
+ iqd <- q3[i] - q1[i]
+ upper[i] <- min(q3[i] + range * iqd, data.max)
+ lower[i] <- max(q1[i] - range * iqd, data.min)
+
+ # strategy: xmin = min(lower, data.min)) ymax = max(upper, data.
max))
+ est.xlim <- c(min(lower[i], data.min), max(upper[i], data.max))
+
+ # estimate density curve
+ smout <- do.call("sm.density", c(list(data, xlim = est.xlim), ar
gs))
+
+ # calculate stretch factor the plots density heights is defined
in range 0.0
+ # ... 0.5 we scale maximum estimated point to 0.4 per data
+ hscale <- 0.4/max(smout$estimate) * wex
+
+ # add density curve x,y pair to lists
+ base[[i]] <- smout$eval.points
+ height[[i]] <- smout$estimate * hscale
+ t <- range(base[[i]])
+ baserange[1] <- min(baserange[1], t[1])
+ baserange[2] <- max(baserange[2], t[2])
+ min.d <- boxplot.stats(data)[["stats"]][1]
+ max.d <- boxplot.stats(data)[["stats"]][5]
+ height[[i]] <- height[[i]][(base[[i]] > min.d) & (base[[i]] < ma
x.d)]
+ height[[i]] <- c(height[[i]][1], height[[i]], height[[i]][length
(height[[i]])])
+ base[[i]] <- base[[i]][(base[[i]] > min.d) & (base[[i]] < max.d)
]
+ base[[i]] <- c(min.d, base[[i]], max.d)
+ outliers[[i]] <- list(data[(data < min.d) | (data > max.d)], nam
es(data[(data <

6. +
min.d) | (data > max.d)]))
+
+ # calculate min,max base ranges
+ }
+ # pass 2 - plot graphics setup parameters for plot
+ if (!add) {
+ xlim <- if (n == 1)
+ at + c(-0.5, 0.5) else range(at) + min(diff(at))/2 * c(-1, 1
)
+
+ if (is.null(ylim)) {
+ ylim <- baserange
+ }
+ }
+ if (is.null(names)) {
+ label <- 1:n
+ } else {
+ label <- names
+ }
+ boxwidth <- 0.05 * wex
+
+ # setup plot
+ if (!add)
+ plot.new()
+ if (!horizontal) {
+ if (!add) {
+ plot.window(xlim = xlim, ylim = ylim)
+ axis(2)
+ axis(1, at = at, label = label)
+ }
+
+ box()
+ for (i in 1:n) {
+ # plot left/right density curve
+ polygon(c(at[i] - height[[i]], rev(at[i] + height[[i]])), c(
base[[i]],
+
rev(base[[i]])), col = col, border = border, lty = lty, lwd = lwd)
+
+ if (drawRect) {
+ # browser() plot IQR
+ boxplot(datas[[i]], at = at[i], add = TRUE, yaxt = yaxt,
pars = list(boxwex = 0.6 *
+
wex, outpch = if (mark.outlier) "" else 1))
+ if ((length(outliers[[i]][[1]]) > 0) & mark.outlier)
+ text(rep(at[i], length(outliers[[i]][[1]])), outlier
s[[i]][[1]],
+ labels = outliers[[i]][[2]])
+ # lines( at[c( i, i)], c(lower[i], upper[i]) ,lwd=lwd, l
ty=lty) plot 50% KI
+ # box rect( at[i]-boxwidth/2, q1[i], at[i]+boxwidth/2, q
3[i], col=rectCol)
+ # plot median point points( at[i], med[i], pch=pchMed, c
ol=colMed )
+ }
+ points(at[i], mean(datas[[i]]), pch = pch.mean, cex = 1.3)
+ }
+ } else {
+ if (!add) {
+ plot.window(xlim = ylim, ylim = xlim)
+ axis(1)
+ axis(2, at = at, label = label)
+ }
+
+ box()
+ for (i in 1:n) {
+ # plot left/right density curve

7. + polygon(c(base[[i]], rev(base[[i]])), c(at[i] - height[[i]],
rev(at[i] +
+
height[[i]])), col = col, border = border, lty = lty, lwd = lwd)
+
+ if (drawRect) {
+ # plot IQR
+ boxplot(datas[[i]], yaxt = yaxt, at = at[i], add = TRUE,
pars = list(boxwex = 0.8 *
+
wex, outpch = if (mark.outlier) "" else 1))
+ if ((length(outliers[[i]][[1]]) > 0) & mark.outlier)
+ text(rep(at[i], length(outliers[[i]][[1]])), outlier
s[[i]][[1]],
+ labels = outliers[[i]][[2]])
+ # lines( at[c( i, i)], c(lower[i], upper[i]) ,lwd=lwd, l
ty=lty)
+ }
+ points(at[i], mean(datas[[i]]), pch = pch.mean, cex = 1.3)
+ }
+ }
+ invisible(list(upper = upper, lower = lower, median = med, q1 = q1,
q3 = q3))
+ }
>
> # plot
> par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.l
ab = 1.5,
+ font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
> x <- c(1, 2, 3, 4)
> plot(x, c(-10, -10, -10, -10), type = "p", ylab = " ", xlab = " ", cex =
1.5,
+ ylim = c(0.3, 0.6), xlim = c(1, 4), lwd = 2, pch = 5, axes = F, mai
n = " ")
> axis(1, at = c(1.5, 2.5, 3.5), labels = c("HF", "LF", "VLF"))
> axis(2, pos = 1.1)
> mtext("Word Frequency", side = 1, line = 3, cex = 1.5, font = 2)
>
> par(las = 0)
> mtext("Group Mean M", side = 2, line = 2.9, cex = 1.5, font = 2)
>
> x <- c(1.5, 2.5, 3.5)
>
> vioplot.singmann(RT.hf.sp, RT.lf.sp, RT.vlf.sp, add = TRUE, mark.outlier
= FALSE,
+ at = c(1.5, 2.5, 3.5), wex = 0.4, yaxt = "n")
> vioplot.singmann(RT.hf.ac, RT.lf.ac, RT.vlf.ac, add = TRUE, mark.outlier
= FALSE,
+ at = c(1.5, 2.5, 3.5), wex = 0.4, col = "grey", border
= "grey", rectCol = "grey",
+ colMed = "grey", yaxt = "n")
>
> text(2.5, 0.35, "Speed", cex = 1.4, font = 1, adj = 0.5)
> text(2.5, 0.58, "Accuracy", cex = 1.4, font = 1, col = "grey", adj = 0.5
)

8. Example:
plotsebargraph = function(loc, value, sterr, wiskwidth, color = "grey", lin
ewidth = 2) {
w = wiskwidth/2
segments(x0 = loc, x1 = loc, y0 = value, y1 = value + sterr, col = colo
r,
lwd = linewidth)
segments(x0 = loc - w, x1 = loc + w, y0 = value + sterr, y1 = value + s
terr,
col = color, lwd = linewidth) # upper whiskers
}
plotsegraph = function(loc, value, sterr, wiskwidth, color = "grey", linewi
dth = 2) {
w = wiskwidth/2
segments(x0 = loc, x1 = loc, y0 = value - sterr, y1 = value + sterr, co
l = color,

9. lwd = linewidth)
segments(x0 = loc - w, x1 = loc + w, y0 = value + sterr, y1 = value + s
terr,
col = color, lwd = linewidth) # upper whiskers
segments(x0 = loc - w, x1 = loc + w, y0 = value - sterr, y1 = value - s
terr,
col = color, lwd = linewidth) # lower whiskers
}
# =======================================================
# Data; order = Speed, neutral, accuracy
MRT <- c(429, 515, 555)
MRT.se <- c(25, 25, 30)
Er <- c(0.23, 0.14, 0.13)
Er.se <- c(0.022, 0.021, 0.021)
# ======================================================
par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.lab
= 1.5,
font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
# mpg = c(3, 1, 0) is default. first = axis labels!; middle = tick labels m
ar
# = c(5, 4, 4, 2) + 0.1 is default
digitsize <- 1.2
x <- c(1, 2, 3, 4)
plot(x, c(-10, -10, -10, -10), type = "p", ylab = " Mean Response Time (ms.
)",
xlab = " ", cex = 1.5, ylim = c(200, 800), xlim = c(1, 4), lwd = 2, pch
= 5,
axes = F, main = " ")
axis(1, at = c(1.5, 2.5, 3.5), labels = c("Speed", "Neutral", "Accuracy"))
mtext("Cue", side = 1, line = 3, cex = 1.5, font = 2)
axis(2, at = c(300, 400, 500, 600, 700))
x = c(1.5, 2.5, 3.5)

10. points(x, MRT, cex = 1.5, lwd = 2, pch = 19)
plot.errbars = plotsegraph(x, MRT, MRT.se, 0.1, color = "black") #0.1 = wi
skwidth
lines(c(1.5, 2.5, 3.5), MRT, lwd = 2, type = "c")
text(1.5, MRT[1] + 60, "429", adj = 0.5, cex = digitsize)
text(2.5, MRT[2] + 60, "515", adj = 0.5, cex = digitsize)
text(3.5, MRT[3] + 60, "555", adj = 0.5, cex = digitsize)
par(new = TRUE)
x <- c(1, 2, 3, 4)
plot(x, c(-10, -10, -10, -10), type = "p", ylab = " ", xlab = " ", cex = 1.
5,
ylim = c(0, 1), xlim = c(1, 4), lwd = 2, axes = FALSE, main = " ")
axis(4, at = c(0, 0.1, 0.2, 0.3, 0.4), las = 1)
grid::grid.text("Mean Proportion of Errors", 0.97, 0.5, rot = 270, gp = gri
d::gpar(cex = 1.5,
font = 2))
width <- 0.25
linewidth <- 2
x0 <- 1.5 - width
x1 <- 1.5 + width
y0 <- 0
y1 <- Er[1]
segments(x0, y0, x0, y1, lwd = linewidth)
segments(x0, y1, x1, y1, lwd = linewidth)
segments(x1, y1, x1, y0, lwd = linewidth)
segments(x1, y0, x0, y0, lwd = linewidth)
x0 <- 2.5 - width
x1 <- 2.5 + width
y0 <- 0
y1 <- Er[2]
segments(x0, y0, x0, y1, lwd = linewidth)
segments(x0, y1, x1, y1, lwd = linewidth)
segments(x1, y1, x1, y0, lwd = linewidth)
segments(x1, y0, x0, y0, lwd = linewidth)

11. x0 <- 3.5 - width
x1 <- 3.5 + width
y0 <- 0
y1 <- Er[3]
segments(x0, y0, x0, y1, lwd = linewidth)
segments(x0, y1, x1, y1, lwd = linewidth)
segments(x1, y1, x1, y0, lwd = linewidth)
segments(x1, y0, x0, y0, lwd = linewidth)
loc.errbars <- c(1.5, 2.5, 3.5)
plot.errbars <- plotsebargraph(loc.errbars, Er, Er.se, 0.2, color = "black"
) # 0.2 = wiskwidth
text(1.5, 0.9, "Behavioral Data", font = 2, cex = 2, pos = 4)
text(1.5, 0.05, "0.23", adj = 0.5, cex = digitsize)
text(2.5, 0.05, "0.14", adj = 0.5, cex = digitsize)
text(3.5, 0.05, "0.13", adj = 0.5, cex = digitsize)

12. Example:
xbar.therapy <- 92
s.therapy <- 8.5
xbar.placebo <- 85
s.placebo <- 9.1
n <- 15
xdiff <- xbar.therapy - xbar.placebo
sdiff <- sqrt((s.therapy^2 + s.placebo^2)/2) * sqrt(2/n)
sdiff <- sqrt(s.therapy^2 + s.placebo^2)/sqrt(n)
muH0 <- 0
muH1 <- 8
t0 <- (xdiff - muH0)/sdiff
par(cex.main = 1.5, mar = c(4, 4.5, 4.5, 1), mgp = c(3.5, 1, 0), cex.lab =
1.5,
font.lab = 2, cex.axis = 1.8, bty = "n", las = 1)
par(mar = c(4, 4.5, 4.5, 1)
x <- seq(-15, 30, by = 0.001)
y <- dt(x/sdiff, df = 28)
y3 <- dt((x - 9)/sdiff, df = 28)
plot(x, y, type = "l", axes = FALSE, xlab = NA, ylab = NA, xlim = c(-15, 25
),
lwd = 2)
lines(x, y3, lwd = 2)
axis(side = 1, at = seq(-15, 30, by = 5), pos = 0, lwd = 2, cex.axis = 1.7)
axis(side = 1, at = 7, pos = 0, col = "red4", col.axis = "red4", lwd = 2, p
adj = 0.1)
abline(v = xdiff, col = "red4", lwd = 2)
L0 <- dt((xdiff/sdiff), df = 28)
L2 <- dt(((xdiff - 9)/sdiff), df = 28)
lines(c(6.7, 7.3), y = rep(L0, 2), col = "red4", lwd = 2)
lines(c(6.7, 7.3), y = rep(L2, 2), col = "red4", lwd = 2)
text(8, L0, expression(paste(italic("L"), " = .04")), adj = 0, col = "red4"
,
cex = 1.8)

13. text(7.5, L2, expression(paste(italic("L"), " = .32")), adj = 0, col = "red
4",
cex = 1.8)
text(-16, 0.35, expression(paste(H[0], " : ", mu[diff], " = 0", sep = "")),
adj = 0,
cex = 1.8)
text(-16, 0.3, expression(paste(H[1], " : ", mu[diff], " = 9", sep = "")),
adj = 0,
cex = 1.8)
mtext(expression(bar(x)[diff]), side = 1, line = 2, at = 6.5, adj = 0, col
= "red4",
cex = 1.8, padj = 0.1)
text(14, 0.2, expression(paste("LR = ", frac(".32", ".04") %~~% 8, sep = ""
)),
adj = 0, col = "red4", cex = 1.8)
Example:

14. Max.BF10 = function(p) {
# Computes the upper bound on the Bayes factor As in Sellke, Bayarri, &
# Berger, 2001
Max.BF10 <- -1/(exp(1) * p * log(p))
return(Max.BF10)
}
# Plot this function for p in .001 to .1
xlow <- 0.001
xhigh <- 0.1
p1 <- 0.0373
p2 <- 0.00752
p3 <- 0.001968
par(cex.main = 1.5, mar = c(5, 6, 4, 5) + 0.1, mgp = c(3.5, 1, 0), cex.lab
= 1.5,
font.lab = 2, cex.axis = 1.3, bty = "n", las = 1)
plot(function(p) Max.BF10(p), xlow, xhigh, xlim = c(xlow, xhigh), lwd = 2,
xlab = " ",
ylab = " ")
mtext("Two-sided p value", 1, line = 2.5, cex = 1.5, font = 2)
mtext("Maximum Bayes factor for H1", 2, line = 2.8, cex = 1.5, font = 2, la
s = 0)
lines(c(0, p1), c(3, 3), lwd = 2, col = "grey")
lines(c(0, p2), c(10, 10), lwd = 2, col = "grey")
lines(c(0, p3), c(30, 30), lwd = 2, col = "grey")
lines(c(p1, p1), c(0, 3), lwd = 2, col = "grey")
lines(c(p2, p2), c(0, 10), lwd = 2, col = "grey")
lines(c(p3, p3), c(0, 30), lwd = 2, col = "grey")
cexsize <- 1.2
text(0.005, 31, expression(max((BF[10])) == 30 %<->% p %~~% 0.002), cex = c
exsize,
pos = 4)
text(0.01, 11, expression(max((BF[10])) == 10 %<->% p %~~% 0.008), cex = ce
xsize,
pos = 4)
text(p1 - 0.005, 5, expression(max((BF[10])) == 3 %<->% p %~~% 0.037), cex
= cexsize,

15. pos = 4)
Example:
# rm(list = ls())
IndividualPerformance <- function(choice, lo, show.losses = FALSE) {
# Plots the choice profile Args: choice: A vector containing the choice
s on
# each trial lo: A vector containing the losses on each trial show.loss
es:
# logical: Should the losses be indicated by filled dots?
par(mar = c(4, 4.5, 0.5, 1))
plot(choice, type = "b", axes = FALSE, xlab = "Trial", ylab = "Deck", c
ex.lab = 2)
axis(1, seq(0, 100, length = 6), cex.axis = 1.8)
axis(2, 1:4, labels = c("A", "B", "C", "D"), cex.axis = 1.8, las = 1)

16. if (show.losses == TRUE) {
index.losses <- which(lo < 0)
points(matrix(c(index.losses, choice[index.losses]), byrow = FALSE,
nrow = length(index.losses)),
pch = 19, lwd = 1.5)
}
}
# Synthetic data
choice <- sample(1:4, 100, replace = TRUE)
lo <- sample(c(-1250, -250, -50, 0), 100, replace = TRUE)
# postscript('DiversePerformance.eps', width = 7, height = 7)
IndividualPerformance(choice, lo, show.losses = TRUE)
# dev.off()

17. Example:
library(plotrix)
# mix of 2 normal distributions
mixedNorm <- function(x) {
return(0.5 * dnorm(x, 0.25, 0.13) + 0.5 * dnorm(x, 0.7, 0.082))
}
### normalize so that area [0,1] integrates to 1; k = normalizing constant
k <- 1/integrate(mixedNorm, 0, 1)$value
# normalized
pdfmix <- function(x, k) {
return(k * (0.5 * dnorm(x, 0.25, 0.13) + 0.5 * dnorm(x, 0.7, 0.082)))
}
# integrate(pdfmix, 0.0790321,0.4048)$value # 0.4
op <- par(mfrow = c(1, 2), mar = c(5.9, 6, 4, 2) + 0.1)
barplot(height = c(0.2, 0.25, 0.1, 0.05, 0.35, 0.05), names.arg = c(1,
2, 3, 4, 5, 6), axes = FALSE, ylim = c(0, 1), width = 1, cex.names = 1.
5)
arrows(x0 = 0.6, x1 = 0.6, y0 = 0.38, y1 = 0.23, length = c(0.2, 0.2),
lwd = 2)
text(0.6, 0.41, "0.2", cex = 1.3)
ablineclip(v = 1.9, y1 = 0.28, y2 = 0.375, lwd = 2)
ablineclip(v = 4.2, y1 = 0.28, y2 = 0.375, lwd = 2)
ablineclip(h = 0.375, x1 = 1.9, x2 = 4.2, lwd = 2)
arrows(x0 = 3.05, x1 = 3.05, y0 = 0.525, y1 = 0.375, length = c(0.2, 0.2),
lwd = 2)
text(3.05, 0.555, "0.4", cex = 1.3)
ablineclip(v = 5.5, y1 = 0.38, y2 = 0.43, lwd = 2)
arrows(x0 = 6.7, x1 = 6.7, y0 = 0.43, y1 = 0.09, length = c(0.2, 0.2),
lwd = 2)
ablineclip(h = 0.43, x1 = 5.5, x2 = 6.7, lwd = 2)
text(6.1, 0.46, "7 x", cex = 1.3)
par(las = 1)

18. axis(2, at = seq(0, 1, 0.1), labels = seq(0, 1, 0.1), lwd = 2, cex.axis = 1
.3)
par(las = 0)
mtext("Probability Mass", side = 2, line = 3.7, cex = 2)
mtext("Value", side = 1, line = 3.7, cex = 2)
par(mar = c(4.6, 6, 3.3, 2) + 0.1)
xx <- c(0.0790321, 0.079031, seq(0.08, 0.4, 0.01), 0.4084, 0.4084)
yy <- c(0, pdfmix(0.079031, k = k), pdfmix(seq(0.08, 0.4, 0.01), k = k), pd
fmix(0.4084, k = k),
0)
plot(1, type = "n", axes = FALSE, ylab = "", xlab = "", xlim = c(0, 1),
ylim = c(0, 3))
polygon(xx, yy, col = "grey", border = NA)
curve(pdfmix(x, k = k), from = 0, to = 1, lwd = 2, ylab = "", xlab = "", xl
im = c(0,
1), ylim = c(0, 3), add = TRUE)
text(0.25, 0.7, "0.4", cex = 1.3)
par(las = 1)
axis(2, at = seq(0, 3, 0.5), labels = seq(0, 3, 0.5), lwd = 2, cex.axis = 1
.3)
points(0.539580297, pdfmix(0.539580297, k = k), pch = 21, bg = "white", cex
= 1.4,
lwd = 2.7)
points(uniroot(function(x) pdfmix(x, k = k) - 5 * pdfmix(0.539580297, k = k
), interval = c(0.56,
0.7))$root, pdfmix(uniroot(function(x) pdfmix(x, k = k) - 5 * pdfmix(0.
539580297, k = k),
interval = c(0.56, 0.7))$root, k = k), pch = 21, bg = "white", cex = 1.
4,
lwd = 2.7)
arrows(x0 = 0.539580297, x1 = 0.539580297, y0 = 2.7, y1 = 0.7, length = c(0
.17,
0.17), angle = 19, lwd = 2)
ablineclip(h = 2.7, x1 = 0.539580297, x2 = 0.6994507, lwd = 2)
ablineclip(v = 0.6994507, y1 = 2.55, y2 = 2.7, lwd = 2)
text(0.6194593, 2.79, "5 x", cex = 1.3)
axis(1, at = seq(0, 1, 0.1), labels = c("0", ".1", ".2", ".3", ".4", ".5",
".6", ".7", ".8", ".9", "1"), line = -1.2, lwd = 2, cex.axis = 1.37)
par(las = 0)

19. mtext("Probability Density", side = 2, line = 3.7, cex = 2)
mtext("Value", side = 1, line = 2.4, cex = 2)
par(op)
Example:
library("psych")
library("qgraph")
# Load BFI data:
data(bfi)
bfi <- bfi[, 1:25]
# Groups and names object (not needed really, but make the plots easier to
# interpret):
Names <- scan("http://sachaepskamp.com/files/BFIitems.txt", what = "charact
er", sep = "n")

20. # Create groups object:
Groups <- rep(c("A", "C", "E", "N", "O"), each = 5)
# Compute correlations:
cor_bfi <- cor_auto(bfi)
# Plot correlation network:
graph_cor <- qgraph(cor_bfi, layout = "spring", nodeNames = Names, groups =
Groups, legend.cex = 0.6,
DoNotPlot = TRUE)
# Plot partial correlation network:
graph_pcor <- qgraph(cor_bfi, graph = "concentration", layout = "spring", n
odeNames = Names,
groups = Groups, legend.cex = 0.6, DoNotPlot = TRUE)
# Plot glasso network:
graph_glas <- qgraph(cor_bfi, graph = "glasso", sampleSize = nrow(bfi), lay
out = "spring",
nodeNames = Names, legend.cex = 0.6, groups = Groups, legend.cex = 0.7,
GLratio = 2,
DoNotPlot = TRUE)
# centrality plot (all graphs):
centralityPlot(list(r = graph_cor, `Partial r` = graph_pcor, glasso = graph
_glas),
labels = Names) + labs(colour = "") + theme_bw() + theme(legend.positio
n = "bottom")

21. Example:
### prior & posterior parameters
mean.prior <- 75
sd.prior <- 12
mean.posterior <- 73.33644
sd.posterior <- 4.831067
### plot settings
xlim <- c(40, 115)
ylim <- c(0, 0.117)
lwd <- 2
lwd.points <- 2
lwd.axis <- 1.2
cex.points <- 1.4
cex.axis <- 1.2
cex.text <- 1.1

22. cex.labels <- 1.3
cexLegend <- 1.2
op <- par(mar = c(5.1, 4.1, 4.1, 2.1))
### create empty canvas
plot(1, xlim = xlim, ylim = ylim, axes = FALSE, xlab = "", ylab = "")
### shade prior area < 70
greycol1 <- rgb(0, 0, 0, alpha = 0.2)
greycol2 <- rgb(0, 0, 0, alpha = 0.4)
polPrior <- seq(xlim[1], 70, length.out = 400)
xx <- c(polPrior, polPrior[length(polPrior)], polPrior[1])
yy <- c(dnorm(polPrior, mean.prior, sd.prior), 0, 0)
polygon(xx, yy, col = greycol1, border = NA)
### shade posterior area < 70
polPosterior <- seq(xlim[1], 70, length.out = 400)
xx <- c(polPosterior, polPosterior[length(polPosterior)], polPosterior[1])
yy <- c(dnorm(polPosterior, mean.posterior, sd.posterior), 0, 0)
polygon(xx, yy, col = greycol2, border = NA)
### shade posterior area on interval (81, 84)
polPosterior2 <- seq(81, 84, length.out = 400)
xx <- c(polPosterior2, polPosterior2[length(polPosterior2)], polPosterior2[
1])
yy <- c(dnorm(polPosterior2, mean.posterior, sd.posterior), 0, 0)
polygon(xx, yy, col = greycol2, border = NA)
### grey dashed lines to prior mean, posterior mean and posterior at 77
lines(rep(mean.prior, 2), c(0, dnorm(mean.prior, mean.prior, sd.prior)), lt
y = 2, col = "grey",
lwd = lwd)
lines(rep(mean.posterior, 2), c(0, dnorm(mean.posterior, mean.posterior, sd
.posterior)),
lty = 2, col = "grey", lwd = lwd)

23. lines(rep(mean.posterior + (mean.posterior - 70), 2), c(0, dnorm(mean.poste
rior + (mean.posterior -
70), mean.posterior, sd.posterior)), lty = 2, col = "grey", lwd = lwd)
### axes
axis(1, at = seq(xlim[1], xlim[2], 5), cex.axis = cex.axis, lwd = lwd.axis)
axis(2, labels = FALSE, tck = 0, lwd = lwd.axis, line = -0.5)
### axes labels
mtext("IQ Bob", side = 1, cex = 1.6, line = 2.4)
mtext("Density", side = 2, cex = 1.5, line = 0)
### plot prior and posterior
# prior
plot(function(x) dnorm(x, mean.prior, sd.prior), xlim = xlim, ylim = ylim,
xlab = "",
ylab = "", lwd = lwd, lty = 3, add = TRUE)
# posterior
plot(function(x) dnorm(x, mean.posterior, sd.posterior), xlim = xlim, ylim
= ylim, add = TRUE,
lwd = lwd)
### add points
# posterior density at 70
points(70, dnorm(70, mean.posterior, sd.posterior), pch = 21, bg = "white",
cex = cex.points,
lwd = lwd.points)
# posterior density at 76.67
points(mean.posterior + (mean.posterior - 70), dnorm(mean.posterior + (mean
.posterior -
70), mean.posterior, sd.posterior), pch = 21, bg = "white", cex = cex.p
oints, lwd = lwd.points)
# maximum a posteriori value

24. points(mean.posterior, dnorm(mean.posterior, mean.posterior, sd.posterior),
pch = 21,
bg = "white", cex = cex.points, lwd = lwd.points)
### credible interval
CIlow <- qnorm(0.025, mean.posterior, sd.posterior)
CIhigh <- qnorm(0.975, mean.posterior, sd.posterior)
yCI <- 0.11
arrows(CIlow, yCI, CIhigh, yCI, angle = 90, code = 3, length = 0.1, lwd = l
wd)
text(mean.posterior, yCI + 0.0042, labels = "95%", cex = cex.text)
text(CIlow, yCI, labels = paste(round(CIlow, 2)), cex = cex.text, pos = 2,
offset = 0.3)
text(CIhigh, yCI, labels = paste(round(CIhigh, 2)), cex = cex.text, pos = 4
, offset = 0.3)
### legend
legendPosition <- 115
legend(legendPosition, ylim[2] + 0.002, legend = c("Posterior", "Prior"), l
ty = c(1,
3), bty = "n", lwd = c(lwd, lwd), cex = cexLegend, xjust = 1, yjust = 1
, x.intersp = 0.6,
seg.len = 1.2)
### draw labels
# A
arrows(x0 = 57, x1 = 61, y0 = dnorm(62, mean.prior, sd.prior) + 0.0003, y1
= dnorm(62,
mean.prior, sd.prior) - 0.007, length = c(0.08, 0.08), lwd = lwd, code
= 2)
text(55.94, dnorm(5, mean.prior, sd.prior) + 0.0205, labels = "A", cex = ce
x.labels)
# B
arrows(x0 = 64.5, x1 = 69, y0 = dnorm(68, mean.posterior, sd.posterior) + 0
.003, y1 = dnorm(68,
mean.posterior, sd.posterior) - 0.005, length = c(0.08, 0.08), lwd = lw
d, code = 2)

25. text(63.5, dnorm(68, mean.posterior, sd.posterior) + 0.0042, labels = "B",
cex = cex.labels)
# C
arrows(x0 = mean.posterior + 1, x1 = mean.posterior + 6, y0 = dnorm(mean.po
sterior, mean.posterior,
sd.posterior) + 0.001, y1 = dnorm(mean.posterior, mean.posterior, sd.po
sterior) +
0.008, length = c(0.08, 0.08), lwd = lwd, code = 1)
text(mean.posterior + 7, dnorm(mean.posterior, mean.posterior, sd.posterior
) + 0.0093,
labels = "C", cex = cex.labels)
# D
arrows(x0 = 70 - 0.25, x1 = 70 - 0.25, y0 = dnorm(70, mean.posterior, sd.po
sterior) +
0.005, y1 = 0.092, length = c(0.08, 0.08), lwd = lwd, code = 1)
lines(c(70 - 0.25, mean.posterior), rep(0.092, 2), lwd = lwd)
arrows(x0 = mean.posterior, x1 = mean.posterior, y0 = 0.092, y1 = dnorm(mea
n.posterior,
mean.posterior, sd.posterior) + 0.003, length = c(0.08, 0.08), lwd = lw
d, code = 2)
ratio <- dnorm(mean.posterior, mean.posterior, sd.posterior)/dnorm(70, mean
.posterior,
sd.posterior)
text(mean(c(70 - 0.255, mean.posterior)), 0.096, labels = paste(round(ratio
, 2), "x"),
cex = cex.text)
text(70 - 1.5, dnorm(70, mean.posterior, sd.posterior) + 0.02, labels = "D"
, cex = cex.labels)
# E
arrows(x0 = 70 + 1, x1 = mean.posterior + (mean.posterior - 70) - 1, y0 = d
norm(70, mean.posterior,
sd.posterior), y1 = dnorm(mean.posterior + (mean.posterior - 70), mean.
posterior,
sd.posterior), length = c(0.08, 0.08), lwd = lwd, code = 3)
text(74.9, dnorm(mean.posterior + (mean.posterior - 70), mean.posterior, sd
.posterior) -
0.005, labels = "E", cex = cex.labels)
# F

26. arrows(x0 = 82.5, x1 = 87, y0 = dnorm(82, mean.posterior, sd.posterior) - 0
.012, y1 = dnorm(82,
mean.posterior, sd.posterior) - 0.005, length = c(0.08, 0.08), lwd = lw
d, code = 1)
text(88, dnorm(82, mean.posterior, sd.posterior) - 0.0034, labels = "F", ce
x = cex.labels)
# G
arrows(x0 = CIhigh + 6, x1 = CIhigh + 8.2, y0 = yCI, y1 = yCI, length = c(0
.08, 0.08),
lwd = lwd, code = 1)
text(CIhigh + 9.5, yCI, labels = "G", cex = cex.labels)
### additional information
scores <- "Bob's IQ scores: {73, 67, 79}"
priorText1 <- "Prior distribution:"
priorText2 <- expression(paste("IQ Bob ~ N(", 75, ", ", 12^2, ")"))
posteriorText1 <- "Posterior distribution:"
posteriorText2 <- expression(paste("IQ Bob ~ N(", 73.34, ", ", 4.83^2, ")")
)
xx <- 87
yCI2 <- 0.12
text(xx, yCI2 - 0.033, labels = priorText1, cex = cexLegend, pos = 4, offse
t = 0.3)
text(xx, yCI2 - 0.042, labels = priorText2, cex = cexLegend, pos = 4, offse
t = 0.3)
text(xx, yCI2 - 0.059, labels = scores, cex = cexLegend, pos = 4, offset =
0.3)
text(xx, yCI2 - 0.074, labels = posteriorText1, cex = cexLegend, pos = 4, o
ffset = 0.3)
text(xx, yCI2 - 0.083, labels = posteriorText2, cex = cexLegend, pos = 4, o
ffset = 0.3)
par(op)

27. Example:
> library(metafor)
Zorunlu paket yükleniyor: Matrix
Loading 'metafor' package (version 1.9-9).
> g <- c(-0.35, -0.67, -0.25, -0.22, -0.22, -0.36, -0.67, -0.25, -0.22, -0.
22, -0.36, -0.22,
+ -0.67, -0.25, -0.22, -0.36, -0.67, -0.25, -0.22, -0.22, -0.36)
>
> gSE <- c(0.469041575982343, 0.469041575982343, 0.458257569495584, 0.45825
7569495584,
+ 0.458257569495584, 0.458257569495584, 0.469041575982343, 0.45825
7569495584, 0.458257569495584,
+ 0.458257569495584, 0.458257569495584, 0.458257569495584, 0.46904
1575982343, 0.458257569495584,
+ 0.458257569495584, 0.458257569495584, 0.469041575982343, 0.45825
7569495584, 0.458257569495584,
+ 0.458257569495584, 0.458257569495584)
>
> cMeanSmile <- c("3.48", "3.75", "3.48", "3.67", "3.60", "3.58", "3.75", "
3.48", "3.67",
+ "3.60", "3.58", "3.67", "3.75", "3.48", "3.60", "3.58", "
3.75", "3.48", "3.67", "3.60",
+ "3.58")
> cMeanPout <- c("3.96", "4.81", "3.81", "3.94", "3.88", "4.03", "4.81", "3
.81", "3.94",
+ "3.88", "4.03", "3.94", "4.81", "3.81", "3.88", "4.03", "4
.81", "3.81", "3.94", "3.88", "4.03")

28. > forest(x = g, sei = gSE, xlab = "Hedges' g", cex.lab = 1.4, ilab = cbind(
cMeanSmile,
+
cMeanPout), ilab.xpos = c(-3.2, -2.5), cex.axis = 1.1, mlab = "Meta-Analyti
c Effect:",
+ lwd = 1.4, rows = 22:2, addfit = FALSE, atransf = FALSE, ylim = c(
-2, 25))
There were 50 or more warnings (use warnings() to see the first 50)
>
> text(-4.05, 24, "Study", cex = 1.3)
> text(-3.2, 24, "Smile", cex = 1.3)
> text(-2.5, 24, "Pout", cex = 1.3)
> text(2.75, 24, "Hedges' g [95% CI]", cex = 1.3)
>
> abline(h = 1, lwd = 1.4)
> addpoly(metaG, atransf = FALSE, row = -1, cex = 1.3, mlab = "Meta-Analyti
c Effect Size:")
Reference: http://shinyapps.org/apps/RGraphCompendium/index.php
Example:
> library(tidyr)
> library(plotly)
> s <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/s
chool_earnings.csv")
> s <- s[order(s$Men), ]
> gather(s, Sex, value, Women, Men) %>%
+ plot_ly(x = value, y = School, mode = "markers",
+ color = Sex, colors = c("pink", "blue")) %>%
+ add_trace(x = value, y = School, mode = "lines",

29. + group = School, showlegend = F, line = list(color = "gray")
) %>%
+ layout(
+ title = "Gender earnings disparity",
+ xaxis = list(title = "Annual Salary (in thousands)"),
+ margin = list(l = 65)
+ )

30. Example:
> library(lattice)
> library(ggplot2)
> quakes$Magnitude <- equal.count(quakes$mag, 4)
> pl <- cloud(depth ~ lat * long | Magnitude, data = quakes,
+ zlim = rev(range(quakes$depth)), screen = list(z = 105,x = -70), panel.as
pect = 0.75, xlab = "Longitude",ylab = "Latitude", zlab = "Depth")
> print(pl)
> pl
Example:
kx <- function(u, v) cos(u) * (r + cos(u/2) * sin(t *v) - sin(u/2) * sin(2 * t * v))
ky <- function(u, v) sin(u) * (r + cos(u/2) * sin(t *v) - sin(u/2) * sin(2 * t * v))
kz <- function(u, v) sin(u/2) * sin(t * v) + cos(u/2) *sin(t * v)
n <- 50
u <- seq(0.3, 1.25, length = n) * 2 * pi
v <- seq(0, 1, length = n) * 2 * pi
um <- matrix(u, length(u), length(u))

31. vm <- matrix(v, length(v), length(v), byrow = TRUE)
r <- 2
t <- 1
pl <- wireframe(kz(um, vm) ~ kx(um, vm) + ky(um, vm),shade = TRUE,screen = list(z = 170, x = -
60), alpha = 0.75,panel.aspect = 0.6, aspect = c(1, 0.4))
print(pl)