Package 'gplots'

Adjust Color in HSV Space

Description

Adjust color hue, saturation, and/or alpha value.

Usage

adjust_hsv(col, h=NULL, s=NULL, v=NULL, alpha=NULL)

Arguments

col	a color or vector of colors.
h	the desired hue.
s	the desired saturation.
v	the desired value.
alpha	the desired transparency.

Details

Colors can be specified as a color name, a hexadecimal string, or an integer.

Hue, saturation, value, and transparency are specified as values from 0 to 1, or NULL to leave unchanged.

Value

Adjusted colors in hexadecimal string format.

Author(s)

Arni Magnusson.

See Also

col2rgb, rgb2hsv, and hsv are the underlying functions used to convert and adjust the colors.

Examples

col <- "#123456"
col2 <- adjust_hsv(col, h=0.1)
col3 <- adjust_hsv(col, s=0.1)
col4 <- adjust_hsv(col, v=0.7)

barplot(rep(1, 4), col=c(col, col2, col3, col4))

---

Add a Axis to a Plot with Rotated Labels

Description

Add a labeled axis to the current plot with rotated text

Usage

angleAxis(side, labels, at = 1:length(labels), srt = 45, adj, xpd = TRUE, ...)

Arguments

side	an integer specifying which side of the plot the axis is to be drawn on. The axis is placed as follows: 1=below, 2=left, 3=above and 4=right.
labels	character or expression vector of labels to be placed at the tickpoints.
at	the points at which tick-marks are to be drawn. Non-finite (infinite, NaN or NA) values are omitted.
srt	The string rotation in degrees. Defaults to 45 degrees (clockwise).
adj	Text justification. A value of 0 produces left-justified text, 0.5 centered text and 1 right-justified text. For side=1 and side=2, the default value is adj=1. For side=3 and side=4 the default value is adj=0.
xpd	A logical value or NA. If FALSE, labels are clipped to the plot region, if TRUE, labels are clipped to the figure region, and if NA, labels are clipped to the device region.
...	optional arguments passed to text. Common examples are col, cex.

Details

This function augments the feature of the axis functon by allowing the axis labels to be rotated.

Author(s)

Gregory R. Warnes [email protected]

See Also

axis

Examples

# create a vector with some values and long labels
values <- sample(1:10)
names(values) <- sapply(letters[1:10], 
                        function(x) paste(rep(x, 10), sep="",collapse="") 
                        ) 

# barplot labels are too long for the available space, hence some are not plotted
barplot(values)

# to add angled labels, tell barplot not to label the x axis, and store the bar location
at <- barplot(values, xaxt="n")
# then use angleAxs
angleAxis(1, at=at, labels = names(values))

# angle counter-clockwise instead
at <- barplot(values, xaxt="n")
angleAxis(1, at=at, labels = names(values), srt=-45, adj=0)

# put labels at the top
oldpar <- par()$mar
par(mar=c(1,4,5,2)+0.1)
at <- barplot(values, xaxt="n")
angleAxis(3, at=at, labels = names(values))
par(oldpar)

# put labels on the left
oldpar <- par()$mar
par(mar=c(5,5,3,2)+0.1)
at <- barplot(values, yaxt="n", horiz=TRUE)
angleAxis(2, at=at, labels = names(values))
par(oldpar)

# put labels on the right
oldpar <- par()$mar
par(mar=c(2,5,3,5)+0.1)
at <- barplot(values, yaxt="n", horiz=TRUE)
angleAxis(4, at=at, labels = names(values))
par(oldpar)

# specify colors for bars and labels
at <- barplot(values, xaxt="n", col=1:10)
angleAxis(1, at=at, labels = names(values), col=1:10)

---

Plot a graphical matrix where each cell contains a dot whose size reflects the relative magnitude of the corresponding component.

Description

Plot a graphical matrix where each cell contains a dot whose size reflects the relative magnitude of the corresponding component.

Usage

balloonplot(x, ...)
## S3 method for class 'table'
balloonplot(x, xlab, ylab, zlab, show.zeros=FALSE,show.margins=TRUE,...)
## Default S3 method:
balloonplot(x,y,z,
                                xlab,
                                ylab,
                                zlab=deparse(substitute(z)),
                                dotsize=2/max(strwidth(19),strheight(19)),
                                dotchar=19,
                                dotcolor="skyblue",
                                text.size=1,
                                text.color=par("fg"),
                                main,
                                label=TRUE,
                                label.digits=2,
                                label.size=1,
                                label.color=par("fg"),
                                scale.method=c("volume","diameter"),
                                scale.range=c("absolute","relative"),
                                colsrt=par("srt"),
                                rowsrt=par("srt"),
                                colmar=1,
                                rowmar=2,
                                show.zeros=FALSE,
                                show.margins=TRUE,
                                cum.margins=TRUE,
                                sorted=TRUE,
                                label.lines=TRUE,
                                fun=function(x)sum(x,na.rm=T),
                                hide.duplicates=TRUE,
                                ... )

Arguments

x	A table object, or either a vector or a list of several categorical vectors containing grouping variables for the first (x) margin of the plotted matrix.
y	Vector or list of vectors for grouping variables for the second (y) dimension of the plotted matrix.
z	Vector of values for the size of the dots in the plotted matrix.
xlab	Text label for the x dimension. This will be displayed on the x axis and in the plot title.
ylab	Text label for the y dimension. This will be displayed on the y axis and in the plot title.
zlab	Text label for the dot size. This will be included in the plot title.
dotsize	Maximum dot size. You may need to adjust this value for different plot devices and layouts.
dotchar	Plotting symbol or character used for dots. See the help page for the points function for symbol codes.
dotcolor	Scalar or vector specifying the color(s) of the dots in the plot.
text.size, text.color	Character size and color for row and column headers
main	Plot title text.
label	Boolean flag indicating whether the actual value of the elements should be shown on the plot.
label.digits	Number of digits used in formatting value labels.
label.size, label.color	Character size and color for value labels.
scale.method	Method of scaling the sizes of the dot, either "volume" or "diameter". See below.
scale.range	Method for scaling original data to compute circle diameter. scale.range="absolute" scales the data relative to 0 (i.e, maps [0,max(z)] –> [0,1]), while scale.range="relative" scales the data relative to min(z) (i.e. maps [min(z), max(z)] –> [0,1]).
rowsrt, colsrt	Angle of rotation for row and column labels.
rowmar, colmar	Space allocated for row and column labels. Each unit is the width/height of one cell in the table.
show.zeros	boolean. If FALSE, entries containing zero will be left blank in the plotted matrix. If TRUE, zeros will be displayed.
show.margins	boolean. If TRUE, row and column sums are printed in the bottom and right margins, respectively.
cum.margins	boolean. If TRUE, marginal fractions are graphically presented in grey behind the row/column label area.
sorted	boolean. If TRUE, the rows will be arranged in sorted order by using the levels of the first y factor, then the second y factor, etc. The same process is used for the columns, based on the x factors
label.lines	boolean. If TRUE, borders will be drawn for row and column level headers.
hide.duplicates	boolean. If TRUE, column and row headers will omit duplicates within row/column to reduce clutter. Defaults to TRUE.
fun	function to be used to combine data elements with the same levels of the grouping variables x and y. Defaults to sum
...	Additional arguments passed to balloonplot.default or plot, as appropriate.

Details

This function plots a visual matrix. In each x,y cell a dot is plotted which reflects the relative size of the corresponding value of z. When scale.method="volume" the volume of the dot is proportional to the relative size of z. When scale.method="diameter", the diameter of the dot is proportional to the the relative size of z. The "volume" method is default because the "diameter" method visually exaggerates differences.

Value

Nothing of interest.

Note

z is expected to be non-negative. The function will still operate correctly if there are negative values of z, but the corresponding dots will have 0 size and a warning will be generated.

Author(s)

Gregory R. Warnes [email protected]

References

Function inspired by question posed on R-help by Ramon Alonso-Allende [email protected].

See Also

plot.table.

bubbleplot provides an alternative interface and visual style based on scatterplots instead of tables.

Examples

# Create an Example Data Frame Containing Car x Color data
carnames <- c("bmw","renault","mercedes","seat")
carcolors <- c("red","white","silver","green")
datavals <- round(rnorm(16, mean=100, sd=60),1)
data <- data.frame(Car=rep(carnames,4),
                   Color=rep(carcolors, c(4,4,4,4) ),
                   Value=datavals )
# show the data
data

# generate balloon plot with default scaling
balloonplot( data$Car, data$Color, data$Value)


# show margin label rotation & space expansion, using some long labels
levels(data$Car) <- c("BMW: High End, German","Renault: Medium End, French",
 "Mercedes: High End, German", "Seat: Imaginary, Unknown Producer")

# generate balloon plot with default scaling
balloonplot( data$Car, data$Color, data$Value, colmar=3, colsrt=90)

# Create an example using table
xnames <- sample( letters[1:3], 50, replace=2)
ynames <- sample( 1:5, 50, replace=2)

tab <- table(xnames, ynames)

balloonplot(tab)

# Example of multiple classification variabls using the Titanic data
library(datasets)
data(Titanic)

dframe <- as.data.frame(Titanic) # convert to 1 entry per row format
attach(dframe)
balloonplot(x=Class, y=list(Survived, Age, Sex), z=Freq, sort=TRUE)

# colorize: surviors lightblue, non-survivors: grey
Colors <- Titanic
Colors[,,,"Yes"] <- "skyblue"
Colors[,,,"No"] <- "grey"
colors <- as.character(as.data.frame(Colors)$Freq)

balloonplot(x=list(Age,Sex),
            y=list(Class=Class,
                   Survived=reorder.factor(Survived,new.order=c(2,1))
                   ),
            z=Freq,
            zlab="Number of Passengers",
            sort=TRUE,
            dotcol = colors,
            show.zeros=TRUE,
            show.margins=TRUE)

---

Plot x-y Points with Locally Smoothed Mean and Standard Deviation

Description

Plot x-y points with curves for locally smoothed mean and standard deviation.

Usage

bandplot(x,...)
## S3 method for class 'formula'
bandplot(x, data, subset, na.action, ...,
           xlab=NULL, ylab=NULL, add = FALSE, sd = c(-2:2),
           sd.col=c("magenta", "blue", "red", "blue", "magenta"),
           sd.lwd=c(2, 2, 3, 2, 2),  sd.lty=c(2, 1, 1, 1, 2),
           method = "frac", width = 1/5, n=50)
## Default S3 method:
bandplot(x, y, ..., add = FALSE, sd = c(-2:2),
           sd.col=c("magenta", "blue", "red", "blue", "magenta"),
           sd.lwd=c(2, 2, 3, 2, 2),  sd.lty=c(2, 1, 1, 1, 2),
           method = "frac", width = 1/5, n=50)

Arguments

x	either formula providing a single dependent variable (y) and an single independent variable (x) to use as coordinates in the scatter plot or a numeric vector of x locations
y	numeric vector of y locations
data	an optional data.frame, list, or environment contianing the variables used in the model (and in subset). If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The factory-fresh default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
...	Additional plotting parameters
xlab, ylab	x and y axis labels
add	Boolean indicating whether the local mean and standard deviation lines should be added to an existing plot. Defaults to FALSE.
sd	Vector of multiples of the standard devation that should be plotted. 0 gives the mean, -1 gives the mean minus one standard deviation, etc. Defaults to -2:2.
sd.col, sd.lwd, sd.lty	Color, line width, and line type of each plotted line.
method, width, n	Parameters controlling the smoothing. See the help page for wapply for details.

Details

bandplot was created to look for changes in the mean or variance of scatter plots, particularly plots of regression residuals.

The local mean and standard deviation are calculated by calling 'wapply'. By default, bandplot asks wapply to smooth using intervals that include the nearest 1/5 of the data. See the documentation of that function for details on the algorithm.

Value

Invisibly returns a list containing the x,y points plotted for each line.

Author(s)

Gregory R. Warnes [email protected]

See Also

wapply, lowess

Examples

# fixed mean, changing variance
x <- 1:1000
y <- rnorm(1000, mean=1, sd=1 + x/1000 )
bandplot(x,y)
bandplot(y~x)

# fixed varance, changing mean
x <- 1:1000
y <- rnorm(1000, mean=x/1000, sd=1)
bandplot(x,y)

#
# changing mean and variance
#
x <- abs(rnorm(500))
y <- rnorm(500, mean=2*x, sd=2+2*x)

# the changing mean and dispersion are hard to see whith the points alone:
plot(x,y )

# regression picks up the mean trend, but not the change in variance
reg <- lm(y~x)
summary(reg)
abline(reg=reg, col="blue", lwd=2)

# using bandplot on the original data helps to show the mean and
# variance trend
bandplot(y ~ x)

# using bandplot on the residuals helps to see that regression removes
# the mean trend but leaves the trend in variability
bandplot(predict(reg),resid(reg))

---

Enhanced Bar Plots

Description

An enhancement of the standard barplot() function. Creates a bar plot with vertical or horizontal bars. Can plot confidence intervals for each bar, a lined grid behind the bars, change plot area color and logarithmic axes may be used.

Usage

## Default S3 method:
barplot2(height, width = 1, space = NULL,
        names.arg = NULL, legend.text = NULL, beside = FALSE,
        horiz = FALSE, density = NULL, angle = 45,
        col = NULL, prcol = NULL, border = par("fg"),
        main = NULL, sub = NULL, xlab = NULL, ylab = NULL,
        xlim = NULL, ylim = NULL, xpd = TRUE, log = "",
        axes = TRUE, axisnames = TRUE,
        cex.axis = par("cex.axis"), cex.names = par("cex.axis"),
        inside = TRUE, plot = TRUE, axis.lty = 0, offset = 0,
        plot.ci = FALSE, ci.l = NULL, ci.u = NULL,
        ci.color = "black", ci.lty = "solid", ci.lwd = 1, ci.width = 0.5,
        plot.grid = FALSE, grid.inc = NULL,
        grid.lty = "dotted", grid.lwd = 1, grid.col = "black",
        add = FALSE, panel.first = NULL, panel.last = NULL, ...)

Arguments

height	either a vector or matrix of values describing the bars which make up the plot. If height is a vector, the plot consists of a sequence of rectangular bars with heights given by the values in the vector. If height is a matrix and beside is FALSE then each bar of the plot corresponds to a column of height, with the values in the column giving the heights of stacked “sub-bars” making up the bar. If height is a matrix and beside is TRUE, then the values in each column are juxtaposed rather than stacked.
width	optional vector of bar widths. Re-cycled to length the number of bars drawn. Specifying a single value will no visible effect unless xlim is specified.
space	the amount of space (as a fraction of the average bar width) left before each bar. May be given as a single number or one number per bar. If height is a matrix and beside is TRUE, space may be specified by two numbers, where the first is the space between bars in the same group, and the second the space between the groups. If not given explicitly, it defaults to c(0,1) if height is a matrix and beside is TRUE, and to 0.2 otherwise.
names.arg	a vector of names to be plotted below each bar or group of bars. If this argument is omitted, then the names are taken from the names attribute of height if this is a vector, or the column names if it is a matrix.
legend.text	a vector of text used to construct a legend for the plot, or a logical indicating whether a legend should be included. This is only useful when height is a matrix. In that case given legend labels should correspond to the rows of height; if legend.text is true, the row names of height will be used as labels if they are non-null.
beside	a logical value. If FALSE, the columns of height are portrayed as stacked bars, and if TRUE the columns are portrayed as juxtaposed bars.
horiz	a logical value. If FALSE, the bars are drawn vertically with the first bar to the left. If TRUE, the bars are drawn horizontally with the first at the bottom.
density	a vector giving the the density of shading lines, in lines per inch, for the bars or bar components. The default value of NULL means that no shading lines are drawn. Non-positive values of density also inhibit the drawing of shading lines.
angle	the slope of shading lines, given as an angle in degrees (counter-clockwise), for the bars or bar components.
col	a vector of colors for the bars or bar components. By default, grey is used if height is a vector, and heat.colors(nrow(height)) if height is a matrix.
prcol	the color to be used for the plot region.
border	the color to be used for the border of the bars.
main, sub	overall and sub titles for the plot.
xlab	a label for the x axis.
ylab	a label for the y axis.
xlim	limits for the x axis.
ylim	limits for the y axis.
xpd	logical. Should bars be allowed to go outside region?
log	a character string which contains ‘"x"’ if the x axis is to be logarithmic, ‘"y"’ if the y axis is to be logarithmic and ‘"xy"’ or ‘"yx"’ if both axes are to be logarithmic.
axes	logical. If TRUE, a vertical (or horizontal, if horiz is true) axis is drawn.
axisnames	logical. If TRUE, and if there are names.arg (see above), the other axis is drawn (with lty = 0) and labeled.
cex.axis	expansion factor for numeric axis labels.
cex.names	expansion factor for names.
inside	logical. If TRUE, the lines which divide adjacent (non-stacked!) bars will be drawn. Only applies when space = 0 (which it partly is when beside = TRUE).
plot	logical. If FALSE, nothing is plotted.
axis.lty	the graphics parameter lty applied to the axis and tick marks of the categorical (default horzontal) axis. Note that by default the axis is suppressed.
offset	a vector indicating how much the bars should be shifted relative to the x axis.
plot.ci	logical. If TRUE, confidence intervals are plotted over the bars. Note that if a stacked bar plot is generated, confidence intervals will not be plotted even if plot.ci = TRUE
ci.l, ci.u	The confidence intervals (ci.l = lower bound, ci.u = upper bound) to be plotted if plot.ci = TRUE. Values must have the same dim structure as height.
ci.color	the color for the confidence interval line segments
ci.lty	the line type for the confidence interval line segments
ci.lwd	the line width for the confidence interval line segments
ci.width	length of lines used for the "t" at the end of confidence interval line segments, as a multple of width. Defaults to 0.5.
plot.grid	if TRUE a lined grid will be plotted behind the bars
grid.inc	the number of grid increments to be plotted
grid.lty	the line type for the grid
grid.lwd	the line width for the grid
grid.col	the line color for the grid
add	logical, if TRUE add barplot to current plot.
panel.first	An expression to be evaluated after the plot region coordinates have been set up, but prior to the drawing of the bars and other plot region contents. This can be useful to add additional plot region content behind the bars. This will also work if add = TRUE
panel.last	An expression to be evaluated after the bars have been drawn, but prior to the addition of confidence intervals, a legend and the axis annotation
...	further graphical parameters (par) are passed to plot.window(), title() and axis.

Details

This is a generic function, it currently only has a default method. A formula interface may be added eventually.

Value

A numeric vector (or matrix, when beside = TRUE), say mp, giving the coordinates of all the bar midpoints drawn, useful for adding to the graph.

If beside is true, use colMeans(mp) for the midpoints of each group of bars, see example.

Note

Prior to R 1.6.0, barplot behaved as if axis.lty = 1, unintentionally. 0 (zero) and NA values in height will not be plotted if using logarithmic scales. If there are NA values in height and beside = FALSE, values after the NA will not be plotted in stacked bars.

Author(s)

Original barplot() by R-Core. Enhancements by Marc Schwartz.

See Also

plot(..., type = "h"), dotchart, hist.

Examples

tN <- table(Ni <- rpois(100, lambda = 5))
r <- barplot2(tN, col = 'gray')

#- type = "h" plotting *is* `bar'plot
lines(r, tN, type = 'h', col = 'red', lwd = 2)

barplot2(tN, space = 1.5, axisnames = FALSE,
        sub = "barplot2(..., space = 1.5, axisnames = FALSE)")

data(VADeaths, package = "datasets")
barplot2(VADeaths, plot = FALSE)
barplot2(VADeaths, plot = FALSE, beside = TRUE)

mp <- barplot2(VADeaths) # default
tot <- colMeans(VADeaths)
text(mp, tot + 3, format(tot), xpd = TRUE, col = "blue")
barplot2(VADeaths, beside = TRUE,
        col = c("lightblue", "mistyrose", "lightcyan",
                "lavender", "cornsilk"),
        legend = rownames(VADeaths), ylim = c(0, 100))
title(main = "Death Rates in Virginia", font.main = 4)

# Example with confidence intervals and grid
hh <- t(VADeaths)[, 5:1]
mybarcol <- "gray20"
ci.l <- hh * 0.85
ci.u <- hh * 1.15
mp <- barplot2(hh, beside = TRUE,
        col = c("lightblue", "mistyrose",
                "lightcyan", "lavender"),
        legend = colnames(VADeaths), ylim = c(0, 100),
        main = "Death Rates in Virginia", font.main = 4,
        sub = "Faked 95 percent error bars", col.sub = mybarcol,
        cex.names = 1.5, plot.ci = TRUE, ci.l = ci.l, ci.u = ci.u,
        plot.grid = TRUE)
mtext(side = 1, at = colMeans(mp), line = -2,
      text = paste("Mean", formatC(colMeans(hh))), col = "red")
box()

# Example with horizontal bars, grid and logarithmic x axis
barplot2(1:10 , log = "x", plot.grid = TRUE, grid.inc = 10,
        xlim = c(0.5, 20), horiz = TRUE, cex.axis = 0.9,
        prcol = "gray95")
box()

# Bar shading example
barplot2(VADeaths, angle = 15 + 10 * 1:5, density = 20, col = "black",
        legend = rownames(VADeaths))
title(main = list("Death Rates in Virginia", font = 4))

# border :
barplot2(VADeaths, border = "dark blue")

---

Produce a Boxplot Annotated with the Number of Observations

Description

This funcntion uses boxplot to produce a boxplot which is then annotated with the number of observations in each group.

Usage

boxplot2(..., top=FALSE, shrink=1, textcolor=NULL)

Arguments

...	parameters passed to boxplot.
top	logical indicating whether the number of observations should be added to the top or the bottom of the plotting region. Defaults to FALSE.
shrink	value to shrink character size (cex) when annotating.
textcolor	text color.

Note

This function replaces boxplot.n, which has been deprecated avoid potential problems with S3 method dispatching.

Author(s)

Gregory R. Warnes [email protected]

See Also

boxplot, text

Examples

data(state)

# n's at bottom
boxplot2( state.area ~ state.region)

# n's at top
boxplot2( state.area ~ state.region, top=TRUE)

# small red text
boxplot2( state.area ~ state.region, shrink=0.8, textcolor="red")

---

Bubble Plot

Description

Draw a bubble plot, a scatterplot with varying symbol sizes and colors, or add points to existing plots. A variety of input formats are supported, including vectors, matrices, data frames, formulas, etc.

Usage

bubbleplot(x, ...)

## Default S3 method:
bubbleplot(x, y, z, std=TRUE, pow=0.5, add=FALSE,
           rev=FALSE, type="p", ylim=NULL, xlab=NULL, ylab=NULL,
           pch=c(16,1), cex.points=1, col="black", bg=par("bg"), ...)

## S3 method for class 'formula'
bubbleplot(formula, data, subset, na.action=NULL, ...)

Arguments

x	a vector of values for the horizontal axis. Can also be a 2-dimensional matrix or table (x values in column names and y values in row names), or a data frame containing x, y, and z in that order. If the data frame contains column names x, y, and z then they will be used for plotting.
...	passed to plot and points.
y	a vector of values for the vertical axis.
z	a vector of values determining the bubble sizes.
std	whether to standardize the z values.
pow	a power coefficient for the bubble sizes.
add	whether to add bubbles to an existing plot.
rev	whether to reverse the y axis.
type	passed to points.
ylim	passed to plot.
xlab, ylab	passed to plot.
pch	passed to points.
cex.points	scales all bubble sizes.
col, bg	passed to points.
formula	has the form z ~ x + y, where z determines the bubble sizes and x and y determine bubble locations.
data	where formula terms are stored, e.g. data frame or list.
subset	a logical vector specifying which data to plot.
na.action	how NA values are handled.

Details

The std standardization sets z = abs(z) / mean(abs(z)).

The pow = 0.5 (square root) is a good default, where a z value of 2 has twice the area of 1. See example #2 below for an exception, where the z value is tree circumference and therefore proportional to the tree diameter.

The pch, col, and bg arguments can be be vectors of length 2, where positive z values are drawn with pch[1], col[1], bg[1] and negative z values are drawn with pch[2], col[2], and bg[2].

Author(s)

Arni Magnusson.

See Also

points is the underlying function used to draw the bubbles.

symbols can also draw bubbles, but does not handle negative z values or have convenience features such as pow and rev.

balloonplot provides an alternative interface and visual style based on tables instead of scatterplots.

Examples

catch.t <- xtabs(Catch~Year+Age, catch.d)              # example table
catch.m <- as.matrix(as.data.frame(unclass(catch.t)))  # example matrix

# 1  Formula
bubbleplot(Catch~Age+Year, data=catch.d)
# Use rev=TRUE to get same layout as crosstab matrix:
print(catch.m)
bubbleplot(Catch~Age+Year, data=catch.d, rev=TRUE, las=1)

# 2  Data frame
bubbleplot(catch.d)
bubbleplot(Orange)
# Visualize tree transverse section at breast height
bubbleplot(Orange, pow=1, cex=2, pch=21,
           col="darkred", bg="peru", lwd=1.5)

# 3  Matrix or table
bubbleplot(catch.m)
bubbleplot(catch.t)

# 4  Positive and negative values
bubbleplot(catch.r)
bubbleplot(Resid~Age+Year, catch.r, subset=Age %in% 4:9,
           rev=TRUE, xlim=c(3.5,9.5), cex=1.3)
# Residuals from orange tree model
library(nlme)
fm <- nlme(circumference~phi1/(1+exp(-(age-phi2)/phi3)),
           fixed=phi1+phi2+phi3~1, random=phi1~1|Tree,
           data=Orange, start=c(phi1=200,phi2=800,phi3=400))
bubbleplot(residuals(fm)~Tree+age, Orange)
bubbleplot(residuals(fm)~Tree+age, Orange, cex=2.5, pch=16,
           col=c("dodgerblue","orange"))

# 5  Richter magnitude, amplitude, and energy release
bubbleplot(mag~long+lat, quakes, pch=1)
bubbleplot(10^mag~long+lat, quakes, cex=1.2, col=gray(0, 0.3))
bubbleplot(sqrt(1000)^mag~long+lat, quakes, cex=1.2, col=gray(0, 0.3))
bubbleplot(sqrt(1000)^mag~long+lat, quakes, cex=1.2, col="#FF00004D")

---

Catch at Age and Residuals

Description

Catch-at-age observed data and model residuals from Icelandic saithe assessment.

Usage

catch.d
catch.r

Format

Data frame containing three columns:

Year	year
Age	age
and
Catch	catch (thousands of individuals)
or
Resid	standardized residual

Details

The data are from Tables 8.2 and 8.6 in the ICES (2015) fish stock assessment of Icelandic saithe.

Source

ICES (2015) Report of the North-Western Working Group (NWWG). ICES CM 2015/ACOM:07, pp. 240–246.

See Also

bubbleplot is an effective way to visualize these data.

Examples

catch.t <- xtabs(Catch~Year+Age, catch.d)
catch.m <- as.matrix(as.data.frame(unclass(catch.t)))

# 1  Formula
bubbleplot(Catch~Age+Year, data=catch.d)
# Use rev=TRUE to get same layout as crosstab matrix:
print(catch.m)
bubbleplot(Catch~Age+Year, data=catch.d, rev=TRUE, las=1)

# 2  Data frame
bubbleplot(catch.d)

# 3  Matrix or table
bubbleplot(catch.m)
bubbleplot(catch.t)

# 4  Positive and negative values
bubbleplot(catch.r)
bubbleplot(Resid~Age+Year, catch.r, subset=Age %in% 4:9,
           rev=TRUE, xlim=c(3.5,9.5), cex=1.3)

---

Create 2-dimensional empirical confidence regions

Description

Create 2-dimensional empirical confidence regions from provided data.

Usage

ci2d(x, y = NULL,
     nbins=51, method=c("bkde2D","hist2d"),
     bandwidth, factor=1.0,
     ci.levels=c(0.50,0.75,0.90,0.95,0.975),
     show=c("filled.contour","contour","image","none"),
     col=topo.colors(length(breaks)-1),
     show.points=FALSE,
     pch=par("pch"),
     points.col="red",
     xlab, ylab, 
     ...)
## S3 method for class 'ci2d'
print(x, ...)

Arguments

x	either a vector containing the x coordinates or a matrix with 2 columns.
y	a vector contianing the y coordinates, not required if ‘x’ is matrix
nbins	number of bins in each dimension. May be a scalar or a 2 element vector. Defaults to 51.
method	One of "bkde2D" (for KernSmooth::bdke2d) or "hist2d" (for gplots::hist2d) specifyting the name of the method to create the 2-d density summarizing the data. Defaults to "bkde2D".
bandwidth	Bandwidth to use for KernSmooth::bkde2D. See below for default value.
factor	Numeric scaling factor for bandwidth. Useful for exploring effect of changing the bandwidth. Defaults to 1.0.
ci.levels	Confidence level(s) to use for plotting data. Defaults to c(0.5, 0.75, 0.9, 0.95, 0.975)
show	Plot type to be displaed. One of "filled.contour", "contour", "image", or "none". Defaults to "filled.contour".
show.points	Boolean indicating whether original data values should be plotted. Defaults to TRUE.
pch	Point type for plots. See points for details.
points.col	Point color for plotting original data. Defaiults to "red".
col	Colors to use for plots.
xlab, ylab	Axis labels
...	Additional arguments passed to KernSmooth::bkde2D or gplots::hist2d.

Details

This function utilizes either KernSmooth::bkde2D or gplots::hist2d to estmate a 2-dimensional density of the data passed as an argument. This density is then used to create and (optionally) display confidence regions.

When bandwidth is ommited and method="bkde2d", KernSmooth::dpik is appled in x and y dimensions to select the bandwidth.

Value

A ci2d object consisting of a list containing (at least) the following elements:

nobs	number of original data points
x	x position of each density estimate bin
y	y position of each density estimate bin
density	Matrix containing the probability density of each bin (count in bin/total count)
cumDensity	Matrix where each element contains the cumulative probability density of all elements with the same density (used to create the confidence region plots)
contours	List of contours of each confidence region.
call	Call used to create this object

Note

Confidence intervals generated by ci2d are approximate, and are subject to biases and/or artifacts induced by the binning or kernel smoothing method, bin locations, bin sizes, and kernel bandwidth.

The conf2d function in the r2d2 package may create a more accurate confidence region, and reports the actual proportion of points inside the region.

Author(s)

Gregory R. Warnes [email protected]

See Also

bkde2D, conf2d, dpik, hist2d

Examples

####
   ## Basic usage 
   ####
   data(geyser, package="MASS")

   x <- geyser$duration
   y <- geyser$waiting

   # 2-d confidence intervals based on binned kernel density estimate
   ci2d(x,y)                   # filled contour plot
   ci2d(x,y, show.points=TRUE) # show original data


   # image plot
   ci2d(x,y, show="image")
   ci2d(x,y, show="image", show.points=TRUE)

   # contour plot
   ci2d(x,y, show="contour", col="black")
   ci2d(x,y, show="contour", col="black", show.points=TRUE)

   ####
   ## Control Axis scales
   ####
   x <- rnorm(2000, sd=4)
   y <- rnorm(2000, sd=1)

   # 2-d confidence intervals based on binned kernel density estimate
   ci2d(x,y)

   # 2-d confidence intervals based on 2d histogram
   ci2d(x,y, method="hist2d", nbins=25)
 
   # Require same scale for each axis, this looks oval
   ci2d(x,y, range.x=list(c(-20,20), c(-20,20)))
   ci2d(x,y, method="hist2d", same.scale=TRUE, nbins=25) # hist2d 

   ####
   ## Control smoothing and binning 
   ####
   x <- rnorm(2000, sd=4)
   y <- rnorm(2000, mean=x, sd=2)

   # Default 2-d confidence intervals based on binned kernel density estimate
   ci2d(x,y)

   # change the smoother bandwidth
   ci2d(x,y,
        bandwidth=c(sd(x)/8, sd(y)/8)
       )

   # change the smoother number of bins
   ci2d(x,y, nbins=10)
   ci2d(x,y)
   ci2d(x,y, nbins=100)

   # Default 2-d confidence intervals based on 2d histogram
   ci2d(x,y, method="hist2d", show.points=TRUE)

   # change the number of histogram bins
   ci2d(x,y, nbin=10, method="hist2d", show.points=TRUE )
   ci2d(x,y, nbin=25, method="hist2d", show.points=TRUE )

   ####
   ## Perform plotting manually
   ####
   data(geyser, package="MASS")

   # let ci2d handle plotting contours...
   ci2d(geyser$duration, geyser$waiting, show="contour", col="black")

   # call contour() directly, show the 90 percent CI, and the mean point 
   est <- ci2d(geyser$duration, geyser$waiting, show="none")
   contour(est$x, est$y, est$cumDensity,
           xlab="duration", ylab="waiting",
           levels=0.90, lwd=4, lty=2)
   points(mean(geyser$duration), mean(geyser$waiting),
         col="red", pch="X")


   ####
   ## Extract confidence region values
   ###
   data(geyser, package="MASS")

   ## Empirical 90 percent confidence limits
   quantile( geyser$duration, c(0.05, 0.95) )
   quantile( geyser$waiting, c(0.05, 0.95) )

   ## Bivariate 90 percent confidence region
   est <- ci2d(geyser$duration, geyser$waiting, show="none")
   names(est$contours) ## show available contours

   ci.90 <- est$contours[names(est$contours)=="0.9"]  # get region(s)
   ci.90 <- rbind(ci.90[[1]],NA, ci.90[[2]], NA, ci.90[[3]]) # join them

   print(ci.90)                  # show full contour
   range(ci.90$x, na.rm=TRUE)    # range for duration
   range(ci.90$y, na.rm=TRUE)    # range for waiting

   ####
   ## Visually compare confidence regions 
   ####
   data(geyser, package="MASS")

   ## Bivariate smoothed 90 percent confidence region
   est <- ci2d(geyser$duration, geyser$waiting, show="none")
   names(est$contours) ## show available contours

   ci.90 <- est$contours[names(est$contours)=="0.9"]  # get region(s)
   ci.90 <- rbind(ci.90[[1]],NA, ci.90[[2]], NA, ci.90[[3]]) # join them

   plot( waiting ~ duration, data=geyser,
         main="Comparison of 90 percent confidence regions" )
   polygon( ci.90, col="green", border="green", density=10)

   ## Univariate Normal-Theory 90 percent confidence region
   mean.x <- mean(geyser$duration)
   mean.y <- mean(geyser$waiting)
   sd.x <- sd(geyser$duration)
   sd.y <- sd(geyser$waiting)

   t.value <- qt(c(0.05,0.95), df=length(geyser$duration), lower=TRUE)
   ci.x <- mean.x +  t.value* sd.x
   ci.y <- mean.y +  t.value* sd.y

   plotCI(mean.x, mean.y,
          li=ci.x[1],
          ui=ci.x[2],
          barcol="blue", col="blue",
          err="x",
          pch="X",
          add=TRUE )

   plotCI(mean.x, mean.y,
          li=ci.y[1],
          ui=ci.y[2],
          barcol="blue", col="blue",
          err="y",
          pch=NA,
          add=TRUE )

#   rect(ci.x[1], ci.y[1], ci.x[2], ci.y[2], border="blue",
#        density=5,
#        angle=45,
#        col="blue" )


   ## Empirical univariate 90 percent confidence region
   box <- cbind( x=quantile( geyser$duration, c(0.05, 0.95 )), 
                 y=quantile( geyser$waiting, c(0.05, 0.95 )) )

   rect(box[1,1], box[1,2], box[2,1], box[2,2], border="red",
        density=5,
        angle=-45,
        col="red" )

   ## now a nice legend
   legend( "topright", legend=c("       Region type",
                                "Univariate Normal Theory",
                                "Univarite Empirical",
                                "Smoothed Bivariate"),
           lwd=c(NA,1,1,1),
           col=c("black","blue","red","green"),
           lty=c(NA,1,1,1)
         )

   ####
   ## Test with a large number of points
   ####
   ## Not run: 
   x <- rnorm(60000, sd=1)
   y <- c( rnorm(40000, mean=x, sd=1),
           rnorm(20000, mean=x+4, sd=1) )

   hist2d(x,y)
   ci <- ci2d(x,y)
   ci
   
## End(Not run)

---

Convert color names to hex RGB strings

Description

Convert color names to hex RGB strings

Usage

col2hex(cname)

Arguments

cname

Color name(s)

Value

Character vector giving the hex color code translation of the provided color names.

Author(s)

Gregory R. Warnes

See Also

col2rgb, colors, rgb

Examples

col2hex(c("red","yellow","lightgrey"))

---

Generate a smoothly varying set of colors

Description

colorpanel generate a set of colors that varies smoothly. redgreen, greenred, bluered, and redblue generate red-black-green, green-black-red, red-white-blue, and blue-white-red colorbars, respectively. colors

Usage

colorpanel(n, low, mid, high)
redgreen(n)
greenred(n)
bluered(n)
redblue(n)

Arguments

n	Desired number of color elements in the panel.
low, mid, high	Colors to use for the Lowest, middle, and highest values. mid may be ommited.

Details

The values for low, mid, high can be given as color names ("red"), plot color index (2), and HTML-style RGB, ("#FF0000").

If mid is supplied, then the returned color panel will consist of n - floor(n/2) HTML-style RGB elements which vary smoothly between low and mid, then between mid and high. Note that if n is even, the color mid will occur twice at the center of the sequence.

If mid is omitted, the color panel will vary smoothly beween low and high.

Value

Vector of HTML-style RGB colors.

Author(s)

Gregory R. Warnes [email protected]

See Also

colors

Examples

showpanel <- function(col)
{
  image(z=matrix(1:100, ncol=1), col=col, xaxt="n", yaxt="n" )
}

par(mfrow=c(3,3))

# two colors only:
showpanel(colorpanel(8,low="red",high="green"))

# three colors
showpanel(colorpanel(8,"red","black","green"))
# note the duplicatation of black at the center, using an odd
# number of elements resolves this:
showpanel(colorpanel(9,"red","black","green"))

showpanel(greenred(64))
showpanel(redgreen(64))
showpanel(bluered(64))
showpanel(redblue(64))

---

Defunct functions

Description

These functions are defunct and have been removed from the gplots package.

Usage

boxplot.n(..., top=FALSE, shrink=1, textcolor=NULL)
   plot.lm2(
            x,
            which = 1:5,
            caption = c("Residuals vs Fitted", "Normal Q-Q plot",
              "Scale-Location plot", "Cook's distance plot"),
            panel = panel.smooth,
            sub.caption = deparse(x$call),
            main = "",
            ask,
            ...,
            id.n = 3,
            labels.id = names(residuals(x)),
            cex.id = 0.75,
            band=TRUE,
            rug=TRUE,
            width=1/10,
            max.n=5000
            )
  smartlegend(x = c("left", "center", "right"),
             y = c("top", "center", "bottom"),
             ...,
             inset = 0.05)

Arguments

ask, band, caption, cex.id, id.n, inset, labels.id, main, max.n, panel, rug, shrink, sub.caption, textcolor, top, which, width, x, y, ...

see man page for the corresponding replacement function

Details

These functions are no longer available. Please refer to the manual page for the replacement function:

• boxplot.n has been replaced by boxplot2

• plot.lm2 has been replaced by lmplot2

• smartlegend is no longer needed because relative positioning has been implemented in legend.

Author(s)

Gregory R. Warnes [email protected]

See Also

boxplot2, lmplot2, legend, Defunct

---

Deprecated functions

Description

These functions have been deprecated and will be removed in future releases of gplots.

Usage

## No deprecated functions at this time ##

Details

These functions have been deprecated. Please refer to the manual page for the replacement function:

• (No deprecated functions at this time)

Author(s)

Gregory R. Warnes [email protected]

See Also

Deprecated

---

Enhanced Heat Map

Description

A heat map is a false color image (basically image(t(x))) with a dendrogram added to the left side and/or to the top. Typically, reordering of the rows and columns according to some set of values (row or column means) within the restrictions imposed by the dendrogram is carried out.

This heatmap provides a number of extensions to the standard R heatmap function.

Usage

heatmap.2 (x,

           # dendrogram control
           Rowv = TRUE,
           Colv=if(symm)"Rowv" else TRUE,
           distfun = dist,
           hclustfun = hclust,
           dendrogram = c("both","row","column","none"),
           reorderfun = function(d, w) reorder(d, w),
           symm = FALSE,

           # data scaling
           scale = c("none","row", "column"),
           na.rm=TRUE,

           # image plot
           revC = identical(Colv, "Rowv"),
           add.expr,

           # mapping data to colors
           breaks,
           symbreaks=any(x < 0, na.rm=TRUE) || scale!="none",

           # colors
           col="heat.colors",

           # block sepration
           colsep,
           rowsep,
           sepcolor="white",
           sepwidth=c(0.05,0.05),

           # cell labeling
           cellnote,
           notecex=1.0,
           notecol="cyan",
           na.color=par("bg"),

           # level trace
           trace=c("column","row","both","none"),
           tracecol="cyan",
           hline=median(breaks),
           vline=median(breaks),
           linecol=tracecol,

           # Row/Column Labeling
           margins = c(5, 5),
           ColSideColors,
           RowSideColors,
           cexRow = 0.2 + 1/log10(nr),
           cexCol = 0.2 + 1/log10(nc),
           labRow = NULL,
           labCol = NULL,
           srtRow = NULL,
           srtCol = NULL,
           adjRow = c(0,NA),
           adjCol = c(NA,0),
           offsetRow = 0.5,
           offsetCol = 0.5,
           colRow = NULL,
           colCol = NULL,

           # color key + density info
           key = TRUE,
           keysize = 1.5,
           density.info=c("histogram","density","none"),
           denscol=tracecol,
           symkey = any(x < 0, na.rm=TRUE) || symbreaks,
           densadj = 0.25,
           key.title = NULL,
           key.xlab = NULL,
           key.ylab = NULL,
           key.xtickfun = NULL,
           key.ytickfun = NULL,
           key.par=list(),

           # plot labels
           main = NULL,
           xlab = NULL,
           ylab = NULL,

           # plot layout
           lmat = NULL,
           lhei = NULL,
           lwid = NULL,

           # extras
           extrafun=NULL,
           ...
           )

Arguments

x	numeric matrix of the values to be plotted.
Rowv	determines if and how the row dendrogram should be reordered. By default, it is TRUE, which implies dendrogram is computed and reordered based on row means. If NULL or FALSE, then no dendrogram is computed and no reordering is done. If a dendrogram, then it is used "as-is", ie without any reordering. If a vector of integers, then dendrogram is computed and reordered based on the order of the vector.
Colv	determines if and how the column dendrogram should be reordered. Has the options as the Rowv argument above and additionally when x is a square matrix, Colv="Rowv" means that columns should be treated identically to the rows.
distfun	function used to compute the distance (dissimilarity) between both rows and columns. Defaults to dist.
hclustfun	function used to compute the hierarchical clustering when Rowv or Colv are not dendrograms. Defaults to hclust.
dendrogram	character string indicating whether to draw 'none', 'row', 'column' or 'both' dendrograms. Defaults to 'both'. However, if Rowv (or Colv) is FALSE or NULL and dendrogram is 'both', then a warning is issued and Rowv (or Colv) arguments are honoured.
reorderfun	function(d, w) of dendrogram and weights for reordering the row and column dendrograms. The default uses stats{reorder.dendrogram}

.

symm	logical indicating if x should be treated symmetrically; can only be true when x is a square matrix.
scale	character indicating if the values should be centered and scaled in either the row direction or the column direction, or none. The default is "none".
na.rm	logical indicating whether NA's should be removed.
revC	logical indicating if the column order should be reversed for plotting, such that e.g., for the symmetric case, the symmetry axis is as usual.
add.expr	expression that will be evaluated after the call to image. Can be used to add components to the plot.
breaks	(optional) Either a numeric vector indicating the splitting points for binning x into colors, or a integer number of break points to be used, in which case the break points will be spaced equally between min(x) and max(x).
symbreaks	Boolean indicating whether breaks should be made symmetric about 0. Defaults to TRUE if the data includes negative values, and to FALSE otherwise.
col	colors used for the image. Defaults to heat colors (heat.colors).
colsep, rowsep, sepcolor	(optional) vector of integers indicating which columns or rows should be separated from the preceding columns or rows by a narrow space of color sepcolor.
sepwidth	(optional) Vector of length 2 giving the width (colsep) or height (rowsep) the separator box drawn by colsep and rowsep as a function of the width (colsep) or height (rowsep) of a cell. Defaults to c(0.05, 0.05)
cellnote	(optional) matrix of character strings which will be placed within each color cell, e.g. p-value symbols.
notecex	(optional) numeric scaling factor for cellnote items.
notecol	(optional) character string specifying the color for cellnote text. Defaults to "cyan".
na.color	Color to use for missing value (NA). Defaults to the plot background color.
trace	character string indicating whether a solid "trace" line should be drawn across 'row's or down 'column's, 'both' or 'none'. The distance of the line from the center of each color-cell is proportional to the size of the measurement. Defaults to 'column'.
tracecol	character string giving the color for "trace" line. Defaults to "cyan".
hline, vline, linecol	Vector of values within cells where a horizontal or vertical dotted line should be drawn. The color of the line is controlled by linecol. Horizontal lines are only plotted if trace is 'row' or 'both'. Vertical lines are only drawn if trace 'column' or 'both'. hline and vline default to the median of the breaks, linecol defaults to the value of tracecol.
margins	numeric vector of length 2 containing the margins (see par(mar= *)) for column and row names, respectively.
ColSideColors	(optional) character vector of length ncol(x) containing the color names for a horizontal side bar that may be used to annotate the columns of x.
RowSideColors	(optional) character vector of length nrow(x) containing the color names for a vertical side bar that may be used to annotate the rows of x.
cexRow, cexCol	positive numbers, used as cex.axis in for the row or column axis labeling. The defaults currently only use number of rows or columns, respectively.
labRow, labCol	character vectors with row and column labels to use; these default to rownames(x) or colnames(x), respectively.
srtRow, srtCol	angle of row/column labels, in degrees from horizontal
adjRow, adjCol	2-element vector giving the (left-right, top-bottom) justification of row/column labels (relative to the text orientation).
offsetRow, offsetCol	Number of character-width spaces to place between row/column labels and the edge of the plotting region.
colRow, colCol	color of row/column labels, either a scalar to set the color of all labels the same, or a vector providing the colors of each label item
key	logical indicating whether a color-key should be shown.
keysize	numeric value indicating the size of the key
density.info	character string indicating whether to superimpose a 'histogram', a 'density' plot, or no plot ('none') on the color-key.
denscol	character string giving the color for the density display specified by density.info, defaults to the same value as tracecol.
symkey	Boolean indicating whether the color key should be made symmetric about 0. Defaults to TRUE if the data includes negative values, and to FALSE otherwise.
densadj	Numeric scaling value for tuning the kernel width when a density plot is drawn on the color key. (See the adjust parameter for the density function for details.) Defaults to 0.25.
key.title	main title of the color key. If set to NA no title will be plotted.
key.xlab	x axis label of the color key. If set to NA no label will be plotted.
key.ylab	y axis label of the color key. If set to NA no label will be plotted.
key.xtickfun	function computing tick location and labels for the xaxis of the color key. Returns a named list containing parameters that can be passed to axis. See examples.
key.ytickfun	function computing tick location and labels for the y axis of the color key. Returns a named list containing parameters that can be passed to axis. See examples.
key.par	graphical parameters for the color key. Named list that can be passed to par.
main, xlab, ylab	main, x- and y-axis titles; defaults to none.
lmat, lhei, lwid	visual layout: position matrix, column height, column width. See below for details
extrafun	A function to be called after all other work. See examples.
...	additional arguments passed on to image

Details

If either Rowv or Colv are dendrograms they are honored (and not reordered). Otherwise, dendrograms are computed as dd <- as.dendrogram(hclustfun(distfun(X))) where X is either x or t(x).

If either is a vector (of “weights”) then the appropriate dendrogram is reordered according to the supplied values subject to the constraints imposed by the dendrogram, by reorder(dd, Rowv), in the row case. If either is missing, as by default, then the ordering of the corresponding dendrogram is by the mean value of the rows/columns, i.e., in the case of rows, Rowv <- rowMeans(x, na.rm=na.rm). If either is NULL, no reordering will be done for the corresponding side.

If scale="row" (or scale="col") the rows (columns) are scaled to have mean zero and standard deviation one. There is some empirical evidence from genomic plotting that this is useful.

The default colors range from red to white (heat.colors) and are not pretty. Consider using enhancements such as the RColorBrewer package, https://cran.r-project.org/package=RColorBrewer to select better colors.

By default four components will be displayed in the plot. At the top left is the color key, top right is the column dendrogram, bottom left is the row dendrogram, bottom right is the image plot. When RowSideColor or ColSideColor are provided, an additional row or column is inserted in the appropriate location. This layout can be overriden by specifiying appropriate values for lmat, lwid, and lhei. lmat controls the relative postition of each element, while lwid controls the column width, and lhei controls the row height. See the help page for layout for details on how to use these arguments.

Value

Invisibly, a list with components

rowInd	row index permutation vector as returned by order.dendrogram.
colInd	column index permutation vector.
call	the matched call
rowMeans, rowSDs	mean and standard deviation of each row: only present if scale="row"
colMeans, colSDs	mean and standard deviation of each column: only present if scale="column"
carpet	reordered and scaled 'x' values used generate the main 'carpet'
rowDendrogram	row dendrogram, if present
colDendrogram	column dendrogram, if present
breaks	values used for color break points
col	colors used
vline	center-line value used for column trace, present only if trace="both" or trace="column"
hline	center-line value used for row trace, present only if trace="both" or trace="row"
colorTable	A three-column data frame providing the lower and upper bound and color for each bin
layout	A named list containing the values used for lmat, lhei, and lwid.

Note

The original rows and columns are reordered to match the dendrograms Rowv and Colv (if present).

heatmap.2() uses layout to arragent the plot elements. Consequentially, it can not be used in a multi column/row layout using layout(...), par(mfrow=...) or (mfcol=...).

Author(s)

Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber, G. Warnes, revisions.

See Also

image, hclust

Examples

data(mtcars)
 x  <- as.matrix(mtcars)
 rc <- rainbow(nrow(x), start=0, end=.3)
 cc <- rainbow(ncol(x), start=0, end=.3)

 ##
 ## demonstrate the effect of row and column dendrogram options
 ##
 heatmap.2(x)                    ## default - dendrogram plotted and reordering done.
 heatmap.2(x, dendrogram="none") ##  no dendrogram plotted, but reordering done.
 heatmap.2(x, dendrogram="row")  ## row dendrogram plotted and row reordering done.
 heatmap.2(x, dendrogram="col")  ## col dendrogram plotted and col reordering done.

 heatmap.2(x, keysize=2)         ## default - dendrogram plotted and reordering done.

 heatmap.2(x, Rowv=FALSE, dendrogram="both") ## generates a warning!
 heatmap.2(x, Rowv=NULL, dendrogram="both")  ## generates a warning!
 heatmap.2(x, Colv=FALSE, dendrogram="both") ## generates a warning!

 ## Reorder dendrogram by branch means rather than sums
 heatmap.2(x, reorderfun=function(d, w) reorder(d, w, agglo.FUN = mean) )

 ## plot a sub-cluster using the same color coding as for the full heatmap
 full <- heatmap.2(x)
 heatmap.2(x, Colv=full$colDendrogram[[2]], breaks=full$breaks)  # column subset
 heatmap.2(x, Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # row subset
 heatmap.2(x, Colv=full$colDendrogram[[2]],
              Rowv=full$rowDendrogram[[1]], breaks=full$breaks)  # both

 ## Show effect of row and column label rotation
 heatmap.2(x, srtCol=NULL)
 heatmap.2(x, srtCol=0,   adjCol = c(0.5,1) )
 heatmap.2(x, srtCol=45,  adjCol = c(1,1)   )
 heatmap.2(x, srtCol=135, adjCol = c(1,0)   )
 heatmap.2(x, srtCol=180, adjCol = c(0.5,0) )
 heatmap.2(x, srtCol=225, adjCol = c(0,0)   ) ## not very useful
 heatmap.2(x, srtCol=270, adjCol = c(0,0.5) )
 heatmap.2(x, srtCol=315, adjCol = c(0,1)   )
 heatmap.2(x, srtCol=360, adjCol = c(0.5,1) )

 heatmap.2(x, srtRow=45, adjRow=c(0, 1) )
 heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=45, adjCol=c(1,1) )
 heatmap.2(x, srtRow=45, adjRow=c(0, 1), srtCol=270, adjCol=c(0,0.5) )


 ## Show effect of offsetRow/offsetCol (only works when srtRow/srtCol is
 ## not also present)
 heatmap.2(x, offsetRow=0, offsetCol=0)
 heatmap.2(x, offsetRow=1, offsetCol=1)
 heatmap.2(x, offsetRow=2, offsetCol=2)
 heatmap.2(x, offsetRow=-1, offsetCol=-1)

 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=0, offsetCol=0)
 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=1, offsetCol=1)
 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=2, offsetCol=2)
 heatmap.2(x, srtRow=0, srtCol=90, offsetRow=-1, offsetCol=-1)


 ## Show how to use 'extrafun' to replace the 'key' with a scatterplot
 lmat <- rbind( c(5,3,4), c(2,1,4) )
 lhei <- c(1.5, 4)
 lwid <- c(1.5, 4, 0.75)

 myplot <- function() {
             oldpar <- par("mar")
             par(mar=c(5.1, 4.1, 0.5, 0.5))
             plot(mpg ~ hp, data=x)
           }

 heatmap.2(x, lmat=lmat, lhei=lhei, lwid=lwid, key=FALSE, extrafun=myplot)

 ## show how to customize the color key
 heatmap.2(x,
           key.title=NA, # no title
           key.xlab=NA,  # no xlab
           key.par=list(mgp=c(1.5, 0.5, 0),
                        mar=c(2.5, 2.5, 1, 0)),
           key.xtickfun=function() {
                 breaks <- parent.frame()$breaks
                 return(list(
                      at=parent.frame()$scale01(c(breaks[1],
                                                  breaks[length(breaks)])),
                      labels=c(as.character(breaks[1]),
                               as.character(breaks[length(breaks)]))
                      ))
           })

 heatmap.2(x,
          breaks=256,
          key.title=NA,
          key.xlab=NA,
          key.par=list(mgp=c(1.5, 0.5, 0),
                       mar=c(1, 2.5, 1, 0)),
          key.xtickfun=function() {
               cex <- par("cex")*par("cex.axis")
               side <- 1
               line <- 0
               col <- par("col.axis")
               font <- par("font.axis")
               mtext("low", side=side, at=0, adj=0,
                     line=line, cex=cex, col=col, font=font)
               mtext("high", side=side, at=1, adj=1,
                     line=line, cex=cex, col=col, font=font)
               return(list(labels=FALSE, tick=FALSE))
          })


 ##
 ## Show effect of z-score scaling within columns, blue-red color scale
 ##
 hv <- heatmap.2(x, col=bluered, scale="column", tracecol="#303030")

 ###
 ## Look at the return values
 ###
 names(hv)

 ## Show the mapping of z-score values to color bins
 hv$colorTable

 ## Extract the range associated with white
 hv$colorTable[hv$colorTable[,"color"]=="#FFFFFF",]

 ## Determine the original data values that map to white
 whiteBin <- unlist(hv$colorTable[hv$colorTable[,"color"]=="#FFFFFF",1:2])
 rbind(whiteBin[1] * hv$colSDs + hv$colMeans,
       whiteBin[2] * hv$colSDs + hv$colMeans )
 ##
 ## A more decorative heatmap, with z-score scaling along columns
 ##
 hv <- heatmap.2(x, col=cm.colors(255), scale="column",
	       RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),
	       xlab="specification variables", ylab= "Car Models",
	       main="heatmap(<Mtcars data>, ..., scale=\"column\")",
         tracecol="green", density="density")
 ## Note that the breakpoints are now symmetric about 0

 ## Color the labels to match RowSideColors and ColSideColors
 hv <- heatmap.2(x, col=cm.colors(255), scale="column",
         RowSideColors=rc, ColSideColors=cc, margin=c(5, 10),
	       xlab="specification variables", ylab= "Car Models",
	       main="heatmap(<Mtcars data>, ..., scale=\"column\")",
         tracecol="green", density="density", colRow=rc, colCol=cc,
         srtCol=45, adjCol=c(0.5,1))




 data(attitude)
 round(Ca <- cor(attitude), 2)
 symnum(Ca) # simple graphic

 # with reorder
 heatmap.2(Ca, 		 symm=TRUE, margin=c(6, 6), trace="none" )

 # without reorder
 heatmap.2(Ca, Rowv=FALSE, symm=TRUE, margin=c(6, 6), trace="none" )

 ## Place the color key below the image plot
 heatmap.2(x, lmat=rbind( c(0, 3), c(2,1), c(0,4) ), lhei=c(1.5, 4, 2 ) )

 ## Place the color key to the top right of the image plot
 heatmap.2(x, lmat=rbind( c(0, 3, 4), c(2,1,0 ) ), lwid=c(1.5, 4, 2 ) )

 ## For variable clustering, rather use distance based on cor():
 data(USJudgeRatings)
 symnum( cU <- cor(USJudgeRatings) )

 hU <- heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=topo.colors(16),
              distfun=function(c) as.dist(1 - c), trace="none")

 ## The Correlation matrix with same reordering:
 hM <- format(round(cU, 2))
 hM

 # now with the correlation matrix on the plot itself

 heatmap.2(cU, Rowv=FALSE, symm=TRUE, col=rev(heat.colors(16)),
             distfun=function(c) as.dist(1 - c), trace="none",
             cellnote=hM)

 ## genechip data examples
 ## Not run: 
 library(affy)
 data(SpikeIn)
 pms <- SpikeIn@pm

 # just the data, scaled across rows
 heatmap.2(pms, col=rev(heat.colors(16)), main="SpikeIn@pm",
              xlab="Relative Concentration", ylab="Probeset",
              scale="row")

 # fold change vs "12.50" sample
 data <- pms / pms[, "12.50"]
 data <- ifelse(data>1, data, -1/data)
 heatmap.2(data, breaks=16, col=redgreen, tracecol="blue",
               main="SpikeIn@pm Fold Changes\nrelative to 12.50 sample",
               xlab="Relative Concentration", ylab="Probeset")
 
## End(Not run)

---

Compute and Plot a 2-Dimensional Histogram

Description

Compute and plot a 2-dimensional histogram.

Usage

hist2d(x,y=NULL, nbins=200, same.scale=FALSE, na.rm=TRUE, show=TRUE,
       col=c("black", heat.colors(12)), FUN=base::length, xlab, ylab,
       ... )
## S3 method for class 'hist2d'
print(x, ...)

Arguments

x	either a vector containing the x coordinates or a matrix with 2 columns.
y	a vector contianing the y coordinates, not required if ‘x’ is matrix
nbins	number of bins in each dimension. May be a scalar or a 2 element vector. Defaults to 200.
same.scale	use the same range for x and y. Defaults to FALSE.
na.rm	Indicates whether missing values should be removed. Defaults to TRUE.
show	Indicates whether the histogram be displayed using image once it has been computed. Defaults to TRUE.
col	Colors for the histogram. Defaults to "black" for bins containing no elements, a set of 16 heat colors for other bins.
FUN	Function used to summarize bin contents. Defaults to base::length. Use, e.g., mean to calculate means for each bin instead of counts.
xlab, ylab	(Optional) x and y axis labels
...	Parameters passed to the image function.

Details

This fucntion creates a 2-dimensional histogram by cutting the x and y dimensions into nbins sections. A 2-dimensional matrix is then constucted which holds the counts of the number of observed (x,y) pairs that fall into each bin. If show=TRUE, this matrix is then then passed to image for display.

Value

A list containing 5 elements:

counts	Matrix containing the number of points falling into each bin
x.breaks, y.breaks	Lower and upper limits of each bin
x, y	midpoints of each bin

Author(s)

Gregory R. Warnes [email protected]

See Also

image, persp, hist, freq2d

Examples

## example data, bivariate normal, no correlation
   x <- rnorm(2000, sd=4)
   y <- rnorm(2000, sd=1)

   ## separate scales for each axis, this looks circular
   hist2d(x,y)

   ## same scale for each axis, this looks oval
   hist2d(x,y, same.scale=TRUE)

   ## use different ## bins in each dimension
   hist2d(x,y, same.scale=TRUE, nbins=c(100,200) )

   ## use the hist2d function to create an h2d object
   h2d <- hist2d(x,y,show=FALSE, same.scale=TRUE, nbins=c(20,30))

   ## show object summary
   h2d

   ## object contents
   str(h2d)

   ## perspective plot
   persp( h2d$x, h2d$y, h2d$counts,
          ticktype="detailed", theta=30, phi=30,
          expand=0.5, shade=0.5, col="cyan", ltheta=-30)

   ## for contour (line) plot ...
   contour( h2d$x, h2d$y, h2d$counts, nlevels=4 )

   ## for a filled contour plot ...
   filled.contour( h2d$x, h2d$y, h2d$counts, nlevels=4,
                   col=gray((4:0)/4) )

---

Plots to assess the goodness of fit for the linear model objects

Description

Plots to assess the goodness of fit for the linear model objects

Usage

lmplot2(
            x,
            which = 1:5,
            caption = c("Residuals vs Fitted", "Normal Q-Q plot",
              "Scale-Location plot", "Cook's distance plot"),
            panel = panel.smooth,
            sub.caption = deparse(x$call),
            main = "",
            ask = interactive() && nb.fig < length(which)
            && .Device != "postscript",
            ...,
            id.n = 3,
            labels.id = names(residuals(x)),
            cex.id = 0.75,
            band=TRUE,
            rug=TRUE,
            width=1/10,
            max.n=5000
            )

Arguments

x	lm object
which	Numerical values between 1 and 5, indicating which plots to be shown. The codes are: 1 Fitted vs residuals 2 Normal Q-Q 3 Scale-Location 4 Cook's distance 5 Residuals vs. predictor
caption	Caption for each type of plot
panel	function to draw on the existing plot
sub.caption	SubCaption for the plots
main	Main title of the plot
ask	whether interactive graphics
...	parameters passed to lmplot2.
id.n	integer value, less than or equal to residuals of lm object
labels.id	Names of the residuals of the lm object
cex.id	Parameter to control the height of text stringsx
band	logical vector indicating whether bandplot should also be plotted
rug	logical vector indicating whether rug should be added to the existing plot
width	Fraction of the data to use for plot smooths
max.n	Maximum number of points to display in plots

Note

This function replaces plot.lm2, which has been deprecated to avoid potential problems with S3 method dispatching.

Author(s)

Gregory R. Warnes [email protected] and Nitin Jain [email protected]

See Also

plot.lm

Examples

ctl <- rnorm(100, 4)
trt <- rnorm(100, 4.5)
group <- gl(2,100,200, labels=c("Ctl","Trt"))
weight <- c(ctl, trt)
wt.err <- rnorm(length(weight), mean=weight, sd=1/2)
x <- lm(weight ~ group + wt.err)

lmplot2(x)

lmplot2(x, which=1,   width=1/3)
lmplot2(x, which=1:3, width=1/3)

---

Scatter Plot Smoothing

Description

The lowess function performs the computations for the LOWESS smoother (see the reference below). lowess returns a an object containing components x and y which give the coordinates of the smooth. The smooth can then be added to a plot of the original points with the function lines.

Alternatively, plot can be called directly on the object returned from lowess and the 'lowess' method for plot will generate a scatterplot of the original data with a lowess line superimposed.

Finally, the plotLowess function both calculates the lowess smooth and plots the original data with a lowess smooth.

Usage

lowess(x, ...)

## Default S3 method:
lowess(x, y=NULL, f=2/3, iter=3L, delta=0.01 *
       diff(range(x)), ...)

## S3 method for class 'formula'
lowess(formula,data=parent.frame(), ..., subset, f=2/3,
       iter=3L, delta=.01*diff(range(mf[-response])))

## S3 method for class 'lowess'
plot(x, y, ..., col.lowess="red", lty.lowess=2)

plotLowess(formula, data=parent.frame(), ..., subset=parent.frame(),
           col.lowess="red", lty.lowess=2  )

Arguments

formula	formula providing a single dependent variable (y) and an single independent variable (x) to use as coordinates in the scatter plot.
data	a data.frame (or list) from which the variables in ‘formula’ should be taken.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
x, y	vectors giving the coordinates of the points in the scatter plot. Alternatively a single plotting structure can be specified.
f	the smoother span. This gives the proportion of points in the plot which influence the smooth at each value. Larger values give more smoothness.
iter	the number of robustifying iterations which should be performed. Using smaller values of iter will make lowess run faster.
delta	values of x which lie within delta of each other replaced by a single value in the output from lowess.
...	parameters for methods.
col.lowess, lty.lowess	color and line type for plotted line

References

Cleveland, W. S. (1979) Robust locally weighted regression and smoothing scatterplots. J. Amer. Statist. Assoc. 74, 829–836.

Cleveland, W. S. (1981) LOWESS: A program for smoothing scatterplots by robust locally weighted regression. The American Statistician, 35, 54.

See Also

loess (in package modreg), a newer formula based version of lowess (with different defaults!).

Examples

data(cars)

#
# x,y method
#
plot(cars$speed, cars$dist, main="lowess(cars)")
lines(lowess(cars$speed, cars$dist), col=2)
lines(lowess(cars$speed, cars$dist, f=.2), col=3)
legend(5, 120, c(paste("f=", c("2/3", ".2"))), lty=1, col=2:3)

#
# formula method: plot, then calculate the lowess smoother,
#                 then add smooth to the plot
#
plot(dist ~ speed, data=cars, main="lowess(cars)")
lines(lowess(dist ~ speed, data=cars), col=2, lty=2)
lines(lowess(dist ~ speed, data=cars, f=.2), col=3) # smaller bandwith
legend(5, 120, c(paste("f=", c("2/3", ".2"))), lty=1, col=2:3)

#
# formula method: calculate lowess() smoother, then call plot()
#                  on the lowess object
#
lw <- lowess(dist ~ speed, data=cars)
plot(lw, main="lowess(cars)"  )

#
# formula method: calculate and plot in a single command
#
plotLowess(dist ~ speed, data=cars, main="lowess(cars)")

---

Create an OpenOffice style plot

Description

An extension of barplot2. Creates bar- and line-plots mimicking the style of OpenOffice plots. This utility can plot the values next to each point or bar as well as confidence intervals.

Usage

ooplot(data, ...)
## Default S3 method:
ooplot(data, width=1, space=NULL, names.arg=NULL, 
                           legend.text=NULL, horiz=FALSE, 
                           density=NULL, angle=45, kmg="fpnumkMGTP", 
                           kmglim=TRUE, 
                           type=c("xyplot", "linear", "barplot", "stackbar"), 
                           col=heat.colors(NC), prcol=NULL, 
                           border=par("fg"), main=NULL, sub=NULL, 
                           xlab=NULL, ylab=NULL, xlim=NULL, ylim=NULL, 
                           xpd=TRUE, log="", axes=TRUE, 
                           axisnames=TRUE, prval=TRUE, lm=FALSE,
                           cex.axis=par("cex.axis"), 
                           cex.names=par("cex.axis"),
                           cex.values=par("cex"),inside=TRUE, 
                           plot=TRUE, axis.lty=0, plot.ci=FALSE, 
                           ci.l=NULL, ci.u=NULL, ci.color="black", 
                           ci.lty="solid", ci.lwd=1, plot.grid=FALSE, 
                           grid.inc=NULL, grid.lty="dotted", 
                           grid.lwd=1, grid.col="black", add=FALSE, 
                           by.row=FALSE, ...)

Arguments

data	a matrix of values describing the values that make up the plot. The first column of data is taken as the axis against which all the other values are plotted. The first column of data may not be sparse.
width	optional vector of barwidths. Re-cycled to the number of bars drawn. A single value will have no visible effect.
space	the amount of space left before each bar. May be given as a single number or one number per bar. If type is stackbar, space may be specified by two numbers, where the first is the space between bars in the same group, and the second the space between groups. Defaults to c(0,1) if type is a stackbar, and to 0.2 otherwise.
names.arg	a vector of names to be plotted below each bar or group of bars. If this argument is omitted, then the names are taken from the row names of data.
legend.text	a vector of text used to construct a legend for the plot, or a logical indicating whether a legend should be included; if legend.text is true, the row names of data will be used as labels if they are non-null.
horiz	a logical value. If FALSE, the bars are drawn vertically with the first bar to the left. If TRUE, the bars are drawn horizontally with the first at the bottom.
density	a vector giving the the density of shading lines, in lines per inch, for the bars or bar components. The default value of NULL means that no shading lines are drawn. Non-positive values of density also inhibit the drawing of shading lines.
angle	the slope of shading lines, given as an angle in degrees (counter-clockwise), for the bars or bar components.
kmg	the set of SI units to convert, defaults to "fpnumkMGTP". See below for details.
kmglim	logical. If FALSE the conversion to SI units is not performed. Default is TRUE.
type	a string indicating the preferred format of the plot, choices are: xyplot : plot where y is plotted against the x-value. linear : plot where y values are plotted against equidistant x-values. barplot : plot where y values are represented as bars against equidistant x-values. stackplot : plot where y values are stacked for identical x-values and bars are equidistant.
col	a vector of colors for the bars or bar components.
prcol	the color to be used for the plot region.
border	the color to be used for the border of the bars.
main, sub	overall and sub titles for the plot.
xlab	a label for the x axis.
ylab	a label for the y axis.
xlim	limits for the x axis.
ylim	limits for the y axis.
xpd	logical. Should bars be allowed to go outside region?
log	a character string which contains ‘"x"’ if the x axis is to be logarithmic, ‘"y"’ if the y axis is to be logarithmic and ‘"xy"’ or ‘"yx"’ if both axes are to be logarithmic.
axes	logical. If TRUE, a vertical (or horizontal, if horiz is true) axis is drawn.
axisnames	logical. If TRUE, and if there are names.arg (see above), the other axis is drawn (with lty=0) and labeled.
prval	logical. If TRUE, then values are plotted above all points and bars.
lm	logical. If TRUE, the linear fit is plotted.
cex.axis, cex.names, cex.values	character scaling factor for numeric axis labels, names, and displayed values, respectively.
inside	logical. If TRUE, the lines which divide adjacent (non-stacked!) bars will be drawn. Only applies when space = 0 (which it partly is when beside = TRUE).
plot	logical. If FALSE, nothing is plotted.
axis.lty	the graphics parameter lty applied to the axis and tick marks of the categorical (default horzontal) axis. Note that by default the axis is suppressed.
plot.ci	logical. If TRUE, confidence intervals are plotted over the bars. Note that if a stacked bar plot is generated, confidence intervals will not be plotted even if plot.ci = TRUE
ci.l, ci.u	The confidence intervals (ci.l = lower bound, ci.u = upper bound) to be plotted if plot.ci = TRUE. Values must have the same dim structure as height.
ci.color	the color for the confidence interval line segments
ci.lty	the line type for the confidence interval line segments
ci.lwd	the line width for the confidence interval line segments
plot.grid	if TRUE a lined grid will be plotted behind the bars
grid.inc	the number of grid increments to be plotted
grid.lty	the line type for the grid
grid.lwd	the line width for the grid
grid.col	the line color for the grid
add	logical, if TRUE add barplot to current plot.
by.row	Logical value. If TRUE the data matrix is organized with variables along rows rather than down colums.
...	further graphical parameters (par) are passed to plot.window(), title() and axis.

Details

Plot units are automatically scaled to SI units based on the maximum value present, according to the set of units specified by characters in the kmg parameter. These letters are interpreted as

P

peta = 1E15

T

tera = 1E12

G

giga = 1E09

M

mega = 1E06

k

kilo = 1E03

m

milli= 1E-03

u

micro= 1E-06

n

nano = 1E-09

p

pico = 1E-12

f

femto= 1E-15

with the default being "fpnumkMGTP" (all of these units). For example, if the largest value plotted is 1243000, it would be presented as 1.234M.

Value

A numeric vector (or matrix, when beside = TRUE), say mp, giving the coordinates of all the bar midpoints drawn, useful for adding to the graph.

If beside is true, use colMeans(mp) for the midpoints of each group of bars, see example.

Author(s)

Lodewijk Bonebakker [email protected] with modifications by Gregory R. Warnes [email protected]. Based on barplot2().

See Also

plot, boxplot

Examples

data(VADeaths, package = "datasets")

     VADeaths <- cbind( Age=c(50,55,60,65,70), VADeaths)

     mp <- ooplot(VADeaths) # default
     mp <- ooplot(VADeaths, type="xyplot")  # same as default
     mp <- ooplot(VADeaths, type="linear")  # linear scale
     mp <- ooplot(VADeaths, type="linear", log="y") # log scale on y axis
     mp <- ooplot(VADeaths, type="barplot") # barplot
     mp <- ooplot(VADeaths, type="stackbar") # stacked


     tot <- colMeans(VADeaths[,-1])
     ooplot(VADeaths, 
             col = c("lightblue", "mistyrose", "lightcyan", "lavender"),
             legend = colnames(VADeaths)[-1], ylim = c(0, 100),
             type="barplot", cex.values=0.75)
     title(main = "Death Rates in Virginia", font.main = 4)


     ##
     ## Capability demo
     ##
     ## examples for the ooplot routine
     ##
     ## create some test data
     test1 <- data.frame(x=c(0,1,2,3,4), lin=c(0,1,2,3,4))
     test2 <- data.frame(x=c(0,1,2,3,4), par=c(0,1,4,9,16))
     test3 <- data.frame(x=c(-2,-1,0,1,2),y2=c(4,1,0,1,4))
     ## single line test example
     test1f <- test1
     ## two column example
     test2f <- merge(test1,test2,by.x="x",all=TRUE,sort=TRUE)
     ## three column example
     test3f <- merge(test2f,test3,by.x="x",all=TRUE,sort=TRUE)
     ## subset, single row, example
     test5r <- test3f[5,]  
     
     ##
     ## xyplot, linear, barplot, stackbar
     dev.off()
     mat <- matrix(c(1:16),4,4,byrow=TRUE)
     layout(mat)
     
     ooplot(test1f,type="barplot",col=c("red"))
     title(main="barplot")
     ooplot(test2f,type="barplot",col=c("red","blue"))
     ooplot(test3f,type="barplot",col=c("red","blue","green"))
     ooplot(test5r,type="barplot",col=c("red","blue","green"))
     
     ooplot(test1f,type="xyplot",col=c("red"))
     title(main="xyplot")
     ooplot(test2f,type="xyplot",col=c("red","blue"))
     ooplot(test3f,type="xyplot",col=c("red","blue","green"))
     ooplot(test5r,type="xyplot",col=c("red","blue","green"))
     
     ooplot(test1f,type="linear",col=c("red"))
     title(main="linear")
     ooplot(test2f,type="linear",col=c("red","blue"))
     ooplot(test3f,type="linear",col=c("red","blue","green"))
     ooplot(test5r,type="linear",col=c("red","blue","green"))
     
     ooplot(test1f,type="stackbar",col=c("red"))
     title(main="stackbar")
     ooplot(test2f,type="stackbar",col=c("red","blue"))
     ooplot(test3f,type="stackbar",col=c("red","blue","green"))
     ooplot(test5r,type="stackbar",col=c("red","blue","green"))

     # restore default layout (1 plot/page)
     layout(1)

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Plot multiple variables on the same region, with appropriate axes

Description

overplot graphs a set of variables defined on the same x-range but which have varying y-ranges on the same plotting area. For each set of y-values it uses a different color and line-type and and draws a correspondingly colored and line-typed axis. panel.overplot is used by overplot to draw the individual graphs.

Usage

overplot(formula, data = parent.frame(), same.scale = FALSE, xlab, ylab,
         xlim, ylim, min.y, max.y, log = "", panel = "panel.overplot",
         subset, plot = TRUE, groups, main, f = 2/3, ...)

Arguments

formula	Formula describing the x and y variables. It should be of the form x ~ y|z. The conditioning variable (z) should be a factor.
same.scale	Logical value indicating whether the plot region should have the same range for all plots. Defaults to FALSE.
xlab, ylab, xlim, ylim, main	Standard plotting parameters. See plot for details
min.y, max.y	Scalar or vector values used to specify the y plotting limits for individual plots. If a single scalar value is provided, it will be used for all plots. These parameters can be used specify one end of the individual plot ranges, while allowing the other end to vary with the data. EG, to force 0 to always be within the plot region.
log	character string ”, 'x', 'y', or 'xy', indicating which axes should be plotted on a log scale. Defaults to ” (neither).
panel	a plotting function to be called to draw the individual plots. Defaults to overplot.panel, which plots the points and a lowess smooth.
plot	Logical value indicating whether to draw the plot.
groups	(optional) character vector giving the names of levels of the conditioning variable to plot. Defaults to all levels of the conditioning variable.
f	Smoothing parameter for lowess
data, subset, ...	parameters passed to model.frame to obtain the data to be plotted from the formula.

Details

This function essentially performs

tmp <- split(data, z)

for(i in levels(z))

plot( x ~ y, data=tmp[[z]] )

except that all of the plots are shown on the same plotting region and varying scales for each value of z are handled nicely.

Value

A copy of the data split by the conditioning variable.

Author(s)

Gregory R. Warnes [email protected]

See Also

interaction.plot, coplot for alternative visualizations of 3-way data.

Examples

# Example teratogenicity rtPCR data
data(rtPCR)

# same scale
overplot( RQ ~ Conc..ug.ml. | Test.Substance,
         data=rtPCR,
         subset=Detector=="ProbeType 1" & Conc..ug.ml. > 0,
         same.scale=TRUE,
         log="xy",
         f=3/4,
         main="Detector=ProbeType 1",
         xlab="Concentration (ug/ml)",
         ylab="Relative Gene Quantification"
         )

# different scales, but force lower limit to 0.01 
overplot( RQ ~ Conc..ug.ml. | Test.Substance,
         data=rtPCR,
         subset=Detector=="ProbeType 8" & Conc..ug.ml. > 0,
         log="xy",
         f=3/4,
         main="Detector=ProbeType 8",
         xlab="Concentration (ug/ml)",
         ylab="Relative Gene Quantification",
         min.y=0.01
         )

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