Title: | Various R Programming Tools for Plotting Data |
---|---|
Description: | Various R programming tools for plotting data, including: - calculating and plotting locally smoothed summary function as ('bandplot', 'wapply'), - enhanced versions of standard plots ('barplot2', 'boxplot2', 'heatmap.2', 'smartlegend'), - manipulating colors ('col2hex', 'colorpanel', 'redgreen', 'greenred', 'bluered', 'redblue', 'rich.colors'), - calculating and plotting two-dimensional data summaries ('ci2d', 'hist2d'), - enhanced regression diagnostic plots ('lmplot2', 'residplot'), - formula-enabled interface to 'stats::lowess' function ('lowess'), - displaying textual data in plots ('textplot', 'sinkplot'), - plotting a matrix where each cell contains a dot whose size reflects the relative magnitude of the elements ('balloonplot'), - plotting "Venn" diagrams ('venn'), - displaying Open-Office style plots ('ooplot'), - plotting multiple data on same region, with separate axes ('overplot'), - plotting means and confidence intervals ('plotCI', 'plotmeans'), - spacing points in an x-y plot so they don't overlap ('space'). |
Authors: | Gregory R. Warnes [aut], Ben Bolker [aut], Lodewijk Bonebakker [aut], Robert Gentleman [aut], Wolfgang Huber [aut], Andy Liaw [aut], Thomas Lumley [aut], Martin Maechler [aut], Arni Magnusson [aut], Steffen Moeller [aut], Marc Schwartz [aut], Bill Venables [aut], Tal Galili [ctb, cre] |
Maintainer: | Tal Galili <[email protected]> |
License: | GPL-2 |
Version: | 3.1.3 |
Built: | 2024-08-15 05:07:19 UTC |
Source: | https://github.com/talgalili/gplots |
Add a labeled axis to the current plot with rotated text
angleAxis(side, labels, at = 1:length(labels), srt = 45, adj, xpd = TRUE, ...)
angleAxis(side, labels, at = 1:length(labels), srt = 45, adj, xpd = TRUE, ...)
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 |
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 |
This function augments the feature of the axis
functon by allowing the axis labels to be rotated.
Gregory R. Warnes [email protected]
# 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()$mar par(mar=c(2,5,3,5)+0.1) at <- barplot(values, yaxt="n", horiz=TRUE) angleAxis(4, at=at, labels = names(values)) par(oldpar) # specify colors for bars and labels at <- barplot(values, xaxt="n", col=1:10) angleAxis(1, at=at, labels = names(values), col=1:10)
# create a vector with some values and long labels values <- sample(1:10) names(values) <- sapply(letters[1:10], function(x) paste(rep(x, 10), sep="",collapse="") ) # barplot labels are too long for the available space, hence some are not plotted barplot(values) # to add angled labels, tell barplot not to label the x axis, and store the bar location at <- barplot(values, xaxt="n") # then use angleAxs angleAxis(1, at=at, labels = names(values)) # angle counter-clockwise instead at <- barplot(values, xaxt="n") angleAxis(1, at=at, labels = names(values), srt=-45, adj=0) # put labels at the top oldpar <- par()$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.
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, ... )
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, ... )
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. |
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 |
show.margins |
boolean. If |
cum.margins |
boolean. If |
sorted |
boolean. If |
label.lines |
boolean. If |
hide.duplicates |
boolean. If |
fun |
function to be used to combine data elements with the same
levels of the grouping variables |
... |
Additional arguments passed to |
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.
Nothing of interest.
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.
Gregory R. Warnes [email protected]
Function inspired by question posed on R-help by Ramon Alonso-Allende [email protected].
# 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)
# 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 curves for locally smoothed mean and standard deviation.
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)
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)
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 |
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. |
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 |
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.
Invisibly returns a list containing the x,y points plotted for each line.
Gregory R. Warnes [email protected]
# 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))
# 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))
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.
## 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, ...)
## 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, ...)
height |
either a vector or matrix of values describing the
bars which make up the plot. If |
width |
optional vector of bar widths. Re-cycled to length the
number of bars drawn. Specifying a single value will no visible
effect unless |
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 |
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 |
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 |
beside |
a logical value. If |
horiz |
a logical value. If |
density |
a vector giving the the density of shading lines, in
lines per inch, for the bars or bar components.
The default value of |
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 |
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 |
axisnames |
logical. If |
cex.axis |
expansion factor for numeric axis labels. |
cex.names |
expansion factor for names. |
inside |
logical. If |
plot |
logical. If |
axis.lty |
the graphics parameter |
offset |
a vector indicating how much the bars should be shifted relative to the x axis. |
plot.ci |
logical. If |
ci.l , ci.u
|
The confidence intervals (ci.l = lower bound, ci.u =
upper bound) to be plotted if |
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 |
plot.grid |
if |
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 |
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
|
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 ( |
This is a generic function, it currently only has a default method. A formula interface may be added eventually.
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.
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.
Original barplot() by R-Core. Enhancements by Marc Schwartz (email was marc\[email protected], may no longer be active)
plot(..., type = "h")
, dotchart
,
hist
.
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")
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")
This funcntion uses boxplot
to produce a boxplot which is then
annotated with the number of observations in each group.
boxplot2(..., top=FALSE, shrink=1, textcolor=NULL)
boxplot2(..., top=FALSE, shrink=1, textcolor=NULL)
... |
parameters passed to |
top |
logical indicating whether the number of observations
should be added to the top or the bottom of the plotting
region. Defaults to |
shrink |
value to shrink character size (cex) when annotating. |
textcolor |
text color. |
This function replaces boxplot.n
, which has been deprecated
avoid potential problems with S3 method dispatching.
Gregory R. Warnes [email protected]
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")
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")
Create 2-dimensional empirical confidence regions from provided data.
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, ...)
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, ...)
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 |
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 |
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 |
pch |
Point type for plots. See |
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 |
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.
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 |
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.
Gregory R. Warnes [email protected]
#### ## 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)
#### ## 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
col2hex(cname)
col2hex(cname)
cname |
Color name(s) |
Character vector giving the hex color code translation of the provided color names.
Gregory R. Warnes
col2hex(c("red","yellow","lightgrey"))
col2hex(c("red","yellow","lightgrey"))
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
colorpanel(n, low, mid, high) redgreen(n) greenred(n) bluered(n) redblue(n)
colorpanel(n, low, mid, high) redgreen(n) greenred(n) bluered(n) redblue(n)
n |
Desired number of color elements in the panel. |
low , mid , high
|
Colors to use for the Lowest, middle,
and highest values. |
The values for low, mid, high
can be given as
color names ('red'), plot color index (2=red), and HTML-style RGB,
("\#FF0000"=red).
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
.
Vector of HTML-style RGB colors.
Gregory R. Warnes [email protected]
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))
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))
These functions are defunct and have been removed from the gplots package.
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$call), main = "", ask, ..., id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75, band=TRUE, rug=TRUE, width=1/10, max.n=5000 ) smartlegend(x = c("left", "center", "right"), y = c("top", "center", "bottom"), ..., inset = 0.05)
boxplot.n(..., top=FALSE, shrink=1, textcolor=NULL) plot.lm2( x, which = 1:5, caption = c("Residuals vs Fitted", "Normal Q-Q plot", "Scale-Location plot", "Cook's distance plot"), panel = panel.smooth, sub.caption = deparse(x$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)
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 |
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
.
Gregory R. Warnes [email protected]
boxplot2
,
lmplot2
,
legend
,
Defunct
These functions have been deprecated and will be removed in future releases of gplots.
## No deprecated functions at this time ##
## No deprecated functions at this time ##
These functions have been deprecated. Please refer to the manual page for the replacement function:
(No deprecated functions at this time)
Gregory R. Warnes [email protected]
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.
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, ... )
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, ... )
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
|
Colv |
determines if and how the column dendrogram should
be reordered. Has the options as the |
distfun |
function used to compute the distance (dissimilarity)
between both rows and columns. Defaults to |
hclustfun |
function used to compute the hierarchical clustering
when |
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 |
|
.
symm |
logical indicating if |
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 |
na.rm |
logical indicating whether |
revC |
logical indicating if the column order should be
|
add.expr |
expression that will be evaluated after the call to
|
breaks |
(optional) Either a numeric vector indicating the
splitting points for binning |
symbreaks |
Boolean indicating whether breaks should be
made symmetric about 0. Defaults to |
col |
colors used for the image. Defaults to 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
|
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 |
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 |
notecol |
(optional) character string specifying the color for
|
na.color |
Color to use for missing value ( |
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 |
margins |
numeric vector of length 2 containing the margins
(see |
ColSideColors |
(optional) character vector of length
|
RowSideColors |
(optional) character vector of length
|
cexRow , cexCol
|
positive numbers, used as |
labRow , labCol
|
character vectors with row and column labels to
use; these default to |
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 |
symkey |
Boolean indicating whether the color key should be
made symmetric about 0. Defaults to |
densadj |
Numeric scaling value for tuning the kernel width when
a density plot is drawn on the color key. (See the |
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 |
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 |
key.par |
graphical parameters for the color key. Named list that
can be passed to |
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 |
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.
Invisibly, a list with components
rowInd |
row index permutation vector as returned by
|
colInd |
column index permutation vector. |
call |
the matched call |
rowMeans , rowSDs
|
mean and standard deviation of each row: only
present if |
colMeans , colSDs
|
mean and standard deviation of each column: only
present if |
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
|
hline |
center-line value used for row trace, present only if
|
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
|
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=...)
.
Andy Liaw, original; R. Gentleman, M. Maechler, W. Huber, G. Warnes, revisions.
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)
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.
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, ...)
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, ...)
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
|
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
|
xlab , ylab
|
(Optional) x and y axis labels |
... |
Parameters passed to the image function. |
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.
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 |
Gregory R. Warnes [email protected]
## 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$counts, nlevels=4, col=gray((4:0)/4) )
## example data, bivariate normal, no correlation x <- rnorm(2000, sd=4) y <- rnorm(2000, sd=1) ## separate scales for each axis, this looks circular hist2d(x,y) ## same scale for each axis, this looks oval hist2d(x,y, same.scale=TRUE) ## use different ## bins in each dimension hist2d(x,y, same.scale=TRUE, nbins=c(100,200) ) ## use the hist2d function to create an h2d object h2d <- hist2d(x,y,show=FALSE, same.scale=TRUE, nbins=c(20,30)) ## show object summary h2d ## object contents str(h2d) ## perspective plot persp( h2d$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
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$call), main = "", ask = interactive() && nb.fig < length(which) && .Device != "postscript", ..., id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75, band=TRUE, rug=TRUE, width=1/10, max.n=5000 )
lmplot2( x, which = 1:5, caption = c("Residuals vs Fitted", "Normal Q-Q plot", "Scale-Location plot", "Cook's distance plot"), panel = panel.smooth, sub.caption = deparse(x$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 )
x |
lm object |
which |
Numerical values between 1 and 5, indicating which plots to be shown. The codes are:
|
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 |
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 |
This function replaces plot.lm2
, which has been deprecated
to avoid potential problems with S3 method dispatching.
Gregory R. Warnes [email protected] and Nitin Jain [email protected]
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)
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)
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.
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 )
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 )
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 |
delta |
values of |
... |
parameters for methods. |
col.lowess , lty.lowess
|
color and line type for plotted line |
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.
loess
(in package modreg
), a newer
formula based version of lowess
(with different defaults!).
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)")
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)")
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.
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, ...)
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, ...)
data |
a matrix of values describing the values that make up the
plot. The first column of |
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 |
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 |
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 |
horiz |
a logical value. If |
density |
a vector giving the the density of shading lines, in
lines per inch, for the bars or bar components.
The default value of |
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 |
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 |
axisnames |
logical. If |
prval |
logical. If |
lm |
logical. If |
cex.axis , cex.names , cex.values
|
character scaling factor for numeric axis labels, names, and displayed values, respectively. |
inside |
logical. If |
plot |
logical. If |
axis.lty |
the graphics parameter |
plot.ci |
logical. If |
ci.l , ci.u
|
The confidence intervals (ci.l = lower bound, ci.u =
upper bound) to be plotted if |
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 |
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 |
by.row |
Logical value. If |
... |
further graphical parameters ( |
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
peta = 1E15
tera = 1E12
giga = 1E09
mega = 1E06
kilo = 1E03
milli= 1E-03
micro= 1E-06
nano = 1E-09
pico = 1E-12
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.
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.
Lodewijk Bonebakker [email protected] with modifications by Gregory R. Warnes [email protected]. Based on barplot2().
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)
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)
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.
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, ...)
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, ...)
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 |
xlab , ylab , xlim , ylim , main
|
Standard plotting parameters. See
|
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 |
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 |
data , subset , ...
|
parameters passed to |
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.
A copy of the data split by the conditioning variable.
Gregory R. Warnes [email protected]
interaction.plot
,
coplot
for alternative visualizations of 3-way data.
# 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 )
# 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 )
Given a set of x and y values and interval width or upper and lower bounds, plot the points with error bars. This can be a useful tool for visualizing confidence intervals.
plotCI(x, y = NULL, uiw, liw = uiw, ui, li, err='y', ylim=NULL, xlim=NULL, type="p", col=par("col"), barcol=col, pt.bg = par("bg"), sfrac = 0.01, gap=1, lwd=par("lwd"), lty=par("lty"), labels=FALSE, add=FALSE, xlab, ylab, minbar, maxbar, ... )
plotCI(x, y = NULL, uiw, liw = uiw, ui, li, err='y', ylim=NULL, xlim=NULL, type="p", col=par("col"), barcol=col, pt.bg = par("bg"), sfrac = 0.01, gap=1, lwd=par("lwd"), lty=par("lty"), labels=FALSE, add=FALSE, xlab, ylab, minbar, maxbar, ... )
x , y
|
coordinates for the center of error bars. |
uiw |
width of the upper or right error bar. Set to |
liw |
width of the lower or left error bar. Defaults to same value as
|
ui |
upper end of error bars. Defaults to |
li |
lower end of error bars. Defaults to |
err |
direction for error bars. Set to "y" for vertical bars. Set to "x" for horizontal bars. Defaults to "y". |
col |
color of plotting character used center marker of error bars. Default is "black". |
xlim , ylim
|
range of x/y values to include in the plotting area. |
type |
point/line type; passed to |
barcol |
color of the error bars. Defaults to the same value as
|
pt.bg |
background color of points (use
|
sfrac |
width of "crossbar" at the end of error bar as a fraction of the x plotting region. Defaults to 0.01. |
gap |
space left between the center of the error bar and the lines marking the error bar in units of the height (width) of the letter "O". Defaults to 1.0 |
lwd |
width of bar lines. |
lty |
line type of bar lines. |
labels |
either a logical value indicating whether the circles
representing the x values should be replaced with text giving the
actual values or a vector containing labels to use
instead. Defaults to |
add |
logical indicating whether error bars should be added to
the current plot. If |
minbar |
minumum allowed value for bar ends. If specified,
values smaller than |
maxbar |
maximum allowed value for bar ends. If specified,
values larger than |
... |
optional plotting parameters |
xlab |
label for x axis. |
ylab |
label for y axis. |
Original version by Bill Venables [email protected] posted to r-help on Sep. 20, 1997. Enhanced version posted to r-help by Ben Bolker [email protected] on Apr. 16, 2001. This version was modified and extended by Gregory R. Warnes [email protected]. Additional changes suggested by Martin Maechler [email protected] integrated on July 29, 2004.
plotmeans
provides an enhanced wrapper to
plotCI
.
# plot means and data(state) tmp <- split(state.area, state.region) means <- sapply(tmp, mean) stdev <- sqrt(sapply(tmp, var)) n <- sapply(tmp,length) ciw <- qt(0.975, n) * stdev / sqrt(n) # plain plotCI(x=means, uiw=ciw) # prettier plotCI(x=means, uiw=ciw, col="black", barcol="blue", lwd=1) # give mean values plotCI(x=means, uiw=ciw, col="black", barcol="blue", labels=round(means,-3), xaxt="n", xlim=c(0,5) ) axis(side=1, at=1:4, labels=names(tmp), cex=0.7) # better yet, just use plotmeans ... # plotmeans( state.area ~ state.region )
# plot means and data(state) tmp <- split(state.area, state.region) means <- sapply(tmp, mean) stdev <- sqrt(sapply(tmp, var)) n <- sapply(tmp,length) ciw <- qt(0.975, n) * stdev / sqrt(n) # plain plotCI(x=means, uiw=ciw) # prettier plotCI(x=means, uiw=ciw, col="black", barcol="blue", lwd=1) # give mean values plotCI(x=means, uiw=ciw, col="black", barcol="blue", labels=round(means,-3), xaxt="n", xlim=c(0,5) ) axis(side=1, at=1:4, labels=names(tmp), cex=0.7) # better yet, just use plotmeans ... # plotmeans( state.area ~ state.region )
Plot group means and confidence intervals.
plotmeans(formula, data=NULL, subset, na.action, bars=TRUE, p=0.95, minsd=0, minbar, maxbar, xlab=names(mf)[2], ylab=names(mf)[1], mean.labels=FALSE, ci.label=FALSE, n.label=TRUE, text.n.label="n=", digits=getOption("digits"), col="black", barwidth=1, barcol="blue", connect=TRUE, ccol= col, legends=names(means), xaxt, use.t=TRUE, lwd=par("lwd"), ...)
plotmeans(formula, data=NULL, subset, na.action, bars=TRUE, p=0.95, minsd=0, minbar, maxbar, xlab=names(mf)[2], ylab=names(mf)[1], mean.labels=FALSE, ci.label=FALSE, n.label=TRUE, text.n.label="n=", digits=getOption("digits"), col="black", barwidth=1, barcol="blue", connect=TRUE, ccol= col, legends=names(means), xaxt, use.t=TRUE, lwd=par("lwd"), ...)
formula |
symbolic expression specifying the outcome (continuous) and grouping variable (factor). See lm() for details. |
data |
optional data frame containing the variables in the model. |
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 ‘NA’s. See lm() for details. |
bars |
a logical value indicating whether confidence interval bars should be plotted. Defaults to TRUE. |
p |
confidence level for error bars. Defaults to 0.95. |
minsd |
minumum permitted value for the standard deviation within
each factor level. Any standard deviation estimates smaller than
|
minbar |
minumum allowed value for bar ends. If specified,
values smaller than |
maxbar |
maximum allowed value for bar ends. If specified,
values larger than |
xlab |
x-axis label. |
ylab |
y-axis label. |
mean.labels |
either a logical value indicating whether the circles representing the group means should be replaced with text giving the actual mean values or a vector containing labels to use instead. Defaults to FALSE. |
ci.label |
a logical value indicating whether text giving the actual interval end values should be placed at the end of each confidence interval bar. Defaults to FALSE. |
n.label |
a logical value indicating whether text giving the number of observations in each group should should be added to the plot. |
text.n.label |
Prefix text for labeling observation counts. Defaults to "n=". |
digits |
number of significant digits to use when displaying mean or confidince limit values. |
col |
color of cicles marking group means. Default is "black". |
barwidth |
linewidth of interval bars and end marks. Default is 1. |
barcol |
color of interval bars and end marks. Default is "blue". |
connect |
either a logical value indicating whether the means of each group should be connected by a line, or a list of vectors giving the index of bars that should be connected by a line. Defaults to TRUE. |
ccol |
color of lines used to connect means. Defaults to the same color as "col". |
legends |
vector containing strings used to label groups along the x axis. Defaults to group names. |
xaxt |
A character which specifies the axis type. Specifying ‘"n"’ causes an axis to be set up, but not plotted. |
use.t |
a logical value indicating whether the t distribution
should be used to compute confidence intervals. If |
lwd |
Width of connecting lines |
... |
optional plotting parameters. |
Gregory R. Warnes [email protected]
# library(gplots) # show comparison with boxplot data(state) plotmeans(state.area ~ state.region) # show some color and mean labels plotmeans(state.area ~ state.region, mean.labels=TRUE, digits=-3, col="red", connect=FALSE) # show how to specify which means should be connected plotmeans(state.area ~ state.region, connect=list(1:2, 3:4), ccol="red", pch=7 ) # more complicated example showing how to show an interaction data(esoph) par(las=2, # use perpendicular axis labels mar=c(10.1,4.1,4.1,2.1), # create enough space for long x labels mgp=c(8,1,0) # move x axis legend down to avoid overlap ) plotmeans(ncases/ncontrols ~ interaction(agegp , alcgp, sep =" "), connect=list(1:6,7:12,13:18,19:24), barwidth=2, col="dark green", data=esoph, xlab="Age Group and Alcohol Consumption", ylab="# Cases / # Controls", ylim = c(-.9,1.4), main=c("Fraction of Cases for by Age and Alcohol Consumption", "Ile-et-Vilaine Esophageal Cancer Study") ) abline(v=c(6.5, 12.5, 18.5), lty=2)
# library(gplots) # show comparison with boxplot data(state) plotmeans(state.area ~ state.region) # show some color and mean labels plotmeans(state.area ~ state.region, mean.labels=TRUE, digits=-3, col="red", connect=FALSE) # show how to specify which means should be connected plotmeans(state.area ~ state.region, connect=list(1:2, 3:4), ccol="red", pch=7 ) # more complicated example showing how to show an interaction data(esoph) par(las=2, # use perpendicular axis labels mar=c(10.1,4.1,4.1,2.1), # create enough space for long x labels mgp=c(8,1,0) # move x axis legend down to avoid overlap ) plotmeans(ncases/ncontrols ~ interaction(agegp , alcgp, sep =" "), connect=list(1:6,7:12,13:18,19:24), barwidth=2, col="dark green", data=esoph, xlab="Age Group and Alcohol Consumption", ylab="# Cases / # Controls", ylim = c(-.9,1.4), main=c("Fraction of Cases for by Age and Alcohol Consumption", "Ile-et-Vilaine Esophageal Cancer Study") ) abline(v=c(6.5, 12.5, 18.5), lty=2)
Makes a half or full normal plot for the effects from a model
inheriting from
class aov
. One can interactively label the points in the plot.
## S3 method for class 'aov' qqnorm(y, full=FALSE, label=FALSE, omit=NULL, xlab=paste(if (full) "" else "Half", " Normal plot"), ylab="Effects", ...)
## S3 method for class 'aov' qqnorm(y, full=FALSE, label=FALSE, omit=NULL, xlab=paste(if (full) "" else "Half", " Normal plot"), ylab="Effects", ...)
y |
A model object inheriting from |
full |
Full or half normal plot (half is default) |
label |
If |
omit |
Numeric or character vector of effects to omit, the intercept is always omitted |
xlab |
Horizontal axix label |
ylab |
Vertical axis label |
... |
Further arguments to be given to the plot function |
Produces a (half) normal plot of the effects from an AOV model. The idea behind the plot is that most effects will be small or null, and this effects can be used as a basis for estimation of the experimental variance. This small effects will show up in the plot as a straight line, other effects can be judged against this as a background. Heavily used by Box, Hunter \& Hunter, which attributes the idea to Daniel.
This is a simpler implementation than the one in S-Plus.
If label=TRUE
, the vector of points identified, else nothing of
interest.
Kjetil Halvorsen [email protected]
Box, Hunter and Hunter: Statistics for Experimenters. An Introduction
to Design, Data Analysis and Model Building. Wiley.
Daniel, C (1976): Applications of Statistics to Industrial
Experimentation. Wiley.
Daniel, C (1959): Use of half-normal plot in interpreting factorial
two-level experiments. Technometrics.1, 149.
library(MASS) data(npk) npk.aov <- aov(yield ~ block + N*P*K, npk) qqnorm(npk.aov) ## interactive labeling of points. Click mouse on points to show label. if (dev.interactive()) qqnorm(npk.aov, omit=2:6, label=TRUE)
library(MASS) data(npk) npk.aov <- aov(yield ~ block + N*P*K, npk) qqnorm(npk.aov) ## interactive labeling of points. Click mouse on points to show label. if (dev.interactive()) qqnorm(npk.aov, omit=2:6, label=TRUE)
Reorder the levels of a factor
## S3 method for class 'factor' reorder(x, X, FUN, ..., order=is.ordered(x), new.order, sort=mixedsort)
## S3 method for class 'factor' reorder(x, X, FUN, ..., order=is.ordered(x), new.order, sort=mixedsort)
x |
factor |
X |
auxillary data vector |
FUN |
function to be applied to subsets of |
... |
optional parameters to |
order |
logical value indicating whether the returned
object should be an |
new.order |
a vector of indexes or a vector of label names giving the order of the new factor levels |
sort |
function to use to sort the factor level names, used only
when |
This function changes the order of the levels of a factor. It can do
so via three different mechanisms, depending on whether, X
and FUN
, new.order
or sort
are provided.
If X
and Fun
are provided: The data in X
is grouped by the levels of x
and FUN
is applied.
The groups are then sorted by this value, and the resulting order is
used for the new factor level names.
If new.order
is a numeric vector, the new factor level names
are constructed by reordering the factor levels according to the
numeric values. If new.order
is a chraccter vector,
new.order
gives the list of new factor level names. In either
case levels omitted from new.order
will become missing
(NA
) values.
If sort
is provided (as it is by default): The new factor level
names are generated by calling the function specified by sort
to the existing factor level names. With sort=mixedsort
(the default) the factor levels are sorted so that combined numeric
and character strings are sorted in according to character rules on
the character sections (including ignoring case), and the numeric
rules for the numeric sections. See mixedsort
for details.
A new factor with reordered levels
Gregory R. Warnes [email protected]
# Create a 4 level example factor trt <- factor( sample( c("PLACEBO", "300 MG", "600 MG", "1200 MG"), 100, replace=TRUE ) ) summary(trt) # Note that the levels are not in a meaningful order. # Change the order to something useful.. # - default "mixedsort" ordering trt2 <- reorder(trt) summary(trt2) # - using indexes: trt3 <- reorder(trt, new.order=c(4, 2, 3, 1)) summary(trt3) # - using label names: trt4 <- reorder(trt, new.order=c("PLACEBO", "300 MG", "600 MG", "1200 MG")) summary(trt4) # - using frequency trt5 <- reorder(trt, X=rnorm(100), FUN=mean) summary(trt5) # Drop out the '300 MG' level trt6 <- reorder(trt, new.order=c("PLACEBO", "600 MG", "1200 MG")) summary(trt6)
# Create a 4 level example factor trt <- factor( sample( c("PLACEBO", "300 MG", "600 MG", "1200 MG"), 100, replace=TRUE ) ) summary(trt) # Note that the levels are not in a meaningful order. # Change the order to something useful.. # - default "mixedsort" ordering trt2 <- reorder(trt) summary(trt2) # - using indexes: trt3 <- reorder(trt, new.order=c(4, 2, 3, 1)) summary(trt3) # - using label names: trt4 <- reorder(trt, new.order=c("PLACEBO", "300 MG", "600 MG", "1200 MG")) summary(trt4) # - using frequency trt5 <- reorder(trt, X=rnorm(100), FUN=mean) summary(trt5) # Drop out the '300 MG' level trt6 <- reorder(trt, new.order=c("PLACEBO", "600 MG", "1200 MG")) summary(trt6)
These functions are undocumented. Some are internal and not intended for direct use. Others simply haven't been documented yet.
residplot(model, formula, ...)
residplot(model, formula, ...)
model |
Undocumented |
formula |
Undocumented |
... |
arguments to be passed to |
These functions are undocumented. Some are internal and not intended for direct use. Others simply haven't been documented yet.
Gregory R. Warnes [email protected]
Create a vector of n
colors that are perceptually equidistant
and in an order that is easy to interpret.
rich.colors(n, palette="temperature", alpha=1.0, rgb=FALSE, plot=FALSE)
rich.colors(n, palette="temperature", alpha=1.0, rgb=FALSE, plot=FALSE)
n |
number of colors to generate. |
palette |
palette to use: |
alpha |
alpha transparency, from 0 (fully transparent) to 1 (opaque). |
rgb |
if |
plot |
whether to plot a descriptive color diagram. |
A character vector of color codes.
Arni Magnusson.
m <- abs(matrix(1:120+rnorm(120), nrow=15, ncol=8)) opar <- par(bg="gray", mfrow=c(1,2)) matplot(m, type="l", lty=1, lwd=3, col=rich.colors(8)) matplot(m, type="l", lty=1, lwd=3, col=rich.colors(8,"blues")) par(opar) barplot(rep(1,100), col=rich.colors(100), space=0, border=0, axes=FALSE) barplot(rep(1,20), col=rich.colors(40)[11:30]) # choose subset plot(m, rev(m), ylim=c(120,0), pch=16, cex=2, col=rich.colors(200,"blues",alpha=0.6)[1:120]) # semitransparent rich.colors(100, plot=TRUE) # describe rgb recipe par(mfrow=c(2,2)) barplot(m, col=heat.colors(15), main="\nheat.colors") barplot(m, col=1:15, main="\ndefault palette") barplot(m, col=rich.colors(15), main="\nrich.colors") barplot(m, col=rainbow(15), main="\nrainbow") par(opar)
m <- abs(matrix(1:120+rnorm(120), nrow=15, ncol=8)) opar <- par(bg="gray", mfrow=c(1,2)) matplot(m, type="l", lty=1, lwd=3, col=rich.colors(8)) matplot(m, type="l", lty=1, lwd=3, col=rich.colors(8,"blues")) par(opar) barplot(rep(1,100), col=rich.colors(100), space=0, border=0, axes=FALSE) barplot(rep(1,20), col=rich.colors(40)[11:30]) # choose subset plot(m, rev(m), ylim=c(120,0), pch=16, cex=2, col=rich.colors(200,"blues",alpha=0.6)[1:120]) # semitransparent rich.colors(100, plot=TRUE) # describe rgb recipe par(mfrow=c(2,2)) barplot(m, col=heat.colors(15), main="\nheat.colors") barplot(m, col=1:15, main="\ndefault palette") barplot(m, col=rich.colors(15), main="\nrich.colors") barplot(m, col=rainbow(15), main="\nrainbow") par(opar)
rtPCR data for experiments investigating a variety of markers for characterizing teratogenicity.
data(rtPCR)
data(rtPCR)
A data frame with 1672 observations on the following 21 variables.
a factor with levels A0027002
through
A0054019
a factor with levels Compound A
through
Compound H
a factor with levels Non
Strong
Weak / Moderate
a factor with levels Sample 1
- Sample 152
a factor with levels Rep 1
- Rep 21
a factor with levels Ctrl
, Neg. Ctrl
P1
- P9
, No Vehicle Ctrl
, and Pos. Ctrl
a numeric vector
a factor with levels ProbeType 1
-
ProbeType 17
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
a numeric vector
TBA
Anonymized data.
data(rtPCR) # same scale overplot( RQ ~ Conc..ug.ml. | Test.Substance, data=rtPCR, subset=Detector=="ProbeType 7" & Conc..ug.ml. > 0, same.scale=TRUE, log="xy", f=3/4, main="Detector=ProbeType 7", 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 7" & Conc..ug.ml. > 0, log="xy", f=3/4, main="Detector=ProbeType 7", xlab="Concentration (ug/ml)", ylab="Relative Gene Quantification", min.y=0.01 )
data(rtPCR) # same scale overplot( RQ ~ Conc..ug.ml. | Test.Substance, data=rtPCR, subset=Detector=="ProbeType 7" & Conc..ug.ml. > 0, same.scale=TRUE, log="xy", f=3/4, main="Detector=ProbeType 7", 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 7" & Conc..ug.ml. > 0, log="xy", f=3/4, main="Detector=ProbeType 7", xlab="Concentration (ug/ml)", ylab="Relative Gene Quantification", min.y=0.01 )
Divert R's standard text output to a graphics device.
sinkplot(operation = c("start", "plot", "cancel"), ...)
sinkplot(operation = c("start", "plot", "cancel"), ...)
operation |
See below |
... |
Plot arguments. (Ignored unless |
This function allows the printed output of R commands to be captured and displayed on a graphics device.
The capture process is started by calling
sinkplot("start")
. Now R commands can be executed and all
printed output (except errors) will be captured. When the desired
text has been captured sinkplot("plot")
can be called to
actually display the output. sinkplot("cancel")
can be used to
abort the output capture without plotting.
The current implementation does not allow sinkplot
to be nested.
Invisibly returns a character vector containing one element for each line of the captured output.
Gregory R. Warnes [email protected]
Functionality requested by Kevin Wright [email protected] in the R-devel newlist posting https://stat.ethz.ch/pipermail/r-devel/2004-January/028483.html.
set.seed(12456) x <- factor(sample( LETTERS[1:5], 50, replace=TRUE)) y <- rnorm(50, mean=as.numeric(x), sd=1) ## construct a figure showing a box plot of the data, followed by an ## analysis of variance table for the data layout(cbind(1:2), heights=c(2,1)) boxplot(y~x, col="darkgreen") sinkplot() anova(lm(y~x)) sinkplot("plot",col="darkgreen")
set.seed(12456) x <- factor(sample( LETTERS[1:5], 50, replace=TRUE)) y <- rnorm(50, mean=as.numeric(x), sd=1) ## construct a figure showing a box plot of the data, followed by an ## analysis of variance table for the data layout(cbind(1:2), heights=c(2,1)) boxplot(y~x, col="darkgreen") sinkplot() anova(lm(y~x)) sinkplot("plot",col="darkgreen")
Space points in an x-y plot so they don't overlap.
space(x, y, s=1/50, na.rm=TRUE, direction="x")
space(x, y, s=1/50, na.rm=TRUE, direction="x")
x |
numeric vector of x coordonates. |
y |
numeric vector of x coordonates. |
s |
either a single numeric value or 2 element vector specifying the minimum distance between points in the x and y dimensions as a fraction of the x and y range. Defaults to 1/50. |
na.rm |
logical indicating whether pairs where one or both elements are missing should be removed. Defaults to TRUE. |
direction |
"x" or "y", indicating which direction points should be moved to accomplish spacine. |
In an x-y plot where at least one variable has discrete levels several points may be plotted at or very near the same coordonates. This makes it difficult to guage the number of points in a specific region. A common method of resolving this problem is to 'jitter' the points by adding random noise.
This function takes a different approach to the same problem.
When there are two or more points with the same (x,y) value (or within x+-s[1] and x+-s[2]), it spaces these out in the x direction so that the points are separated by at least distance s.
Another method for dealing with overploting is available in the
sunflowerplot
function.
list with two components
x |
(modified) x location for each input point |
y |
y location of each input point |
Gregory R. Warnes [email protected]
x <- rep(1:5, 10) y <- round(rnorm(length(x),x)) prepar <- par("mfrow") par(mfrow=c(1,3)) # standard x-y plot: noverlapping points are hidden plot(x,y) title("Standard Plot") # 'spaced' plot: overlapping points are spread out and visible plot(space(x,y)) title("Plot with 'space'") # 'spaced' plot: overlapping points are spread out along y and visible plot(space(x,y, direction='y')) title("Plot with 'space', direction='y' ") # 'sunflower' plot, another approach, overlapping points are # indicated via petals sunflowerplot(x,y) title("Sunflower Plot") par(mfrow=prepar)
x <- rep(1:5, 10) y <- round(rnorm(length(x),x)) prepar <- par("mfrow") par(mfrow=c(1,3)) # standard x-y plot: noverlapping points are hidden plot(x,y) title("Standard Plot") # 'spaced' plot: overlapping points are spread out and visible plot(space(x,y)) title("Plot with 'space'") # 'spaced' plot: overlapping points are spread out along y and visible plot(space(x,y, direction='y')) title("Plot with 'space', direction='y' ") # 'sunflower' plot, another approach, overlapping points are # indicated via petals sunflowerplot(x,y) title("Sunflower Plot") par(mfrow=prepar)
This function displays text output in a graphics window. It is the equivalent of 'print' except that the output is displayed as a plot.
textplot(object, halign="center", valign="center", cex, ...) ## Default S3 method: textplot(object, halign=c("center","left","right"), valign=c("center", "top", "bottom"), cex, ... ) ## S3 method for class 'character' textplot(object, halign = c("center", "left", "right"), valign = c("center", "top", "bottom"), cex, fixed.width=TRUE, cspace=1, lspace=1, mar=c(0, 0, 3, 0) + 0.1, tab.width = 8, ...) ## S3 method for class 'data.frame' textplot(object, halign = c("center", "left", "right"), valign = c("center", "top", "bottom"), cex, ...) ## S3 method for class 'matrix' textplot(object, halign = c("center", "left", "right"), valign = c("center", "top", "bottom"), cex, cmar = 2, rmar = 0.5, show.rownames = TRUE, show.colnames = TRUE, hadj = 1, vadj = 1, mar = c(1, 1, 4, 1) + 0.1, col.data = par("col"), col.rownames = par("col"), col.colnames = par("col"), ...)
textplot(object, halign="center", valign="center", cex, ...) ## Default S3 method: textplot(object, halign=c("center","left","right"), valign=c("center", "top", "bottom"), cex, ... ) ## S3 method for class 'character' textplot(object, halign = c("center", "left", "right"), valign = c("center", "top", "bottom"), cex, fixed.width=TRUE, cspace=1, lspace=1, mar=c(0, 0, 3, 0) + 0.1, tab.width = 8, ...) ## S3 method for class 'data.frame' textplot(object, halign = c("center", "left", "right"), valign = c("center", "top", "bottom"), cex, ...) ## S3 method for class 'matrix' textplot(object, halign = c("center", "left", "right"), valign = c("center", "top", "bottom"), cex, cmar = 2, rmar = 0.5, show.rownames = TRUE, show.colnames = TRUE, hadj = 1, vadj = 1, mar = c(1, 1, 4, 1) + 0.1, col.data = par("col"), col.rownames = par("col"), col.colnames = par("col"), ...)
object |
Object to be displayed. |
halign |
Alignment in the x direction, one of "center", "left", or "right". |
valign |
Alignment in the y direction, one of "center", "top" , or "bottom" |
cex |
Character size, see |
fixed.width |
Logical value indicating whether to emulate a fixed-width font by aligning characters in each row of text. This is usually necessary for text-formatted tables display properly. Defaults to 'TRUE'. |
cspace |
Space between characters as a
multiple of the width of the letter 'W'. This only applies when
|
lspace |
Line spacing. This only applies when
|
mar |
Figure margins, see the documentation for |
rmar , cmar
|
Space between rows or columns, in fractions of the size of the letter 'M'. |
show.rownames , show.colnames
|
Logical value indicating whether row or column names will be displayed. |
hadj , vadj
|
Vertical and horizontal location of elements within
matrix cells. These have the same meaning as the |
col.data |
Colors for data elements. If a single value is provided, all data elements will be the same color. If a matrix matching the dimensions of the data is provided, each data element will receive the specified color. |
col.rownames , col.colnames
|
Colors for row names and column names, respectively. Either may be specified as a scalar or a vector of appropriate length. |
tab.width |
Width of a single tab stop, in characters |
... |
Optional arguments passed to the text plotting command or specialied object methods |
A new plot is created and the object is displayed
using the largest font that will fit on in the plotting region. The
halign
and valign
parameters can be used to control the
location of the string within the plotting region.
For matrixes and vectors a specialized textplot function is available, which plots each of the cells individually, with column widths set according to the sizes of the column elements. If present, row and column labels will be displayed in a bold font.
The character scaling factor (cex
) used.
Gregory R. Warnes [email protected]
## Not run: ### simple examples # show R version information textplot(version) # show the alphabet as a single string textplot( paste(letters[1:26], collapse=" ") ) # show the alphabet as a matrix textplot( matrix(letters[1:26], ncol=2)) ### Make a nice 4 way display with two plots and two text summaries data(iris) par(mfrow=c(2,2)) plot( Sepal.Length ~ Species, data=iris, border="blue", col="cyan", main="Boxplot of Sepal Length by Species" ) plotmeans( Sepal.Length ~ Species, data=iris, barwidth=2, connect=FALSE, main="Means and 95% Confidence Intervals\nof Sepal Length by Species") info <- sapply( split(iris$Sepal.Length, iris$Species), function(x) round(c(Mean=mean(x), SD=sd(x), N=nrow(x)),2) ) textplot( info, valign="top" ) title("Sepal Length by Species") reg <- lm( Sepal.Length ~ Species, data=iris ) textplot( capture.output(summary(reg)), valign="top") title("Regression of Sepal Length by Species") par(mfrow=c(1,1)) ### Show how to control text color cols <- c("red", "green", "magenta", "forestgreen") mat <- cbind(name=cols, t(col2rgb(cols)), hex=col2hex(cols)) textplot(mat, col.data=matrix(cols, nrow=length(cols), byrow=FALSE, ncol=5), ) ### Show how to manually tune the character size data(iris) reg <- lm( Sepal.Length ~ Species, data=iris ) text <- capture.output(summary(reg)) # do the plot and capture the character size used textplot(text, valign="top") # see what size was used cex # now redo the plot at 80% size textplot( text, valign="top", cex=cex*0.80) ## End(Not run)
## Not run: ### simple examples # show R version information textplot(version) # show the alphabet as a single string textplot( paste(letters[1:26], collapse=" ") ) # show the alphabet as a matrix textplot( matrix(letters[1:26], ncol=2)) ### Make a nice 4 way display with two plots and two text summaries data(iris) par(mfrow=c(2,2)) plot( Sepal.Length ~ Species, data=iris, border="blue", col="cyan", main="Boxplot of Sepal Length by Species" ) plotmeans( Sepal.Length ~ Species, data=iris, barwidth=2, connect=FALSE, main="Means and 95% Confidence Intervals\nof Sepal Length by Species") info <- sapply( split(iris$Sepal.Length, iris$Species), function(x) round(c(Mean=mean(x), SD=sd(x), N=nrow(x)),2) ) textplot( info, valign="top" ) title("Sepal Length by Species") reg <- lm( Sepal.Length ~ Species, data=iris ) textplot( capture.output(summary(reg)), valign="top") title("Regression of Sepal Length by Species") par(mfrow=c(1,1)) ### Show how to control text color cols <- c("red", "green", "magenta", "forestgreen") mat <- cbind(name=cols, t(col2rgb(cols)), hex=col2hex(cols)) textplot(mat, col.data=matrix(cols, nrow=length(cols), byrow=FALSE, ncol=5), ) ### Show how to manually tune the character size data(iris) reg <- lm( Sepal.Length ~ Species, data=iris ) text <- capture.output(summary(reg)) # do the plot and capture the character size used textplot(text, valign="top") # see what size was used cex # now redo the plot at 80% size textplot( text, valign="top", cex=cex*0.80) ## End(Not run)
Plot a Venn diagrams for up to 5 sets
venn(data, universe=NA, small=0.7, showSetLogicLabel=FALSE, simplify=FALSE, show.plot=TRUE, intersections=TRUE, names, ...) ## S3 method for class 'venn' plot(x, y, ..., small=0.7, showSetLogicLabel=FALSE, simplify=FALSE)
venn(data, universe=NA, small=0.7, showSetLogicLabel=FALSE, simplify=FALSE, show.plot=TRUE, intersections=TRUE, names, ...) ## S3 method for class 'venn' plot(x, y, ..., small=0.7, showSetLogicLabel=FALSE, simplify=FALSE)
data , x
|
Either a list list containing vectors of names or indices of group intersections, or a data frame containing boolean indicators of group intersectionship (see below) |
universe |
Subset of valid name/index elements. Values ignore values
in codedata not in this list will be ignored. Use |
small |
Character scaling of the smallest group counts |
showSetLogicLabel |
Logical flag indicating whether the internal group label should be displayed |
simplify |
Logical flag indicating whether unobserved groups should be omitted. |
show.plot |
Logical flag indicating whether the plot should be displayed. If false, simply returns the group count matrix. |
intersections |
Logical flag indicating if the returned object should have the attribute "individuals.in.intersections" featuring for every set a list of individuals that are assigned to it. |
y |
Ignored |
... |
Optional graphical parameters. |
names |
Optional vector of group names. |
data
should be either a named list of vectors containing
character string names ("GeneAABBB", "GeneBBBCY", .., "GeneXXZZ") or
indexes of group intersections (1, 2, .., N), or a data frame containing
indicator variables (TRUE, FALSE, TRUE, ..) for group intersectionship.
Group names will be taken from the component list element or column
names.
Invisibly returns an object of class "venn", containing:
A matrix of all possible sets of groups, and the observed count of items belonging to each The fist column contains observed counts, subsequent columns contain 0-1 indicators of group intersectionship.
If intersections=TRUE
, the attribute intersections
will be a list of vectors containing the names of the elements
belonging to each subset.
Steffen Moeller (email was steffen\[email protected], may no longer be active), with cleanup and packaging by Gregory R. Warnes [email protected].
## ## Example using a list of item names belonging to the ## specified group. ## ## construct some fake gene names.. oneName <- function() paste(sample(LETTERS,5,replace=TRUE),collapse="") geneNames <- replicate(1000, oneName()) ## GroupA <- sample(geneNames, 400, replace=FALSE) GroupB <- sample(geneNames, 750, replace=FALSE) GroupC <- sample(geneNames, 250, replace=FALSE) GroupD <- sample(geneNames, 300, replace=FALSE) input <-list(GroupA,GroupB,GroupC,GroupD) input tmp <- venn(input) attr(tmp, "intersections") ## ## Example using a list of item indexes belonging to the ## specified group. ## GroupA.i <- which(geneNames %in% GroupA) GroupB.i <- which(geneNames %in% GroupB) GroupC.i <- which(geneNames %in% GroupC) GroupD.i <- which(geneNames %in% GroupD) input.i <-list(A=GroupA.i,B=GroupB.i,C=GroupC.i,D=GroupD.i) input.i venn(input.i) ## ## Example using a data frame of indicator ('f'lag) columns ## GroupA.f <- geneNames %in% GroupA GroupB.f <- geneNames %in% GroupB GroupC.f <- geneNames %in% GroupC GroupD.f <- geneNames %in% GroupD input.df <- data.frame(A=GroupA.f,B=GroupB.f,C=GroupC.f,D=GroupD.f) head(input.df) venn(input.df) ## smaller set to create empty groupings small <- input.df[1:20,] venn(small, simplify=FALSE) # with empty groupings venn(small, simplify=TRUE) # without empty groupings ## Capture group counts, but don't plot tmp <- venn(input, show.plot=FALSE) tmp ## Show internal binary group labels venn(input, showSetLogicLabel=TRUE) ## Limit universe tmp <- venn(input, universe=geneNames[1:100]) tmp ## ## Example to determine which elements are in A and B but not in ## C and D using the 'intersections' attribute. ## tmp <- venn(input, intersection=TRUE) isect <- attr(tmp, "intersection") # Look at all of the subsets str(isect) # Extract and combine the subsets of interest.. AandB <- unique(c(isect$A, isect$B, isect$'A:B')) # and look at the results str(AandB) ## ## The full set of elements of each intersection is provided in the ## "interesections" attribute. ## a<-venn(list(1:5,3:8), show.plot=FALSE) intersections<-attr(a,"intersections") print(intersections) # $A # [1] "1" "2" # # $B # [1] "6" "7" "8" # # $`A:B` # [1] "3" "4" "5"
## ## Example using a list of item names belonging to the ## specified group. ## ## construct some fake gene names.. oneName <- function() paste(sample(LETTERS,5,replace=TRUE),collapse="") geneNames <- replicate(1000, oneName()) ## GroupA <- sample(geneNames, 400, replace=FALSE) GroupB <- sample(geneNames, 750, replace=FALSE) GroupC <- sample(geneNames, 250, replace=FALSE) GroupD <- sample(geneNames, 300, replace=FALSE) input <-list(GroupA,GroupB,GroupC,GroupD) input tmp <- venn(input) attr(tmp, "intersections") ## ## Example using a list of item indexes belonging to the ## specified group. ## GroupA.i <- which(geneNames %in% GroupA) GroupB.i <- which(geneNames %in% GroupB) GroupC.i <- which(geneNames %in% GroupC) GroupD.i <- which(geneNames %in% GroupD) input.i <-list(A=GroupA.i,B=GroupB.i,C=GroupC.i,D=GroupD.i) input.i venn(input.i) ## ## Example using a data frame of indicator ('f'lag) columns ## GroupA.f <- geneNames %in% GroupA GroupB.f <- geneNames %in% GroupB GroupC.f <- geneNames %in% GroupC GroupD.f <- geneNames %in% GroupD input.df <- data.frame(A=GroupA.f,B=GroupB.f,C=GroupC.f,D=GroupD.f) head(input.df) venn(input.df) ## smaller set to create empty groupings small <- input.df[1:20,] venn(small, simplify=FALSE) # with empty groupings venn(small, simplify=TRUE) # without empty groupings ## Capture group counts, but don't plot tmp <- venn(input, show.plot=FALSE) tmp ## Show internal binary group labels venn(input, showSetLogicLabel=TRUE) ## Limit universe tmp <- venn(input, universe=geneNames[1:100]) tmp ## ## Example to determine which elements are in A and B but not in ## C and D using the 'intersections' attribute. ## tmp <- venn(input, intersection=TRUE) isect <- attr(tmp, "intersection") # Look at all of the subsets str(isect) # Extract and combine the subsets of interest.. AandB <- unique(c(isect$A, isect$B, isect$'A:B')) # and look at the results str(AandB) ## ## The full set of elements of each intersection is provided in the ## "interesections" attribute. ## a<-venn(list(1:5,3:8), show.plot=FALSE) intersections<-attr(a,"intersections") print(intersections) # $A # [1] "1" "2" # # $B # [1] "6" "7" "8" # # $`A:B` # [1] "3" "4" "5"
This function applies the specified function to the sets of y values
that are defined by overlapping "windows" in the x-dimension. For
example, setting fun=mean
returns local means, while setting
fun=function(x) sqrt(var(x))
returns local estimates of
the standard deviation.
wapply(x, y, fun=mean, method="range", width, n=50, drop.na=TRUE, pts, ...)
wapply(x, y, fun=mean, method="range", width, n=50, drop.na=TRUE, pts, ...)
x |
vector of x values for (x,y) pairs |
y |
vector of y values for (x,y) pairs |
fun |
function to be applied |
method |
method of defining an x-neighborhood. One of "width","nobs","range", or "fraction". See details. |
width |
width of an x-neighborhood. See details. |
n |
Number of equally spaced points at which to compute local estimates. See details. |
drop.na |
should points which result in missing values |
pts |
|
... |
arguments to be passed to |
Two basic techniques are available for determining what points fall
within the same x-neighborhood. The first technique uses a window with
a fixed width in the x-dimension and is is selected by
setting method="width"
or method="range"
. For
method="width"
the width
argument is an absolute
distance in the x-dimension. For method="range"
, the width is
expressed as a fraction of the x-range. In both cases, pts
specifies the points at which evaluation of fun
occurs. When
pts
is omitted, n
x values equally spaced along the x
range are used.
The second technique uses windows containing k neighboring points. The
(x,y) pairs are sorted by the x-values and the nearest k/2 points with
higher x values and the k/2 nearest points with lower x values are
included in the window. When method="nobs"
, k equals
width
(actually 2*floor(width
/2) ). When
method="fraction"
, width
specifies what fraction of the
total number of points should be included. The actual number of points
included in each window will be floor(n*frac/2)*2. Regardless of the
value of pts
, the function fun
will be evaluated at all
x locations.
Returns a list with components
x |
x location' |
y |
Result of applying |
Gregory R. Warnes [email protected]
#show local mean and inner 2-sd interval to help diagnose changing mean #or variance structure x <- 1:1000 y <- rnorm(1000, mean=1, sd=1 + x/1000 ) plot(x,y) lines(wapply(x,y,mean),col="red") CL <- function(x,sd) mean(x)+sd*sqrt(var(x)) lines(wapply(x,y,CL,sd= 1),col="blue") lines(wapply(x,y,CL,sd=-1),col="blue") lines(wapply(x,y,CL,sd= 2),col="green") lines(wapply(x,y,CL,sd=-2),col="green") #show local mean and inner 2-sd interval to help diagnose changing mean #or variance structure x <- 1:1000 y <- rnorm(1000, mean=x/1000, sd=1) plot(x,y) lines(wapply(x,y,mean),col="red") CL <- function(x,sd) mean(x)+sd*sqrt(var(x)) lines(wapply(x,y,CL,sd= 1,method="fraction",width=1/20),col="blue") lines(wapply(x,y,CL,sd=-1,method="fraction",width=1/20),col="blue") lines(wapply(x,y,CL,sd= 2,method="nobs",width=250),col="green") lines(wapply(x,y,CL,sd=-2,method="nobs",width=250),col="green")
#show local mean and inner 2-sd interval to help diagnose changing mean #or variance structure x <- 1:1000 y <- rnorm(1000, mean=1, sd=1 + x/1000 ) plot(x,y) lines(wapply(x,y,mean),col="red") CL <- function(x,sd) mean(x)+sd*sqrt(var(x)) lines(wapply(x,y,CL,sd= 1),col="blue") lines(wapply(x,y,CL,sd=-1),col="blue") lines(wapply(x,y,CL,sd= 2),col="green") lines(wapply(x,y,CL,sd=-2),col="green") #show local mean and inner 2-sd interval to help diagnose changing mean #or variance structure x <- 1:1000 y <- rnorm(1000, mean=x/1000, sd=1) plot(x,y) lines(wapply(x,y,mean),col="red") CL <- function(x,sd) mean(x)+sd*sqrt(var(x)) lines(wapply(x,y,CL,sd= 1,method="fraction",width=1/20),col="blue") lines(wapply(x,y,CL,sd=-1,method="fraction",width=1/20),col="blue") lines(wapply(x,y,CL,sd= 2,method="nobs",width=250),col="green") lines(wapply(x,y,CL,sd=-2,method="nobs",width=250),col="green")