Plot fdt.default and fdt.multiple objects
S3 methods for fdt.default
and fdt.multiple
objects.
It is possible to plot histograms and polygons (absolute, relative
and cumulative).
## S3 methods ## S3 method for class 'fdt.default' plot(x, type=c('fh', 'fp', 'rfh', 'rfp', 'rfph', 'rfpp', 'd', 'cdh', 'cdp', 'cfh', 'cfp', 'cfph', 'cfpp'), v=FALSE, v.round=2, v.pos=3, xlab="Class limits", xlas=0, ylab=NULL, col="gray", xlim=NULL, ylim=NULL, main=NULL, x.round=2, ...) ## S3 method for class 'fdt.multiple' plot(x, type=c('fh', 'fp', 'rfh', 'rfp', 'rfph', 'rfpp', 'd', 'cdh', 'cdp', 'cfh', 'cfp', 'cfph', 'cfpp'), v=FALSE, v.round=2, v.pos=3, xlab="Class limits", xlas=0, ylab=NULL, col="gray", xlim=NULL, ylim=NULL, main=NULL, main.vars=TRUE, x.round=2, grouped=FALSE, args.legend=NULL, ...) ## S3 method for class 'fdt_cat.default' plot(x, type=c('fb', 'fp', 'fd', 'rfb', 'rfp', 'rfd', 'rfpb', 'rfpp', 'rfpd', 'cfb', 'cfp', 'cfd', 'cfpb', 'cfpp', 'cfpd', 'pa'), v=FALSE, v.round=2, v.pos=3, xlab=NULL, xlas=0, ylab=NULL, y2lab=NULL, y2cfp=seq(0, 100, 25), col=gray(.4), xlim=NULL, ylim=NULL, main=NULL, box=FALSE, ...) ## S3 method for class 'fdt_cat.multiple' plot(x, type=c('fb', 'fp', 'fd', 'rfb', 'rfp', 'rfd', 'rfpb', 'rfpp', 'rfpd', 'cfb', 'cfp', 'cfd', 'cfpb', 'cfpp', 'cfpd', 'pa'), v=FALSE, v.round=2, v.pos=3, xlab=NULL, xlas=0, ylab=NULL, y2lab=NULL, y2cfp=seq(0, 100, 25), col=gray(.4), xlim=NULL, ylim=NULL, main=NULL, main.vars=TRUE, box=FALSE, ...)
x |
A fdt object. |
type |
The type of the plot: rfb: Relative frequency barplot, rfpb: Relative frequency (%) barplot, d: Density, cfb: Cumulative frequency barplot, cdpb: Cumulative frequency (%) barplot, pa: Pareto chart. |
v |
Logical flag: should the values be added to the plot? |
v.round |
If |
v.pos |
If |
xlab |
A label for the x axis. |
xlas |
An integer which controls the orientation of the x axis labels: |
ylab |
A label for the y axis. |
y2lab |
A label for the y2 axis. |
y2cfp |
A cumulative percent frequency for the y2 axis. The default is |
col |
A |
xlim |
The x limits of the plot. |
ylim |
The y limits of the plot. |
main |
Title of the plot(s). This option has priority over main.vars, i.e, if any value is informed,
the variable names will not be used as title of the plot(s). For |
main.vars |
Logical flag: should the variables names be added as title of each plot (default |
x.round |
A numeric value to round the x ticks:
0: parallel to the axes, |
box |
If |
grouped |
If |
args.legend |
List of additional arguments to be passed to |
... |
Optional plotting parameters. |
The result is a single histogram or polygon (absolute, relative or cumulative)
for fdt.default
or a set of histograms or polygon (absolute, relative or
cumulative) for fdt.multiple
objects.
Both default and multiple try to compute the maximum number of histograms
that will fit on one page, then it draws a matrix of histograms. More than one
graphical device may be opened to show all histograms.
The result is a single barplot, polygon, dotchar (absolute, relative or cumulative)
and Pareto chart for fdt_cat.default
or a set of the same graphs for
fdt_cat.multiple
objects.
Both default and multiple try to compute the maximum number of histograms
that will fit on one page, then it draws a matrix of graphs lited above. More than one
graphical device may be opened to show all graphs.
José Cláudio Faria
Enio G. Jelihovschi
Ivan B. Allaman
library(fdth) #================================ # Vectors: univariated numerical #================================ x <- rnorm(n=1e3, mean=5, sd=1) (d <- fdt(x)) # Histograms plot(d) # Absolute frequency histogram plot(d, main='My title') plot(d, x.round=3, col='darkgreen') plot(d, xlas=2) plot(d, x.round=3, xlas=2, xlab=NULL) plot(d, v=TRUE, cex=.8, x.round=3, xlas=2, xlab=NULL, col=rainbow(11)) plot(d, type='fh') # Absolute frequency histogram plot(d, type='rfh') # Relative frequency histogram plot(d, type='rfph') # Relative frequency (%) histogram plot(d, type='cdh') # Cumulative density histogram plot(d, type='cfh') # Cumulative frequency histogram plot(d, type='cfph') # Cumulative frequency (%) histogram # Poligons plot(d, type='fp') # Absolute frequency polygon plot(d, type='rfp') # Relative frequency polygon plot(d, type='rfpp') # Relative frequency (%) polygon plot(d, type='cdp') # Cumulative density polygon plot(d, type='cfp') # Cumulative frequency polygon plot(d, type='cfpp') # Cumulative frequency (%) polygon # Density plot(d, type='d') # Density # Theoretical curve and fdt x <- rnorm(1e5, mean=5, sd=1) plot(fdt(x, k=100), type='d', col=heat.colors(100)) curve(dnorm(x, mean=5, sd=1), col='darkgreen', add=TRUE, lwd=2) #================================== # Vectors: univariated categorical #================================== x <- sample(letters[1:5], 1e3, rep=TRUE) (dc <- fdt_cat(x)) # Barplot: the default plot(dc) # Barplot plot(dc, type='fb') # Polygon plot(dc, type='fp') # Dotchart plot(dc, type='fd') # Pareto chart plot(dc, type='pa') #============================================= # Data.frames: multivariated with categorical #============================================= mdf <- data.frame(X1=rep(LETTERS[1:4], 25), X2=as.factor(rep(1:10, 10)), Y1=c(NA, NA, rnorm(96, 10, 1), NA, NA), Y2=rnorm(100, 60, 4), Y3=rnorm(100, 50, 4), Y4=rnorm(100, 40, 4), stringsAsFactors=TRUE) # Histograms (d <- fdt(mdf)) plot(d, v=TRUE, cex=.8) plot(d, col='darkgreen', ylim=c(0, 40)) plot(d, col=rainbow(8), ylim=c(0, 40), main=LETTERS[1:4]) plot(d, type='fh') plot(d, type='rfh') plot(d, type='rfph') plot(d, type='cdh') plot(d, type='cfh') plot(d, type='cfph') # Poligons plot(d, v=TRUE, type='fp') plot(d, type='rfp') plot(d, type='rfpp') plot(d, type='cdp') plot(d, type='cfp') plot(d, type='cfpp') # Density plot(d, type='d') levels(mdf$X1) plot(fdt(mdf, k=5, by='X1'), ylim=c(0, 12)) levels(mdf$X2) plot(fdt(mdf, breaks='FD', by='X2')) plot(fdt(mdf, k=5, by='X2')) # It is difficult to compare plot(fdt(mdf, k=5, by='X2'), ylim=c(0, 8)) # Easy plot(fdt(iris, k=5)) plot(fdt(iris, k=5), col=rainbow(5)) plot(fdt(iris, k=5, by='Species'), v=TRUE) d <- fdt(iris, k=10) plot(d) plot(d, type='d') # Categorical data (dc <- fdt_cat(mdf)) plot(dc) plot(dc, type='fd', pch=19) #========================= # Matrices: multivariated #========================= plot(fdt(state.x77)) plot(fdt(volcano))
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