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hist computes a histogram of the given
data values. If plot = TRUE, the resulting object of
class "histogram" is plotted by
plot.histogram, before it is returned.hist(x, ...)## S3 method for class 'default':
hist(x, breaks = "Sturges",
freq = NULL, probability = !freq,
include.lowest = TRUE, right = TRUE,
density = NULL, angle = 45, col = NULL, border = NULL,
main = paste("Histogram of" , xname),
xlim = range(breaks), ylim = NULL,
xlab = xname, ylab,
axes = TRUE, plot = TRUE, labels = FALSE,
nclass = NULL, warn.unused = TRUE, ...)
pretty values. If
breaks is a function, the x vector is supplied to it
as the only argument.TRUE, the histogram graphic is a
representation of frequencies, the counts component of
the result; if FALSE, probability densities, component
density, are plotted (so that the histogram has a total area
of one). Defaults to TRUE if and only if breaks are
equidistant (and probability is not specified).!freq, for S compatibility.TRUE, an x[i] equal to
the breaks value will be included in the first (or last, for
right = FALSE) bar. This will be ignored (with a warning)
unless breaks is a vector.TRUE, the histogram cells are
right-closed (left open) intervals.NULL means that no shading lines
are drawn. Non-positive values of density also inhibit the
drawing of shading lines.NULL yields unfilled bars.title have useful
defaults here.xlim is not used to define the histogram (breaks),
but only for plotting (when plot = TRUE).TRUE (default), axes are draw if the
plot is drawn.TRUE (default), a histogram is
plotted, otherwise a list of breaks and counts is returned. In the
latter case, a warning is used if (typically graphical) arguments
are specified that only apply to the plot = TRUE case.FALSE; see plot.histogram.nclass is equivalent to breaks for a scalar or
character argument.plot = FALSE and
warn.unused = TRUE, a warning will be issued when graphical
parameters are passed to hist.default().plot.histogram and thence to title and
axis (if plot = TRUE)."histogram" which is a list with components:breaks if that
was a vector). These are the nominal breaks, not with the boundary fuzz.x[] inside.all(diff(breaks) == 1), they are the
relative frequencies counts/n and in general satisfy
$\sum_i \hat f(x_i) (b_{i+1}-b_i) = 1$, where $b_i$ = breaks[i].x argument name.breaks are all the same.breaks. Thus the height of a rectangle is proportional to
the number of points falling into the cell, as is the area
provided the breaks are equally-spaced.The default with non-equi-spaced breaks is to give a plot of area one, in which the area of the rectangles is the fraction of the data points falling in the cells.
If right = TRUE (default), the histogram cells are intervals
of the form (a, b], i.e., they include their right-hand endpoint,
but not their left one, with the exception of the first cell when
include.lowest is TRUE.
For right = FALSE, the intervals are of the form [a, b),
and include.lowest means
A numerical tolerance of $10^{-7}$ times the median bin size
(for more than four bins, otherwise the median is substituted) is
applied when counting entries on the edges of bins. This is not
included in the reported breaks nor in the calculation of
density.
The default for breaks is "Sturges": see
nclass.Sturges. Other names for which algorithms
are supplied are "Scott" and "FD" /
"Freedman-Diaconis" (with corresponding functions
nclass.scott and nclass.FD).
Case is ignored and partial matching is used.
Alternatively, a function can be supplied which
will compute the intended number of breaks or the actual breakpoints
as a function of x.
Venables, W. N. and Ripley. B. D. (2002) Modern Applied Statistics with S. Springer.
nclass.Sturges, stem,
density, truehist in package
Typical plots with vertical bars are not histograms. Consider
barplot or plot(*, type = "h")
for such bar plots.
op <- par(mfrow = c(2, 2))
hist(islands)
utils::str(hist(islands, col = "gray", labels = TRUE))
hist(sqrt(islands), breaks = 12, col = "lightblue", border = "pink")
##-- For non-equidistant breaks, counts should NOT be graphed unscaled:
r <- hist(sqrt(islands), breaks = c(4*0:5, 10*3:5, 70, 100, 140),
col = "blue1")
text(r$mids, r$density, r$counts, adj = c(.5, -.5), col = "blue3")
sapply(r[2:3], sum)
sum(r$density * diff(r$breaks)) # == 1
lines(r, lty = 3, border = "purple") # -> lines.histogram(*)
par(op)
require(utils) # for str
str(hist(islands, breaks = 12, plot = FALSE)) #-> 10 (~= 12) breaks
str(hist(islands, breaks = c(12,20,36,80,200,1000,17000), plot = FALSE))
hist(islands, breaks = c(12,20,36,80,200,1000,17000), freq = TRUE,
main = "WRONG histogram") # and warning
require(stats)
set.seed(14)
x <- rchisq(100, df = 4)
op <- par(mfrow = 2:1, mgp = c(1.5, 0.6, 0), mar = .1 + c(3,3:1))
## Comparing data with a model distribution should be done with qqplot()!
qqplot(x, qchisq(ppoints(x), df = 4)); abline(0, 1, col = 2, lty = 2)
## if you really insist on using hist() ... :
hist(x, freq = FALSE, ylim = c(0, 0.2))
curve(dchisq(x, df = 4), col = 2, lty = 2, lwd = 2, add = TRUE)
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