HISTOGRAM2 Plots a bivariate histogram.
HISTOGRAM2(X,Y) plots a bivariate histogram of X and Y. X and Y can be
arrays of any shape, but they must have the same size. HISTOGRAM2
determines the bin edges using an automatic binning algorithm that
returns uniform bins of an area chosen to cover the range of values in
X and Y and reveal the shape of the underlying distribution.
HISTOGRAM2(X,Y,NBINS), where NBINS is a scalar or 2-element vector,
specifies the number of bins to use. A scalar specifies the same number
of bins in each dimension, whereas the 2-element vector [nbinsx nbinsy]
specifies a different number of bins for the X and Y dimensions.
HISTOGRAM2(X,Y,XEDGES,YEDGES), where XEDGES and YEDGES are vectors,
specifies the edges of the bins.
The value [X(k),Y(k)] is in the (i,j)th bin if XEDGES(i) <= X(k) <
XEDGES(i+1) and YEDGES(j) <= Y(k) < YEDGES(j+1). The last bins in the
X and Y dimensions will also include the upper edge. For example,
[X(k),Y(k)] will fall into the i-th bin in the last row if
XEDGES(end-1) <= X(k) <= XEDGES(end) && YEDGES(i) <= Y(k) < YEDGES(i+1).
HISTOGRAM2(...,'BinWidth',BW) where BW is a scalar or 2-element vector,
uses bins of size BW. A scalar specifies the same bin width for each
dimension, whereas the 2-element vector [bwx bwy] specifies different
bin widths for the X and Y dimensions. To prevent from accidentally
creating too many bins, a limit of 1024 bins can be created along each
dimension when specifying 'BinWidth'. If BW is too small such that more
than 1024 bins are needed in either dimension, HISTOGRAM2 uses larger
bins instead.
HISTOGRAM2(...,'XBinLimits',[XBMIN,XBMAX]) plots a histogram
with only elements between the bin limits inclusive along the X axis,
X>=BMINX & X<=BMAXX. Similarly,
HISTOGRAM2(...,'YBinLimits',[YBMIN,YBMAX]) uses only elements between
the bin limits inclusive along the Y axis, Y>=YBMIN & Y<=YBMAX.
HISTOGRAM2(...,'Normalization',NM) specifies the normalization scheme
of the histogram values. The normalization scheme affects the scaling
of the histogram along the Z axis. NM can be:
'count' The height of each bar is the number of
observations in each bin. The sum of the
bar heights is generally equal to NUMEL(X)
and NUMEL(Y), but is less than if some
of the input data is not included in the bins..
'probability' The height of each bar is the relative
number of observations (number of observations
in bin / total number of observations), and
the sum of the bar heights is less than or equal
to 1.
'countdensity' The height of each bar is, (the number of
observations in each bin) / (area of bin). The
volume (height * area) of each bar is the number
of observations in the bin, and the sum of
the bar volumes is less than or equal to NUMEL(X)
and NUMEL(Y).
'pdf' Probability density function estimate. The height
of each bar is, (number of observations in bin)
/ (total number of observations * area of bin).
The volume of each bar is the relative number of
observations, and the sum of the bar volumes
is less than or equal to 1.
'cumcount' The height of each bar is the cumulative
number of observations in each bin and all
previous bins in both the X and Y dimensions.
The height of the last bar is less than or equal
to NUMEL(X) and NUMEL(Y).
'cdf' Cumulative density function estimate. The height
of each bar is the cumulative relative number
of observations in each bin and all previous
bins in both the X and Y dimensions. The height
of the last bar is less than or equal to 1.
HISTOGRAM2(...,'DisplayStyle',STYLE) specifies the display style of the
histogram. STYLE can be:
'bar3' Display histogram using 3-D bars. This is the
default.
'tile' Display histogram as a rectangular array of
tiles with colors indicating the bin values.
HISTOGRAM2(...,'BinMethod',BM), uses the specified automatic binning
algorithm to determine the number and width of the bins. BM can be:
'auto' The default 'auto' algorithm chooses a bin
size to cover the data range and reveal the
shape of the underlying distribution.
'scott' Scott's rule is optimal if X and Y are close
to being jointly normally distributed, but
is also appropriate for most other
distributions. It uses a bin size of
[3.5*STD(X(:))*NUMEL(X)^(-1/4)
3.5*STD(Y(:))*NUMEL(Y)^(-1/4)]
'fd' The Freedman-Diaconis rule is less sensitive to
outliers in the data, and may be more suitable
for data with heavy-tailed distributions. It
uses a bin size of [2*IQR(X(:))*NUMEL(X)^(-1/4)
2*IQR(Y(:))*NUMEL(Y)^(-1/4)] where IQR is the
interquartile range.
'integers' The integer rule is useful with integer data,
as it creates a bin for each pair of integer
X and Y. It uses a bin width of 1 along each
dimension and places bin edges halfway
between integers. To prevent from accidentally
creating too many bins, a limit of 1024 bins
can be created along each dimension with this
rule. If the data range along either dimension
is greater than 1024, then larger bins are
used instead.
HISTOGRAM2(...,NAME,VALUE) set the property NAME to VALUE.
HISTOGRAM2('XBinEdges', XEDGES, 'YBinEdges', YEDGES, 'BinCounts', COUNTS)
where COUNTS is a matrix of size [length(XEDGES)-1, length(YEDGES)-1],
manually specifies the bin counts. HISTOGRAM2 plots the counts and does
not do any data binning.
HISTOGRAM2(AX,...) plots into AX instead of the current axes.
H = HISTOGRAM2(...) also returns a Histogram2 object. Use this to
inspect and adjust the properties of the histogram.
Class support for inputs X, Y, XEDGES, YEDGES:
float: double, single
integers: uint8, int8, uint16, int16, uint32, int32, uint64, int64
logical
See also HISTCOUNTS2, HISTCOUNTS, HISTOGRAM, DISCRETIZE,
matlab.graphics.chart.primitive.Histogram2
Documentation for histogram2
doc histogram2
Other functions named histogram2
tall/histogram2
