h = andrewsplot(X,...)
andrewsplot(X) creates an Andrews plot
of the multivariate data in the matrix
X. The rows
X correspond to observations, the columns to
variables. Andrews plots represent each observation by a function f(t)
of a continuous dummy variable t over the interval
[0,1]. f(t) is defined for the i th
X as missing values and ignores the corresponding
an Andrews plot where
standopt is one of
'on' — scales each column
X to have mean
0 and standard
1 before making the plot.
'PCA' — creates an Andrews
plot from the principal component scores of
in order of decreasing eigenvalue. (See
'PCAStd' — creates an Andrews
plot using the standardized principal component scores. (See
only the median and the
alpha and (1 –
quantiles of f(t) at each value
of t. This is useful if
the data in different groups with different colors. Groups are defined
group, a numeric array containing a group index
for each observation.
group can also be a categorical
array, character matrix, or cell array of character vectors containing
a group name for each observation.
optional lineseries object properties to the specified values for
all lineseries objects created by
(See Line Properties.)
h = andrewsplot(X,...) returns a column
vector of handles to the lineseries objects created by
one handle per row of
X. If you use the
h contains one handle for each of the
three lineseries objects created. If you use both the
'Group' input parameters,
three handles for each group.
This example shows how to create an Andrews plot to visualize grouped sample data.
Load the sample data.
Create an Andrews plot, grouping the sample data by
Create a second, simplified Andrews plot that only displays the median and quartiles of each group.