Stata 10 includes many new methods of multivariate analysis, and
many existing methods have been greatly expanded.
Stata now performs several discriminant analysis techniques,
including linear, quadratic, logistic, and
Postestimation tools make obtaining classification tables, error rates,
and summary statistics a snap.
Stata now performs modern as well as classical multidimensional
scaling (MDS), including metric and nonmetric MDS. Available loss
functions include stress, normalized stress, squared stress,
normalized squared stress, and Sammon. Available transformations
include identity, power, and monotonic.
Stata can now perform multiple or joint correspondence analysis,
allowing you to explore the relationship among categorical
variables by projecting onto reduced spaces that may correspond to
Here are all the details...
New estimation commands discrim and
candisc provide several discriminant analysis
techniques, including linear discriminant analysis (LDA), quadratic
discriminant analysis (QDA), logistic discriminant analysis, and
kth-nearest-neighbor discriminant analysis. See [MV]
Existing estimation commands
mds, mdslong, and
mdsmat now provide modern as well as classical
multidimensional scaling (MDS), including metric and nonmetric MDS.
Available loss functions include stress, normalized stress, squared
stress, normalized squared stress, and Sammon. Available transformations
include identity, power, and monotonic. mdslong
also now allows aweights and
fweights, and mdsmat has a
weight() option. See [MV]
mdslong, and [MV]
New estimation command mca provides multiple
correspondence analysis (MCA) and joint correspondence analysis (JCA); see
[MV] mca and
mca postestimation. You can use existing command
screeplot afterward to graph principal inertias; see [MV]
Concerning existing estimation command ca
See [MV] ca
ca now allows crossed (stacked) variables.
This provides a way to automatically combine two or more categorical
variables into one crossed variable and perform correspondence
analysis with it.
ca’s existing option
normalize() now allows
normalize(standard) to provide normalization of the coordinates
by singular vectors divided by the square root of the mass.
ca’s new option
length() allows you to customize the length of labels with
crossed variables in output.
New postestimation command
estat loadings, used after ca and
camat, displays correlations of profiles and
Existing postestimation command cabiplot has
new option origin that displays the origin
within the plot. cabiplot also now accepts
to customize the appearance of the origin on the graph.
Existing postestimation commands
now allow row and column marker labels to be specified using the
mlabel() suboption of rowopts() and
mds now allow
the Gower measure for a mix of binary and continuous data; see [MV]
Existing command biplot has new options.
dim() specifies the dimensions to be displayed.
negcol specifies that negative column (variable)
arrows be plotted.
provides graph options for the negative column arrows.
norow and nocolumn suppress
the row points or column arrows. See [MV]
New postestimation command estat rotate after
canon performs orthogonal varimax rotation of
the raw coefficients, standard coefficients, or canonical loadings. After
estat rotate, new postestimation command
estat rotatecompare displays the rotated and
unrotated coefficients or loadings and the most recently rotated
coefficients or loadings. See [MV]
now allow singular correlation or covariance structures. New option
forcepsd modifies a matrix to be positive
semidefinite and thus to be a proper covariance matrix. See [MV]
pca and [MV]
now refer to the “Kaiser normalization” rather than
“Horst normalization”. A search of the literature indicates
that Kaiser normalization is the preferred terminology. Previously option
horst was a synonym for
normalize. Now option horst is not
documented. See [MV]
rotate and [MV]
Existing command procrustes now saves the number
of y variables in scalar e(ny); see [MV]
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