*! version 1.0.0 10dec2013

Title

smvcir -- Sliced mean variance-covariance inverse regression

Syntax

Estimate spanning set of difference space and its dimension

smvcir depvar indepvars [if] [in]
[,smvcir_options]

Plot difference dimensions

smvcir plot [,
smvcir_plot_options]

Produce standardized coefficients

smvcir std [, dimensions(numlist)]

smvcir_options

Reporting
notest omit dimensionality test

noscree omit scree plot

level(#) set confidence level; default is
level(95)

disclevel(#) set discrimination strength level; default is
disclevel(100).

smvcir_plot_options

Main
dimensions(numlist) set difference dimensions to plot

groups(numlist) set groups to plot

Scatter
plot#opts(marker_options) affect the rendition of group #s scatter points

Fit line
line#opts(cline_options) affect the rendition of the linear regression lines for group #

Overall plot options
ycommon give y axes common scales

xcommon give x axes common scales

title_options title to appear on combined graph

region_options outlining, shading, aspect ratio

commonscheme put graphs on common scheme

scheme(schemename) overall look

name(name, ...) specify name for
combined graph

saving(filename, ...) save combined
graph in file

Description

smvcir performs the sliced mean variance–covariance inverse regression
(SMVCIR) algorithm on grouped multivariate data. The data is transformed to a
new coordinate system where the group mean, variance, and covariance
differences are more apparent. A test for the dimension of this new coordinate space is also provided.

See Lindsey et. al (2013) for details on the SMVCIR algorithm.

Options for smvcir

Reporting

notest omits the dimensionality test of the SMVCIR space.

noscree omits the scree plot of the singular values of the SMVCIR
spanning set.

level(#); see [R] estimation options.

disclevel(#) sets the discrimination strength level, a measure of
practical significance. This is an acceptable percentage of the total singular
value sum that is represented by the singular values of the chosen number of
dimensions. The default is disclevel(100).

Options for smvcir plot

Main

dimensions(numlist) specifies which difference dimensions to plot.

group(numlist) specifies which groups to plot.

Scatter

plot#opts(marker_options) affects the rendition of the scatter points for group #, including the size and color of markers.

Fit line

line#opts(cline_options) affects the rendition of the linear regression lines for group #.

Overall plot options

ycommon
and
xcommon
specify that the individual graphs previously drawn by graph
twoway, and for which the by() option was not specified,
be put on common y or x axes scales. See
Combining twoway graphs under
Remarks below.

These options have no effect when applied to the categorical axes of
bar, box, and dot graphs. Also, when twoway graphs
are combined with bar, box, and dot graphs, the options
affect only those graphs of the same type as the first graph combined.

title_options
allow you to specify titles, subtitles, notes, and captions
to be placed on the combined graph; see [G-3] title_options.

region_options
allow you to control the aspect ratio, size, etc., of the combined graph;
see [G-3] region_options. Important among these options are
ysize(#) and xsize(#), which specify the
overall size of the resulting graph. It is sometimes desirable to make
the combined graph wider or longer than usual.

commonscheme and scheme(schemename)
are for use when combining graphs that use different schemes. By default,
each subgraph will be drawn according to its own scheme.

commonscheme specifies that all subgraphs be drawn using the same
scheme and, by default, that scheme will be your default scheme.

scheme(schemename) specifies that the
schemename be used instead; see [G-3] scheme_option.

name(name[, replace])
specifies the name of the resulting combined graph.
name(Graph, replace) is the default.
See [G-3] name_option.

saving(filename[, asis replace])
specifies that the combined graph be saved as filename. If
filename is specified without an extension, .gph is assumed.
asis specifies that the graph be saved in as-is format.
replace specifies that, if the file already exists, it is okay to
replace it. See [G-3] saving_option.

Examples

Setup

. use wine

Estimate SMVCIR spanning set

. smvcir cultivar alcohol-proline, disclevel(70)

Plot SMVCIR dimensions

. smvcir plot

Show standardized coefficients

. smvcir std

Stored results

Scalars

e(N) number of observations

e(k) number of predictors

e(g) number of groups

e(d_test) statistically significant dimension

e(d_prac) practically significant dimension

e(d) min(d_test,d_prac)

e(level) statistical significance level

e(disclevel) practical significance level

Macros

e(cmd) smvcir

e(cmdline) command as typed

e(title) title in estimation output

e(depvar) dependent variable

e(predictors independent vairables

Matrices

e(Spanset) SMVCIR spanset

e(Spanset_Vt) right singular vectors of SMVCIR spanset

e(Spanset_U) left singular vectors of SMVCIR spanset

e(Sv) singular values of SMVCIR spanset

e(Spanset2) SMVCIR kernel

e(Spanset2_eigvals) eigenvalues of SMVCIR kernel

e(Spanset2_eigvecs) eigenvectors of SMVCIR kernel

Reference

Lindsey, C. L. and S. J. Sheather, and J. W. McKean. 2013. Using sliced mean variance–covariance inverse regression for classification and dimension reduction. Computational Statistics. http://dx.doi.org/10.1007/s00180-013-0460-3