canplot | Canonical discriminant structure plot | canplot |

York University

Other designs may be handled either by (a) coding factor combinations 'interactively', so, e.g., the combinations of A*B are represented by a GROUP variable, or (b) by applying the method to adjusted response vectors (residuals) with some other predictor (class or continuous) partialled out. The latter method is equivalent to analysis of the residuals from an initial PROC GLM step, with the effects to be controlled or adjusted for as predictors.

e.g., to examine Treatment, controlling for Block and Sex,

proc glm data=..; model Y1-Y5 = block sex; output out=resids r=E1-E5; %canplot(data=resids, var=E1-E5, class=Treat, ... );

The interpretation of the angles betweeen variable vectors relies on the units for the horizontal and vertical axes being made equal (so that 1 data unit measures the same length on both axes. The axes should be equated either by using the GOPTIONS HSIZE= VSIZE= options, or using the macro HAXIS= and VAXIS= parameters and AXIS statements which specify the LENGTH= value for both axes.

The current version now uses the equate macro if the HAXIS= and VAXIS= arguments are not supplied.

The arguments may be listed within parentheses in any order, separated by commas. For example:

%canplot(data=inputdataset, var=predictors, class=groupvariable..., )

- DATA=_LAST_
- The name of the input dataset. If not specified, the most recently created dataset is used.
- CLASS=
- The name of one class (group) variable
- VAR=
- List of classification (predictor) variables
- SCALE=AUTO
- Scale factor for variable vectors in plot. The variable vectors are multiplied by the SCALE= value, which should be specified (perhaps by trial and error) to make the vectors and observations fill the same plot region. If SCALE=AUTO or SCALE=0, the program estimates a scale factor from the ratio of the maximum distance to the origin in the observations relative to the variables.
- CONF=.95
- Confidence probability for canonical means
- OUT=_DSCORE_
- The name of the output data set containing discriminant scores
- ANNO=_DANNO_
- Output data set containing annotations
- PLOT=YES
- YES to produce the plot, or NO to suppress the plot
- HAXIS=
- The name of an optional AXIS statement for the horizontal axis. The HAXIS= and VAXIS= arguments may be used to equate the axes in the plot so that the units are the same on the horizontal and vertical axes.
- VAXIS=
- The name of an optional AXIS statement for the vertical axis.
- NAME=CANPLOT
- name for graphic catalog entry
- COLORS=RED GREEN BLUE BLACK PURPLE YELLOW BROWN ORANGE
- List of colors to be used for groups
- SYMBOLS=+ SQUARE STAR - PLUS : $ =
- List of symbols to be used for the various groups (levels of the CLASS= variable).

goptions vsize=3.2 in hsize=7.5in htext=2; %include data(iris); title h=2.5 'Iris Data - Canonical Discriminant Plot'; axis1 order=(-10 to 10 by 2); axis2; legend1 value=(h=2); %canplot( data=iris, class=species, var=sepallen sepalwid petallen petalwid, haxis=axis1, vaxis=axis2, legend=legend1, hsym=1.5, colors=red blue black, scale=3.5);

equate Creates AXIS statements for a GPLOT with equated axes

hemat HE plots for all pairs of response variables

heplot Plot Hypothesis and Error matrices for a bivariate MANOVA effect

meanplot Plot means for factorial designs

outlier Robust multivariate outlier detection