1. Home
  2. CVAPLOT procedure

CVAPLOT procedure

Plots the mean and unit scores from a canonical variates analysis (D.A. Murray).

Options

PLOT = string tokens Type of plot to be drawn (meanscores, unitscores, confidenceregion); default mean, conf
GROUPS = factor Group allocations in the CVA
MSCORES = matrix Mean scores from the CVA; if unset these are calculated using the CVA directive
USCORES = matrix Unit scores from the CVA; if unset these are calculated using the CVASCORES procedure
WSSPM = SSPM Within-group sums of squares and products, means etc. for the CVA; must be supplied if the scores and groupings are not provided
CREGION = string tokens Type of confidence region to be drawn (mean, population); default mean
CIPROBABILITY = scalar The probability level for the confidence region; default 0.95
TAREA = scalar Defines the transparency to use to shade the confidence regions; default 255 i.e. no shading

Parameters

YDIMENSION = scalars Dimensions to be plotted in the y direction of each graph
XDIMENSION = scalars Dimension to be plotted in the x direction
TITLE = texts Title for each plot
WINDOW = scalars Window for each graph; default 1
SCREEN = string tokens Whether to clear the screen before plotting (clear, keep); default clea

Description

CVAPLOT plots information from a canonical variates analysis. The type of graph to be displayed is controlled by the PLOT option with settings meanscores to draw mean scores, unitscores to display the unit scores and confidenceregion to display confidence regions about the means or the tolerance region for a population. The CREGION option specifies the type of confidence region that is drawn. The setting mean will draw the confidence region about the population means, and population plots the tolerance region for the populations. By default a 95% confidence region is calculated, but this can be changed by setting the CIPROBABILITY option to the required value (between 0 and 1). You can shade the confidence regions by setting the TAREA option. This defines a transparency value (between 0 and 255) for the shaded regions, in a similar way to the TAREA option of PEN. The default value of 255 indicates that the regions are completely transparent (i.e. completely unshaded); a line is then drawn around each region.

Matrices containing the mean scores and units scores can be supplied directly, using options MSCORES and USCORES respectively, and option GROUPS can supply a factor defining the groupings of the units in the canonical variates analysis. Alternatively, you can supply a within-group SSPM and the scores will be calculated within the procedure, using the CVA directive and the CVASCORES procedure, and the groups will be accessed from within the SSPM.

The YDIMENSION and XDIMENSION parameters specify which dimensions are to be plotted in the y and x directions; by default these are dimensions 1 and 2 respectively. The WINDOW parameter indicates the window to be used for each plot (default 1), the TITLE parameter provides a title for each plot, and the SCREEN parameter indicates whether existing plots on the screen are to be kept or cleared each time (the default being to clear the screen).

Options: PLOT, GROUPS, MSCORES, USCORES, WSSPM, CREGION, CIPROBABILITY, TAREA.

Parameters: YDIMENSION, XDIMENSION, TITLE, WINDOW, SCREEN.

Method

The CVA directive and the CVASCORES procedure are used to calculate the scores if necessary. A two dimensional representation of the results of the CVA is then plotted on the current high resolution graphics device. The 95% confidence region of the group means is calculated by circles of radius

SQRT( EDCHISQUARE( CIPROBABILITY; 2 ) / n )

about the means and the tolerance region of the populations is calculated by

SQRT( EDCHISQUARE( CIPROBABILITY; 2 ) )

(see Krzanowski 1988, page 374).

Reference

Krzanowski, W.J. (1988). Principles of Multivariate Analysis. Oxford University Press, Oxford.

See also

Directive: CVA.

Procedures: CVASCORES, DBIPLOT.

Commands for: Multivariate and cluster analysis, Graphics.

Example

CAPTION   'CVAPLOT example'; STYLE=meta
" The data for this example deal with measurements made on 28 brooches
  found at the archaeological site of the cemetry at Munsingen. Seven
  measurements are used and have been transformed by taking logarithms.

  A grouping factor, obtained from a cluster analysis, with four levels
  has also been included.

  (Doran and Hodson, Mathematics and computers in archaeology. (1975)) "

FACTOR    [LEVELS=4] Groupno
READ      Groupno,Bow_ht,Bow_thck,Bow_wdth,Coil_dia,Elem_dia,Foot_lth,Length
3 3.21888   3.58352   3.58352   2.83321   2.63906   4.54329 4.74493
1 2.07944   2.89037   3.58352   1.94591   1.09861   3.09104 3.58352
2 2.77259   3.49651   3.68888   2.07944   2.19722   3.52636 4.11087
2 3.29584   4.35671   4.14313   2.30259   2.56495   3.17805 4.31749
2 3.17805   3.97029   4.35671   2.07944   2.19722   3.04452 4.23411
1 2.77259   3.58352   3.63759   2.07944   1.38629    3.3322 4.02535
1 2.94444   3.58352   3.58352    2.3979   1.79176   2.77259 3.71357
4 2.63906   2.70805   5.17615   2.07944   1.79176   3.46574 4.00733
2 2.89037   4.20469    4.5326   1.94591   1.94591   2.99573 3.68888
3 3.17805   4.07754   4.30407   2.48491   2.70805   3.73767 4.27667
3 2.89037    3.8712   4.07754    2.3979   2.19722    3.8712 4.36945
4 2.77259   3.68888   4.77068   1.94591   1.94591    3.4012  3.8712
2 2.63906    3.3322   3.97029   1.94591    2.3979   3.17805 3.73767
2 2.77259   3.89182    3.8712   1.79176   2.56495   3.04452 3.66356
2 2.83321   3.58352   3.95124   2.07944   2.19722   2.89037 3.80666
2 2.77259   3.66356   4.02535   2.07944   1.94591   3.04452 3.93183
2 2.63906   3.80666   3.80666   1.79176    2.3979   3.04452 3.61092
4 2.94444   3.17805   4.40672   2.30259   1.79176   3.09104 3.91202
1 2.89037   3.29584    3.2581    2.3979   2.19722    3.3673 3.98898
3 2.77259   3.78419   3.78419   2.56495   2.48491   4.55388 4.85981
4 2.94444   3.43399   4.48864   2.07944   1.79176   3.13549 4.09434
4 2.70805   2.70805   4.96981   1.94591   1.38629   3.04452 3.80666
2 2.77259   3.85015   3.93183   2.07944   2.63906   3.13549  3.8712
2 3.13549   4.17439   4.23411   2.30259   2.30259   2.56495 3.82864
2 2.77259   3.71357   4.41884    2.3979   2.30259    3.3322 3.98898
1 2.99573   3.58352   3.63759   2.07944   1.38629   2.77259 4.04305
1  2.3979   3.17805   3.04452   1.94591   1.09861    2.3979 3.29584
3 2.94444   4.18965   4.54329   2.30259   1.38629   4.23411 4.70953 :

SSPM      [TERMS=Bow_ht,Bow_thck,Bow_wdth,Coil_dia,Elem_dia,Foot_lth,Length;\ 
          GROUPS=Groupno] wssp
FSSP      wssp
CVAPLOT   [PLOT=mean,unit,confidence; WSSP=wssp] YDIMENSION=1,1,2;\
          XDIMENSION=2,3,3; TITLE='1 vs 2','1 vs 3','2 vs 3';\ 
          WINDOW=5,7,8; SCREEN=clear,keep,keep
Updated on March 8, 2019

Was this article helpful?