LINEAR COMBINATION

LINEAR COMBINATION (LET)

Let Subcommand

Compute the linear combination of a matrix and a vector.

If a matrix M has p columns and n rows and C is a vector with p rows, then the linear combination of M and C is defined as:
Y = c(1)*M₁ + C(2)*M₂ + c(3)*M₃ + ... +c(p)*M_p

where M₁, M₂, ... , M_p are the columns of M. The result Y is a vector with n rows.

Linear combinations are common in statistics, particularly in linear models and multivariate analysis. In Dataplot applications, the LINEAR COMBINATION command is most typically used as an intermediate calculation in a larger macro.

LET <y> = LINEAR COMBINATION <mat1> <x>
where <mat1> is a matrix for which the linear combination is to be computed;
            <x> is a vector for which the linear combination is to be computed;
and where <mat2> is a matrix where the resulting linear combination is saved.

LET A = LINEAR COMBINATION M X

Matrices are created with either the READ MATRIX command or the MATRIX DEFINITION command. Enter HELP MATRIX DEFINITION and HELP READ MATRIX for details.

None

None

READ MATRIX	= Read a matrix.
MATRIX COLUMN DIMENSION	= Dimension maximum number of columns for Dataplot matrices.
MATRIX MEAN	= Compute the overall mean for a matrix.
MATRIX COLUMN STATISTIC	= Compute column statistics for a matrix.
MATRIX ROW STATISTIC	= Compute row statistics for a matrix.
QUADRATIC FORM	= Compute quadratic form of a matrix and a vector.

"Applied Multivariate Statistical Analysis", Third Edition, Johnson and Wichern, Prentice-Hall, 1992.

Multivariate Analysis

1998/8

. Perform a Fisher's dsicriminant analysis on Iris data.
.
. READ DATA, 3 GROUPS (N1=N2=N3=3), 2 VARIABLES
FEEDBACK OFF
DIMENSION 200 COLUMNS
SKIP 25
READ IRIS.DAT SEPLENG SEPWIDTH PETLENG PETWIDTH TAG
SKIP 0
LET NTOT = SIZE SEPLENG
LET X = MATRIX DEFINITION SEPLENG NTOT 4
LET P = MATRIX NUMBER OF COLUMNS X
.
LET GROUPID = DISTINCT TAG
LET NG = SIZE GROUPID
LET XMGRAND = MATRIX COLUMN MEANS X
.
. CALCULATE B0 = SUM (I=1,NG) (XBARi - XBARALL)(XBARi-XBARALL)'
.
LET DIAG = 0 FOR I = 1 1 P
LET B0 = DIAGONAL MATRIX DIAG
.
LOOP FOR K = 1 1 NG
        LET N^K = SIZE TAG SUBSET TAG = K
        LET XMEANI = MATRIX COLUMN MEANS X SUBSET TAG = K
        LET XMEANI= XMEANI - XMGRAND
        LET B0TEMP = VECTOR TIMES TRANSPOSE XMEANI
        LET B0 = MATRIX ADDITION B0 B0TEMP
END OF LOOP
.
. CALCULATE Spooled = (N1-1)S1 + .. + (Ng-1)Sg)/(N1+ .. + Ng - g)
LET SPOOL = POOLED VARIANCE-COVARIANCE MATRIX X TAG
LET DENOM = NTOT - NG
LET WINVB = MATRIX MULTIPLICATION SPOOL DENOM
LET WINVB = MATRIX INVERSE WINVB
LET WINVB = MATRIX MULTIPLICATION WINVB B0
.
. COMPUTE EIGENVALUES AND SORT IN DECREASING ORDER
. COMPUTE EIGENVECTORS, ONLY KEEP REAL COMPONENT, SORT
.
LET E = MATRIX EIGENVALUES WINVB
LET EV = MATRIX EIGENVECTORS WINVB
LET INDX = SEQUENCE 1 1 P
RETAIN E FOR I = 1 1 P
LET ESORT = SORTC E INDX
LET REVERSE = SEQUENCE P 1 1
LET REVERSE = SORTC REVERSE ESORT INDX
LET EVECT = DIAGONAL MATRIX DIAG
. NORMALIZE L'SpooledL =1
. DIST = L'SpooledL, MULTIPLY EIGENVECTOR BY 1/SQRT(DIST)
LOOP FOR K = 1 1 P
        LET LTAG = INDX(K)
        RETAIN EV^K FOR I = 1 1 P
        LET EVECT^LTAG = EV^K
        LET DIST = QUADRATIC FORM SPOOL EVECT^LTAG
        LET EVECT^LTAG = (1/SQRT(DIST))*EVECT^LTAG
END OF LOOP
. PLOT FIRST 2 DISCRIMINANTS
LET ZY = LINEAR COMBINATION X EVECT1
LET ZX = LINEAR COMBINATION X EVECT2
DEVICE 1 OFF
MEAN PLOT ZY TAG
LET GMEANY = YPLOT
MEAN PLOT ZX TAG
LET GMEANX = YPLOT
RETAIN GMEANX GMEANY SUBSET TAGPLOT = 1
DEVICE 1 ON
Y1LABEL FIRST DISCRIMINANT
X1LABEL SECOND DISCRIMINANT
CHARACTER CIRCLE SQUARE TRIANGLE
LINE BLANK ALL
LEGEND 1 CIRC() - SPECIES 1
LEGEND 2 SQUA() - SPECIES 2
LEGEND 3 TRIA() - SPECIES 3
LEGEND FONT DUPLEX
LEGEND SIZE 1.2
TITLE PLOT FIRST 2 DISCRIMINANT FUNCTIONS
PLOT ZY ZX TAG
PRINT "FISHER's DISCRIMINANT ANALYSIS"
PRINT " "
PRINT " "
PRINT "B0 MATRIX (= between group sums of cross-products):"
PRINT B0
PRINT " "
PRINT " "
PRINT "POOLED VARIANCE-COVARIANCE MATRIX:"
PRINT SPOOL
PRINT " "
PRINT " "
PRINT "EIGENVALUES:"
PRINT ESORT
PRINT " "
PRINT " "
PRINT "COLUMNS ARE THE DISCRIMINANT FUNCTIONS:"
PRINT EVECT
PRINT " "
PRINT " "
PRINT "GROUP MEANS:"
PRINT GMEANX GMEANY




 FISHER's DISCRIMINANT ANALYSIS
  
  
 B0 MATRIX (= between group sums of cross-products):

 VARIABLES--B01            B02            B03            B04     

   0.1264242E+01 -0.3990533E+00  0.4142301E+01  0.1332920E+01
  -0.3990533E+00  0.2268987E+00 -0.1515458E+01 -0.1713200E+00
   0.4142301E+01 -0.1515458E+01  0.1400072E+02  0.3853480E+01
   0.1332920E+01 -0.1713200E+00  0.3853480E+01  0.2021600E+01
  
  
 POOLED VARIANCE-COVARIANCE MATRIX:

 VARIABLES--SPOOL1         SPOOL2         SPOOL3         SPOOL4  

   0.2650082E+00  0.9272107E-01  0.1675143E+00  0.3840136E-01
   0.9272107E-01  0.1153878E+00  0.5524353E-01  0.3271021E-01
   0.1675143E+00  0.5524353E-01  0.1851877E+00  0.4266530E-01
   0.3840136E-01  0.3271021E-01  0.4266530E-01  0.4188163E-01
  
  
 EIGENVALUES:

 VARIABLES--ESORT   

   0.8901299E+00
   0.2225433E+00
  -0.3733565E-08
  -0.2227863E-07
  
  
 COLUMNS ARE THE DISCRIMINANT FUNCTIONS:

 VARIABLES--EVECT1         EVECT2         EVECT3         EVECT4  

  -0.1369850E+01  0.8875500E+00 -0.2325767E+01 -0.2739004E+01
  -0.9835795E+00 -0.9720628E+00  0.8406132E-02  0.2782870E+01
   0.3014445E+01 -0.2081234E+01  0.5611728E+00  0.1156235E+01
   0.1221005E+01  0.5894731E+01  0.4644992E+00 -0.1621946E+00
  
  
 GROUP MEANS:

 VARIABLES--GMEANX         GMEANY  

   0.1599418E+01 -0.8536139E+01
  -0.4368491E+01  0.2383637E+01
   0.3343979E+01  0.7260216E+01