67
DATA
TAI 2; SET GRA2 ONO;
PROC SORT
; BY LS;
DATA
TA3; MERGE TAI TAI 1 TAI 2 TAI GRAF;
RUN
;
PROC GPLOT
DATA=TA3; TI TLE1 ;
Lampiran 1 Lanjutan
TI TLE2 H=
1
DI STRI BUTI ON OF THE VARI ETI ES I N ; TI TLE3 H=
1
THE ALPHA- LAMBDA SPACE, TAI , 1971 ; AXI S1 MAJOR= H=
0. 5
MI NOR=NONE LABEL= F=TRI PLEX H=
1
VALUE= H=
0. 8
OFFSET= ,
; AXI S2 MAJOR= H=
0. 5
MI NOR=NONE LABEL= F=TRI PLEX H=
1
VALUE= H=
0. 8
OFFSET= ,
; SYMBOL1 COLOR=BLACK L=
1
I NTERPOL=SM40 V= WI DTH=
1
HEI GHT=
1
; SYMBOL2 COLOR=BLACK L=
1
I NTERPOL=SM40 V= WI DTH=
1
HEI GHT=
1
; SYMBOL3 COLOR=BLACK F=SPECI AL V=I I NTERPOL=NONE WI DTH=
0. 1
HEI GHT=
0. 5
; SYMBOL4 COLOR=BLACK F=SPECI AL V=M I NTERPOL=NONE WI DTH=
0. 1
HEI GHT=
0. 5
; PLOT ALPHA LAMBDA=
4
UA80 LS=
1
LA80 LS=
1
UA90 LS=
2
LA90 LS=
2
UA95 LS=
1
LA95 LS=
1
VAXI S=AXI S1 HAXI S=AXI S2 FRAME OVERLAY;
RUN
; COMPUTATI ON OF DI STANCES FOR CLUSTERI NG
DATA
DI STANCE; MERGE ELY KEEP=ENV VAR YI ELD VM GM VI VXE RTY
RANKANAL KEEP=VAR K W S D1 DSA DSB ; BY VAR;
AI I =VI DSA; STANDARDI ZED YI ELD BI I =VXE DSB; STANDARDI ZED I NTERACTI ON
RUN
; RESHAPPI NG OF DATA FOR CLUSTERI NG
OPTI ONS LS=
96
PS=
80
NODATE;
DATA
KI I ; SET YAH KEEP=VAR ; JUST KEEP THE NAME OF THE VARI ETI ES
DATA
JI I ; SET AH KEEP=ENV ; JUST KEEP THE NAME OF THE ENVI RONMENTS
PROC I ML
; USE BRUCE;
READ ALL VAR { VI } I NTO G; A=SHAPE G, p, q ;
B=A` ; ENV= EN1 : EN6 ;
VAR= VAR1 : VAR12 ; USE DI STANCE;
READ ALL VAR { AI I } I NTO H; C=SHAPE H, p, q ;
D=C` ; READ ALL VAR { BI I } I NTO I ;
E=SHAPE I , p, q ; F=E` ;
CREATE ONE FROM A [ COLNAME=ENV] ; APPEND FROM A;
CREATE TWO FROM B [ COLNAME=VAR] ; APPEND FROM B ;
CREATE FI V FROM C [ COLNAME=ENV] ; APPEND FROM C;
CREATE NI NE FROM E[ COLNAME=ENV] ; APPEND FROM E;
CREATE TEN FROM F [ COLNAME=VAR] ; APPEND FROM F ;
DATA
THREE;
Lampiran 1 Lanjutan
68
MERGE KI I ONE;
DATA
FOUR; MERGE J I I TWO;
DATA
SEVEN; MERGE KI I FI V;
DATA
ELEVEN; MERGE KI I NI NE;
TI TLE1 CLUSTERI NG OF VARI ETI ES USI NG RAW YI ELDS OF GENOTYPES ; TI TLE2 HANSON 1970 , MUNGOMERY ET AL. 1974 , JOHNSON 1977 ;
PROC CLUSTER
DATA=THREE METHOD=AVERAGE NONORM NOSQUARE NOPRI NT; I D VAR;
PROC TREE
HORI ZONTAL SPACE=
1
;
RUN
; TI TLE1 CLUSTERI NG OF VARI ETI ES USI NG YI ELDS ADJUSTED FOR GENOTYPE MEANS AND ;
TI TLE2 WEI GHTED BY THE STANDARD DEVI ATI ON, FOX AND ROSI ELLE 1982 ;
PROC CLUSTER
DATA=SEVEN METHOD=AVERAGE NONORM NOSQUARE NOPRI NT; I D VAR;
PROC TREE
HORI ZONTAL SPACE=
1
;
RUN
; TI TLE1 CLUSTERI NG OF VARI ETI ES USI NG THE Gx E I NTERACTI ONS WEI GHTED BY ;
TI TLE2 THE SQUARE ROOT OF WRI CKES ECOVALENCE, ABOU- EL- FI TTOUH ET AL. 1969 ;
PROC CLUSTER
DATA=ELEVEN METHOD=AVERAGE NONORM NOSQUARE NOPRI NT; I D VAR;
PROC TREE
HORI ZONTAL SPACE=
1
;
RUN
; PARTI TI ON I NTO HETEROGENEI TY AND LACK OF CORRELATI ON BASED ON
DEVI ATI ON FROM AVERAGE S
PROC MEANS
DATA=RANKANAL MEAN NOPRI NT; VAR S MEK;
OUTPUT OUT=SCALE MEAN=L MEK;
RUN
;
DATA
MNX; MERGE SCALE RANKANAL;
BY MEK; C=S- L;
HET=C
2
; CORR=W- HET;
PROC MEANS
DATA=MNX SUM NOPRI NT; VAR HET CORR;
OUTPUT OUT=MR SUM=HETERO1 LACKCOR1; PARTI TI ON OF GxE I NTO HETEROGENEI TY OF VARI ANCES AND LACK OF CORRELATI ON
USI NG THE ORI GI NAL FORMULA PROVI DED BY MUI R ET AL. FOR I NDI VI DUAL GENOTYPES
PROC I ML
; USE RANKANAL;
READ ALL VAR { S} I NTO A; B=SHAPE A, p, p ;
D=B` ; E= B- D
2
[ +, ]
2
p ` ; T= B- D
2
`
2
p ; THI S MATRI X CONTAI NS HETEROGENEI TY BETWEEN ANY TWO GENOTYPES, CAN BE USED AS DI STANCE MEASURE
C=A| | A; P=SHAPE C, p,
2
; Q=P` ;
VAR= VAR1 : VAR12 ; CREATE SSS FROM Q[ COLNAME=VAR] ;
APPEND FROM Q; CREATE VHET FROM E[ COLNAME= HETERO ] ;
APPEND FROM E;
PROC CORR
DATA=SSS OUTP=ESS SSCP NOPRI NT; VAR VAR1- - VAR12;
RUN
;
Lampiran 1 Lanjutan
DATA
FSS; SET ESS DROP=I NTERCEPT ;
I F _TYPE_= SSCP ;
DATA
GI FAR; SET FSS;
I F _NAME_ NE I NTERCEPT ;
PROC CORR
DATA=FOUR OUTP=J JJ NOPRI NT; VAR VAR1- - VAR12;
RUN
;
DATA
LI M; SET JJJ ;
69
I F _TYPE_= CORR ;
PROC I ML
; USE LI M;
READ ALL VAR _NUM_ I NTO G; K=- G-
1
; USE GI FAR;
READ ALL VAR _NUM_ I NTO D; E=D
2
; X= KE p; CONTAI NS LACK OF CORREL. BETWEEN ANY TWO GENOTYPES
N=X[ , +] ; VAR= VAR1 : VAR12 ;
CREATE FHP FROM X[ COLNAME=VAR] ; CONTAI NS LACK OF CORRELATI ON BETWEEN ANY TWO GENOTYPES APPEND FROM X; YOU CAN PRI NT THI S DATA SET TO OBTAI N DETAI LED
I NFORMATI ON CREATE LACK FROM N[ COLNAME= LACKCORR ] ;
APPEND FROM N;
DATA
KI YYA; MERGE FI VE KEEP=VAR VARMEAN W VHET LACK MNX;
PROC PRI NT DATA=KI YYA; TI TLE1 TABLE A ;
TI TLE2 PARTI TI ON OF THE G x E I NTERACTI ON SUM OF SQUARES I N TO SUMS OF ; TI TLE3 SQUARES DUE TO HETEROGENEI TY AMONG VARI ANCES AND LACK OF CORRELATI ON ;
TI TLE4 AMONG PERFORMANCE VALUES OF I NDI VI DUAL GENOTYPES MUI R ET AL. 1992 ; VAR VAR VARMEAN W HETERO LACKCORR HET CORR;
RUN; WE HAVE MUTED THE PRI NTI NG OF THI S PART BECAUSE THE PARTI TI ONI NG OF I NDI VI DUAL
GENOTPE S WRI CKE S ECOVALENCE I NTO THAT DUE TO HETEROGENEI TY OR LACK OF CORRELATI ON I S SELDOM CORRECT. WE USED TWO FORMULAE FOR THI S PATI TI ONI NG. THE ONE PROVI DED FOR
I NDI VI DUAL GENOTYPE AND THE ONE FOR THE TOTAL. THE LACK OF CORRELATI ON COMPUTED BY ADAPTI NG THE
FORMULA FOR THE TOTAL I S NAMED LACKCORR, WHI LE THE ONE COMPUTED I N THE USUAL MANNER I S NAMED
CORR. CORRESPONDI NG HETEROGENEI TY COMPONENTS ARE NAMED HETERO AND HET, RESPECTI VELY.
I NTERESTED USERS CAN PRI NT THI S DATA SET AND SEE THE DESCREPANCY, THAT FOR SOME GENOTYPES THE LACK
OF CORRELATI ON OR THE SUM OF THE LACK OF CORRELATI ON AND HETEROGENEI TY I S MORE THAN
WRI CKE S ECOVALENCE, WHI LE FOR OTHER GENOTYPES I T I S LESS THAN THI S COMPONENT ;
PROC MEANS
DATA=KI YYA SUM NOPRI NT; VAR W HETERO LACKCORR; OUTPUT OUT=MR SUM=;
Lampiran 1 Lanjutan
PROC PRI NT
DATA=MR; TI TLE1 TABLE 9 ;
TI TLE2 TOTAL SUMS OF SQUARES DUE TO HETEROGENEI TY AMONG VARI ANCES ; TI TLE3 AND I MPERFECT CORRELATI ON ;
VAR W HETERO LACKCORR;
RUN
; s I NGULAR VALUE DECOMPOSI TI ON OF THE Gx E MATRI X FOR ORDI NATI ON
DATA
GELO; MERGE AH KEEP=ENV EM YAH KEEP=VAR VM ;
RUN
;
DATA
ONE; SET BRUCE KEEP=VXE ;
RUN
;
PROC I ML
; RESET NOPRI NT FUZZ; START J AMBO; USE ONE; READ ALL I NTO G;
X=SHAPE G, p, q ; Y=X` ; P=X Y; A=Y X; Q=EI GVAL P ; I F MI N Q
. 001
