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Analysis Using SAS
For performing the
analysis, input the data in the following format.{ Here
genotypes are termed as trt (trt or the ID variable should
be a string variable) and y1-y8 are the eight characters of
interest.} Prepare a SAS data
file using DATA
cluster; input trt $ y1 - y8;/* y1 to y8 are 8 characters of interest*/ /*
For the variable trt one can also give the original names
for the genotypes*/ cards; 1
42.43 7.51
78.32 128.24
45.26 6.51
7.74
7.66 2
43.83 7.91
71.20 114.71
82.90 6.51
6.34
7.05 3
40.77 9.77
85.47 129.50
189.22 6.64
6.91
10.09 4
43.35 8.18
68.25 113.16
176.59 6.77
6.57
8.91 5
45.74 7.97
83.52 123.77
80.53 6.57
6.83
7.34 6
45.43 8.11
84.45 132.23
77.30 6.47
6.81
7.35 7
42.68 7.68
92.47 128.09
36.70 6.55
7.16
6.43 8
39.18 6.64
73.50 122.04
49.24 6.25
7.20
6.75 9
45.98 8.10
67.41 123.39
45.25 6.20
8.01
6.68 10
43.64 8.56
84.44 138.81
90.98 6.64
6.28
7.04 11
44.68 8.11
91.71 125.12
65.78 6.40
6.49
6.51 12
45.90 7.50
70.85 122.81
54.94 6.36
8.85
8.48 13
42.61 7.57
75.78 120.91
85.98 6.57
7.02
6.91 14
42.56 8.21
94.64 134.48
111.13 6.65
6.60
8.06 15
45.86 7.78
84.67 123.80
82.93 6.67
6.75
6.89 16
41.70 8.00
95.02 137.69
116.10 6.63
6.58
7.45 17
43.25 7.78
82.50 129.05
107.23 6.68
6.83
7.46 18
43.05 8.10
73.76 120.28
203.81 6.64
6.65
10.18 19
40.24 7.48
74.66 121.99
88.66 6.69
6.53
7.46 20
44.43 8.32
83.58 122.86
83.86 6.58
6.58
8.57 21
44.34 7.81
75.01 129.42
74.00 6.61
7.53
8.53 22
44.67 7.98
75.55 123.79
102.31 6.62
6.72
7.85 23
43.54 7.65
94.30 134.73
77.17 6.53
6.93
7.26 24
45.10 8.01
91.40 134.84
86.93 6.52
7.32
7.12 25
45.03 10.15
85.85 133.02
73.07 6.63
6.32
6.85 26
46.82 7.54
85.22 130.92
65.22 6.63
6.59
7.10 27
46.52 7.65
82.51 125.34
66.98 6.59
6.60
6.90 28
42.33 7.20
65.85 120.38
66.24 6.61
7.41
7.17 29
42.98 8.06
84.04 128.80
94.25 6.65
6.91
8.18 30
46.19 7.60
94.02 137.15
97.35 6.59
6.26
7.38 31
45.69 8.03
92.94 136.41
68.96 6.47
6.41
6.54 32
44.46 7.79
85.94 132.44
83.46 6.65
6.74
7.15 33
46.50 7.92
82.59 133.78
56.92 6.39
7.11
6.43 34
46.45 8.30
81.20 134.81
92.09 6.43
8.09
7.52 35
43.05 7.98
84.54 131.97
88.59 6.62
6.59
7.78 36
43.64 7.49
66.53 114.51
107.24 6.78
7.11
8.29 37
44.57 7.76
91.55 135.29
49.93 6.48
6.94
6.74 38
44.34 8.00
94.67 140.19
103.65 6.48
6.50
7.40 39
43.65 7.66
66.88 119.78
127.28 6.59
7.13
8.70 40
43.58 7.92
80.75 133.31
147.71 6.79
6.40
8.58 41
44.45 8.06
84.76 121.68
85.58 6.10
8.22
7.75 42
47.87 8.12
86.94 125.43
81.97 6.39
7.61
8.11 43
45.23 7.22
79.38 126.39
51.82 6.33
9.44
7.25 44
43.53 7.55
82.47 131.00
35.56 6.04
10.36 7.59 45
43.13 7.52
67.45 116.49
133.48 6.70
6.59
9.20 46
41.95 7.34
71.86 121.80
95.91 6.74
6.52
7.58 47
41.06 7.28
69.43 120.32
74.69 6.65
6.78
7.18 48
39.71 7.24
64.23 114.25
130.62 6.76
6.59
8.83 49
41.30 7.18
64.53 113.78
93.68 6.78
6.52
7.71 50
41.95 7.30
66.53 115.44
139.30 6.71
6.32
7.96 51
43.48 7.38
66.36 115.12
106.66 6.74
6.24
8.01 52
43.22 7.56
71.29 115.29
159.95 6.68
6.44
8.54 53
40.15 7.31
67.65 115.35
140.49 6.77
6.69
8.48 54
44.30 7.73
63.26 117.41
144.21 6.71
6.59
8.42 55
38.25 7.24
63.71 113.24
104.85 6.76
6.69
7.46 56
44.07 7.53
64.34 113.68
96.62 6.75
6.48
8.01 57
43.79 7.75
65.22 116.27
128.92 6.70
6.55
8.58 58
43.11 7.68
62.48 115.50
143.87 6.79
6.52
8.97 59
40.87 7.55
66.39 114.95
141.49 6.79
6.43
8.31 60
42.98 7.36
68.64 115.32
115.23 6.72
6.46
8.06 61
47.40 7.76
65.37 115.10
134.99 6.64
6.60
7.41 62
39.00 7.67
64.94 113.05
122.66 6.72
6.49
7.71 63
44.38 7.41
68.30 115.99
128.34 6.76
6.66
8.65 64
42.13 7.19
68.88 119.77
90.78 6.67
7.21
7.90 65
42.68 7.40
65.26 118.73
115.82 6.79
7.03
8.35 66
40.62 7.85
65.17 113.06
134.02 6.79
6.38
8.44 67
44.43 7.40
67.14 117.82
115.09 6.73
7.37
8.47 68
41.56 6.94
69.03 115.53
93.68 6.63
6.79
7.23 69
41.07 7.00
63.97 115.42
91.20 6.83
7.53
7.92 70
41.10 7.71
63.98 113.52
144.02 6.86
6.65
11.09 71
42.45 7.12
65.92 117.29
79.98 6.75
7.18
7.15 72
42.12 7.35
60.95 108.99
128.10 6.77
6.40
8.13 73
41.00 7.33
65.33 113.44
130.96 6.77
6.37
7.97 74
43.67 7.64
62.95 118.32
119.09 6.72
7.15
9.26 75
46.49 7.97
88.06 126.87
75.97 6.58
6.53
6.78 76
42.98 7.39
66.57 119.79
118.84 6.65
7.04
8.54 77
41.01 7.02
59.90 113.64
104.40 6.70
7.09
8.36 78
48.85 6.84
45.32 104.53
66.53 6.75
8.27
7.69 79
49.60 7.17
59.37 110.36
82.16 6.76
7.93
7.80 80
49.50 7.40
62.24 113.10
144.37 6.50
6.64
9.01 81
44.53 7.63
65.14 113.71
140.34 6.76
6.74
8.31 82
46.59 7.47
85.08 123.53
72.37 6.67
6.37
6.74 83
44.78 7.47
85.72 126.54
113.46 6.69
6.59
6.92 84
42.22 7.33
69.77 115.38
105.00 6.70
6.67
8.11 85
37.10 7.13
80.79 122.32
64.18 6.44
6.29
6.69 86
44.42 6.94
66.76 120.34
49.67 6.33
8.35
7.28 87
45.53 7.52
67.79 114.47
146.09 6.71
6.54
8.13 88
42.50 7.48
62.49 114.59
130.72 6.79
6.61
8.24 89
46.06 7.67
86.69 125.51
75.84 6.44
6.44
7.12 90
36.44 7.45
71.74 114.81
137.22 6.73
6.28
8.23 91
42.67 7.36
70.64 121.17
43.06 6.71
6.79
6.13 92
40.44 7.06
59.99 107.81
99.93 6.86
7.11
8.53 93
39.35 7.51
55.26 120.88
91.51 6.63
6.86
6.98 94
45.41 7.08
67.92 118.80
64.25 6.63
7.93
7.61 95
43.19 7.32
65.39 118.82
98.70 6.67
7.07
8.18 96
45.43 7.52
65.12 118.90
95.82 6.79
7.61
8.21 97
44.34 7.48
63.66 113.72
110.46 6.79
7.02
7.97 98
42.76 7.25
63.02 109.59
104.84 6.84
7.08
9.16 99
45.07 8.56
92.05 135.80
94.57 6.68
6.55
7.60 100
40.96 7.39
63.33 112.40
96.36 6.76
6.90
7.56 101
42.15 6.97
61.06 114.76
118.46 6.78
6.71
8.42 102
40.36 7.00
69.93 120.29
53.84 6.23
8.44
7.15 103
42.68 7.81
62.51 113.08
152.97 6.75
6.83
8.49 104
39.78 7.00
69.38 121.18
76.18 6.57
8.58
9.46 105
41.20 7.06
68.03 118.67
69.91 6.61
6.48
6.91 106
40.65 7.16
65.63 118.89
92.78 6.71
7.03
7.99 107
43.32 7.78
65.56 118.91
128.72 6.82
6.92
8.63 108
50.53 7.07
68.18 118.80
89.23 6.72
7.84
8.66 109
44.89 7.82
65.60 114.99
124.45 6.77
6.67
8.40 110
46.23 7.72
67.76 120.83
111.15 6.72
7.52
9.12 /* This statement uses the DISTANCE procedure to obtain a
distance matrix that will be used as input to a subsequent
clustering procedure.An output SAS data set called Distmat
that contains the distance matrix
is created through the OUT= option.METHOD= Euclid requests
that Euclidean (which also is the default) distances should
be computed.For use in PROC CLUSTER, distance or
dissimilarity measures such as METHOD= EUCLID or METHOD=
DGOWER should be chosen. */
var interval(y1-y8); /*The
VAR statement lists the variables*/
id trt; /*variable in the ID option should be a character variable*/ run;
PROC print
data=distmat;
id trt;
run; /*The PROC CLUSTER statement starts the CLUSTER procedure,
identifies a clustering method. The METHOD= specification
determines the clustering method used by the procedure. Here we have used method = AVERAGE (unweighted pair-group
method using arithmetic averages, UPGMA). The output data
set Tree is created (through outtree= Tree) and
used as input to the TREE procedure that produces the
dendrogram .*/
PROC cluster
data=distmat method=average outtree=tree;
id trt;
run; /* The following statements use the TREE procedure to produce
a dendrogram of the clusters. The preceding
statements use the SAS data set Tree as input. The OUT=
option creates an output
SAS data set named out to contain information on
cluster membership. The NCLUSTERS= option specifies the
number of clusters desired in the data set out(here we have
taken 3 clusters).*/ /*after
removing * one can get the output as a cgm file directly,
which can be imported in
PowerPoint or word documents for clarity. */
*OPTIONS
PS = 5000 LS=78 NODATE; *FILENAME
DENDRO 'C:\Documents and Settings\Owner\Desktop\dend.cgm'; *GOPTIONS
DEVICE=CGMOF97L GSFNAME=DENDRO GSFMODE=REPLACE; goptions
htext=1pct ; PROC tree data=Tree nclusters=3
horizontal hordisplay=right
lines=(color=blue) out=out; id trt; run; /*
The following statement use the SORT procedure to sort the
data set out*/
PROC sort
data=out;
by trt;
run; /*
The following statement use the SORT procedure to sort the
data set cluster*/
PROC sort
data = cluster;
by trt;
run;
/*
The following statement merges the two data sets cluster and
out*/
DATA clus;
merge cluster out;
by trt;
run;
/*
The following statement use the SORT procedure to sort the
data set clus*/
PROC sort
data=clus;
by cluster;
run;
/*
The following statement use the PRINT procedure to print the
clusters*/
PROC print;
id trt;
by cluster;
run;
Analysis Using SAS Analysis Using SPSS
Home Descriptive Statistics Tests of Significance Correlation and Regression Completely Randomised Design RCB Design Incomplete Block Design Resolvable Block Design Augmented Design Latin Square Design Factorial RCB Design Partially Confounded Design Factorial Experiment with Extra Treatments Split Plot Design Strip Plot Design Response Surface Design Cross Over Design Analysis of Covariance Diagnostics and Remedial Measures Principal Component Analysis Cluster Analysis Groups of Experiments Non-Linear Models
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| Descriptive Statistics | |||||
| Tests of Significance | |||||
| Correlation and Regression | |||||
| Completely Randomised Design | |||||
| RCB Design | |||||
| Incomplete Block Design | |||||
| Resolvable Block Design | |||||
| Augmented Design | |||||
| Latin Square Design | |||||
| Factorial RCB Design | |||||
| Partially Confounded Design | |||||
| Factorial Experiment with Extra Treatments | |||||
| Split Plot Design | |||||
| Strip Plot Design | |||||
| Response Surface Design | |||||
| Cross-Over Designs | |||||
| Analysis of Covariance | |||||
| Diagnostics and Remedial Measures | |||||
| Principal Component Analysis | |||||
| Cluster Analysis | |||||
| Groups of Experiments | |||||
| Non-Linear Models | |||||
| Contact Us | |||||
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Other
Designed Experiments |
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For
exposure on SAS, SPSS, Please
see Module
I of Electronic Book II: available at Design Resources Server (www.iasri.res.in/design) |
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