LISTING SAS-AUGMENTED DESIGN-RCBD
/* Augmented Design in a Randomized Complete Block Design */ /* Original script written by Mateo Vargas */
/* Modified by Willy B. Suwarno [bayuardi@gmail.com] */
/* Reference: Patersen, R. G. 1994. Agricultural Field Experiments */ /* Date: 10 October 2014, Modified: 16 November 2016 */
/* JANGAN RUNNING SEKALIGUS */
/* Run satu-persatu anova1 lalu anova 2 lanjut uji LSD */
/* clear log and output */ dm 'log; clear; output; clear';
options pagesize=max formdlim='-' nocenter nonumber nodate; /* define data set (can be copy-pasted from Excel) */
/* note that there are $ signs after geno, check, and line_vs_check variables' name */ /* indicating that the variables should be read as string (not numeric) */
/* because the name of the checks are not numbers */ data augrcbd;
input
block geno$ line check$ line_vs_check$ yield;
cards;
1 C-1 0 C-1 check 2.69
1 1 1 0 cand 3.02
1 2 2 0 cand 2.52
1 3 3 0 cand 3.56
1 4 4 0 cand 2.90
1 5 5 0 cand 3.60
1 6 6 0 cand 3.53
1 7 7 0 cand 3.68
1 C-2 0 C-2 check 4.02
1 8 8 0 cand 3.46
1 9 9 0 cand 3.44
1 10 10 0 cand 2.91
1 11 11 0 cand 2.69
1 12 12 0 cand 4.59
1 13 13 0 cand 2.24
1 14 14 0 cand 3.86
1 C-3 0 C-3 check 3.92
1 15 15 0 cand 4.01
1 16 16 0 cand 2.60
1 17 17 0 cand 3.47
1 18 18 0 cand 2.49
1 19 19 0 cand 2.93
1 20 20 0 cand 3.49
1 21 21 0 cand 4.02
1 C-4 0 C-4 check 2.66
1 22 22 0 cand 3.82
1 23 23 0 cand 2.98
1 24 24 0 cand 2.16
1 25 25 0 cand 2.74
1 26 26 0 cand 2.70
1 27 27 0 cand 1.98
1 28 28 0 cand 2.44
2 C-4 0 C-4 check 2.47
2 29 29 0 cand 2.71
2 30 30 0 cand 3.14
2 31 31 0 cand 2.17
2 32 32 0 cand 3.01
2 33 33 0 cand 3.01
2 34 34 0 cand 2.99
2 35 35 0 cand 3.52
2 C-3 0 C-3 check 3.46
2 36 36 0 cand 2.51
2 37 37 0 cand 1.89
2 38 38 0 cand 3.05
2 39 39 0 cand 1.85
2 40 40 0 cand 3.15
2 41 41 0 cand 3.22
2 42 42 0 cand 3.21
2 C-2 0 C-2 check 3.51
2 43 43 0 cand 2.79
2 44 44 0 cand 3.41
2 45 45 0 cand 3.44
2 46 46 0 cand 3.04
2 47 47 0 cand 2.89
2 48 48 0 cand 2.85
2 49 49 0 cand 2.88
2 C-1 0 C-1 check 2.34
2 50 50 0 cand 3.10
2 51 51 0 cand 3.02
2 52 52 0 cand 2.77
2 54 54 0 cand 2.78
2 55 55 0 cand 2.00
2 56 56 0 cand 2.03
3 C-1 0 C-1 check 2.52
3 57 57 0 cand 3.16
3 58 58 0 cand 3.12
3 59 59 0 cand 2.24
3 60 60 0 cand 3.82
3 61 61 0 cand 3.29
3 62 62 0 cand 3.93
3 63 63 0 cand 3.67
3 C-4 0 C-4 check 2.79
3 64 64 0 cand 2.89
3 65 65 0 cand 3.43
3 66 66 0 cand 3.46
3 67 67 0 cand 3.81
3 68 68 0 cand 3.06
3 69 69 0 cand 3.06
3 70 70 0 cand 3.70
3 C-3 0 C-3 check 3.57
3 71 71 0 cand 3.31
3 72 72 0 cand 4.22
3 73 73 0 cand 2.41
3 74 74 0 cand 2.94
3 75 75 0 cand 2.59
3 76 76 0 cand 3.27
3 77 77 0 cand 2.91
3 C-2 0 C-2 check 3.55
3 78 78 0 cand 3.58
3 79 79 0 cand 3.05
3 80 80 0 cand 2.50
3 81 81 0 cand 2.28
3 82 82 0 cand 3.52
3 83 83 0 cand 2.87
3 84 84 0 cand 3.03
4 C-2 0 C-2 check 4.20
4 85 85 0 cand 3.46
4 86 86 0 cand 3.18
4 87 87 0 cand 3.76
4 88 88 0 cand 3.07
4 89 89 0 cand 3.49
4 90 90 0 cand 3.73
4 91 91 0 cand 4.10
4 C-1 0 C-1 check 2.39
4 92 92 0 cand 2.97
4 93 93 0 cand 2.53
4 94 94 0 cand 3.76
4 95 95 0 cand 3.11
4 96 96 0 cand 3.68
4 97 97 0 cand 3.30
4 98 98 0 cand 4.29
4 C-4 0 C-4 check 3.31
4 99 99 0 cand 3.90
4 100 100 0 cand 3.35
4 101 101 0 cand 3.07
4 102 102 0 cand 2.45
4 103 103 0 cand 3.48
4 104 104 0 cand 3.41
4 105 105 0 cand 3.06
4 C-3 0 C-3 check 2.98
4 106 106 0 cand 2.56
4 107 107 0 cand 3.07
4 108 108 0 cand 2.67
4 109 109 0 cand 2.97
4 110 110 0 cand 3.09
4 111 111 0 cand 3.32
4 112 112 0 cand 2.72
5 C-3 0 C-3 check 3.20
5 113 113 0 cand 3.43
5 114 114 0 cand 3.10
5 115 115 0 cand 3.38
5 116 116 0 cand 3.47
5 117 117 0 cand 3.66
5 118 118 0 cand 4.11
5 119 119 0 cand 3.50
5 C-2 0 C-2 check 3.74
5 120 120 0 cand 3.90
5 121 121 0 cand 3.03
5 122 122 0 cand 3.82
5 123 123 0 cand 3.19
5 124 124 0 cand 4.73
5 125 125 0 cand 2.65
5 126 126 0 cand 3.52
5 127 127 0 cand 3.68
5 128 128 0 cand 3.94
5 129 129 0 cand 3.75
5 130 130 0 cand 2.70
5 131 131 0 cand 3.99
5 132 132 0 cand 3.26
5 133 133 0 cand 3.89
5 C-4 0 C-4 check 2.70
5 134 134 0 cand 3.36
5 135 135 0 cand 3.77
5 136 136 0 cand 3.76
5 137 137 0 cand 2.71
5 138 138 0 cand 2.81
5 139 139 0 cand 2.48
5 140 140 0 cand 3.14
;
/* print data */
proc print data=augrcbd; title1 'Plot Data for Analysis'; run;
/* perform anova for the block and treatment (as a whole) effects */ proc glm data=augrcbd;
class block geno;
model yield = block geno /ss3;
title1 'ANOVA for Augmented Design - RCBD - Block Adjusted and Whole-Treatment Adjusted'; run;
/* perform anova for partitioning the treatment effect */ proc glm data=augrcbd;
class block line_vs_check check line;
model yield = block line_vs_check check line(check) /ss1;
title1 'ANOVA for Augmented Design - RCBD - Treatment-Partitions Adjusted'; run;
/* calculate adjusted means (lsmeans) using PROC MIXED */ proc mixed data=augrcbd;
class block line_vs_check check line; model yield = block check line(check); lsmeans line(check) /pdiff;
ods output lsmeans=LSMEANS diffs=DIFFS tests3=DOF; ods listing exclude lsmeans diffs FitStatistics; title1 'PROC MIXED for calculating LSMEANS'; run;
/*
proc print data=lsmeans; run;
*/
/* prepare a list of adjusted means */ data adjmeans;
set lsmeans; AdjMean = Estimate;
keep line check AdjMean StdErr; run;
proc sort data=adjmeans; by descending AdjMean; run;
proc print data=adjmeans; var line check AdjMean StdErr;
title1 'Adjusted Means and Standard Error Associated'; run;
/* calculate LSD, grand mean, and CV */ proc means data=DIFFS mean noprint; output out=StdErrInd;
var StdErr; run;
data StandardError; set StdErrInd; if _stat_='MEAN'; Standard_Error=StdErr; keep Standard_Error; run;
data GrandMean; set lmeansInd; if _stat_='MEAN'; Grand_Mean=Estimate; keep Grand_Mean; run;
data DOF1; set DOF;
effect=lowcase(effect); if effect ='line(check)'; Den_DF=DenDF;
keep Den_DF; run;
data LSD_CV;
merge StandardError DOF1 GrandMean; t=tinv(1-0.05/2,Den_DF);
LSD=t*Standard_Error;
CV=(Standard_Error/Grand_Mean)*100; run;
title1 'LSD, Grand Mean, and CV'; proc print Data = LSD_CV; run;
/* end of code */
OUTPUT SAS-AUGMENTED DESIGN-RCBD
Plot Data for Analysis
line_vs_
Obs block geno line check check yield 1 1 C-1 0 C-1 check 2.69 2 1 1 1 0 cand 3.02 3 1 2 2 0 cand 2.52 4 1 3 3 0 cand 3.56 5 1 4 4 0 cand 2.90 6 1 5 5 0 cand 3.60 7 1 6 6 0 cand 3.53 8 1 7 7 0 cand 3.68 9 1 C-2 0 C-2 check 4.02 10 1 8 8 0 cand 3.46 11 1 9 9 0 cand 3.44 12 1 10 10 0 cand 2.91 13 1 11 11 0 cand 2.69 14 1 12 12 0 cand 4.59 15 1 13 13 0 cand 2.24 16 1 14 14 0 cand 3.86 17 1 C-3 0 C-3 check 3.92 18 1 15 15 0 cand 4.01 ---dan
seterusnya---146 5 127 127 0 cand 3.68 147 5 128 128 0 cand 3.94 148 5 129 129 0 cand 3.75 149 5 130 130 0 cand 2.70 150 5 131 131 0 cand 3.99 151 5 132 132 0 cand 3.26 152 5 133 133 0 cand 3.89 153 5 C-4 0 C-4 check 2.70 154 5 134 134 0 cand 3.36 155 5 135 135 0 cand 3.77 156 5 136 136 0 cand 3.76 157 5 137 137 0 cand 2.71 158 5 138 138 0 cand 2.81 159 5 139 139 0 cand 2.48 160 5 140 140 0 cand 3.14
ANOVA for Augmented Design - RCBD - Block Adjusted and Whole-Treatment Adjusted The GLM Procedure
Class Level Information Class Levels Values
block 5 1 2 3 4 5
60 61 62 63 64 65 66 67 68 69 7 70 71 72 73 74 75 76 77 78 79 8 80 81 82 83 84 85 86 87 88 89 9 90 91 92 93 94 95 96 97 98 99 C-1 C-2 C-3 C-4
Number of observations 160
ANOVA for Augmented Design - RCBD - Block Adjusted and Whole-Treatment Adjusted
The GLM Procedure Dependent Variable: yield Sum of
Source DF Squares Mean Square F Value Pr > F Model 147 48.73268437 0.33151486 4.09 0.0045 Error 12 0.97271000 0.08105917
Corrected Total 159 49.70539437 R-Square Coeff Var Root MSE yield Mean 0.980430 8.983656 0.284709 3.169188
Source DF Type III SS Mean Square F Value Pr > F block 4 0.38957000 0.09739250 1.20 0.3599 geno 143 43.55966813 0.30461306 3.76 0.0067
ANOVA for Augmented Design - RCBD - Treatment-Partitions Adjusted
The GLM Procedure
Class Level Information Class Levels Values
block 5 1 2 3 4 5 line_vs_check 2 cand check check 5 0 C-1 C-2 C-3 C-4
line 141 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
Number of observations 160
Dependent Variable: yield Sum of
Source DF Squares Mean Square F Value Pr > F Model 147 48.73268438 0.33151486 4.09 0.0045 Error 12 0.97271000 0.08105917
Corrected Total 159 49.70539437 R-Square Coeff Var Root MSE yield Mean 0.980430 8.983656 0.284709 3.169188
Source DF Type I SS Mean Square F Value Pr > F block 4 5.17301625 1.29325406 15.95 <.0001 line_vs_check 1 0.05988937 0.05988937 0.74 0.4069 check 3 5.56964000 1.85654667 22.90 <.0001 line(check) 139 37.93013875 0.27287870 3.37 0.0110
PROC MIXED for calculating LSMEANS
The Mixed Procedure Model Information Data Set WORK.AUGRCBD Dependent Variable yield Covariance Structure Diagonal Estimation Method REML Residual Variance Method Profile Fixed Effects SE Method Model-Based Degrees of Freedom Method Residual
Class Level Information Class Levels Values
block 5 1 2 3 4 5 line_vs_check 2 cand check check 5 0 C-1 C-2 C-3 C-4 line 141 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
Dimensions
Columns in X 155
Columns in Z 0
Subjects 1
Max Obs Per Subject 160
Observations Used 160
Observations Not Used 0
Total Observations 160
Covariance Parameter Estimates Cov Parm Estimate Residual 0.08106
Type 3 Tests of Fixed Effects Num Den
Effect DF DF F Value Pr > F block 4 12 1.20 0.3599 check 4 12 17.36 <.0001 line(check) 139 12 3.37 0.0110
Adjusted Means and Standard Error Associated Adj
142 39 0 2.0230 0.3119 143 24 0 1.9555 0.3119 144 27 0 1.7755 0.3119
LSD, Grand Mean, and CV Standard_ Grand_
Obs Error Den_DF Mean t LSD CV
1 0.43512 12 3.17488 2.17881 0.94805 13.7051
LISTING SAS-AUGMENTED DESIGN-RAL
/* Augmented Design in a Randomized Complete Design */ /* Original script written by Mateo Vargas */
/* Modified by Willy B. Suwarno [bayuardi@gmail.com] */
/* Reference: Patersen, R. G. 1994. Agricultural Field Experiments */ /* Date: 10 October 2014, Modified: 16 November 2016 */
/* JANGAN RUNNING SEKALIGUS */
/* Run satu-persatu anova1 lalu anova 2 lanjut uji LSD */
/* clear log and output */ dm 'log; clear; output; clear';
options pagesize=max formdlim='-' nocenter nonumber nodate;
/* define data set (can be copy-pasted from Excel) */
/* note that there are $ signs after geno, check, and line_vs_check variables' name */ /* indicating that the variables should be read as string (not numeric) */
/* because the name of the checks are not numbers */ data augral;
input
geno$ line check$ line_vs_check$ yield;
cards;
C-1 0 C-1 check 2.69
1 1 0 cand 3.02
2 2 0 cand 2.52
3 3 0 cand 3.56
4 4 0 cand 2.90
5 5 0 cand 3.60
6 6 0 cand 3.53
7 7 0 cand 3.68
C-2 0 C-2 check 4.02
8 8 0 cand 3.46
9 9 0 cand 3.44
10 10 0 cand 2.91
11 11 0 cand 2.69
12 12 0 cand 4.59
13 13 0 cand 2.24
14 14 0 cand 3.86
C-3 0 C-3 check 3.92
15 15 0 cand 4.01
16 16 0 cand 2.60
17 17 0 cand 3.47
18 18 0 cand 2.49
19 19 0 cand 2.93
20 20 0 cand 3.49
21 21 0 cand 4.02
C-4 0 C-4 check 2.66
22 22 0 cand 3.82
23 23 0 cand 2.98
24 24 0 cand 2.16
25 25 0 cand 2.74
26 26 0 cand 2.70
27 27 0 cand 1.98
28 28 0 cand 2.44
C-4 0 C-4 check 2.47
29 29 0 cand 2.71
30 30 0 cand 3.14
31 31 0 cand 2.17
33 33 0 cand 3.01
34 34 0 cand 2.99
35 35 0 cand 3.52
C-3 0 C-3 check 3.46
36 36 0 cand 2.51
37 37 0 cand 1.89
38 38 0 cand 3.05
39 39 0 cand 1.85
40 40 0 cand 3.15
41 41 0 cand 3.22
42 42 0 cand 3.21
C-2 0 C-2 check 3.51
43 43 0 cand 2.79
44 44 0 cand 3.41
45 45 0 cand 3.44
46 46 0 cand 3.04
47 47 0 cand 2.89
48 48 0 cand 2.85
49 49 0 cand 2.88
C-1 0 C-1 check 2.34
50 50 0 cand 3.10
51 51 0 cand 3.02
52 52 0 cand 2.77
53 53 0 cand 3.05
54 54 0 cand 2.78
55 55 0 cand 2.00
56 56 0 cand 2.03
C-1 0 C-1 check 2.52
57 57 0 cand 3.16
58 58 0 cand 3.12
59 59 0 cand 2.24
60 60 0 cand 3.82
61 61 0 cand 3.29
62 62 0 cand 3.93
63 63 0 cand 3.67
C-4 0 C-4 check 2.79
64 64 0 cand 2.89
65 65 0 cand 3.43
66 66 0 cand 3.46
67 67 0 cand 3.81
68 68 0 cand 3.06
69 69 0 cand 3.06
70 70 0 cand 3.70
C-3 0 C-3 check 3.57
71 71 0 cand 3.31
72 72 0 cand 4.22
73 73 0 cand 2.41
74 74 0 cand 2.94
75 75 0 cand 2.59
76 76 0 cand 3.27
77 77 0 cand 2.91
C-2 0 C-2 check 3.55
78 78 0 cand 3.58
79 79 0 cand 3.05
80 80 0 cand 2.50
81 81 0 cand 2.28
82 82 0 cand 3.52
83 83 0 cand 2.87
84 84 0 cand 3.03
C-2 0 C-2 check 4.20
85 85 0 cand 3.46
86 86 0 cand 3.18
87 87 0 cand 3.76
88 88 0 cand 3.07
89 89 0 cand 3.49
90 90 0 cand 3.73
91 91 0 cand 4.10
C-1 0 C-1 check 2.39
92 92 0 cand 2.97
93 93 0 cand 2.53
94 94 0 cand 3.76
95 95 0 cand 3.11
96 96 0 cand 3.68
97 97 0 cand 3.30
98 98 0 cand 4.29
C-4 0 C-4 check 3.31
99 99 0 cand 3.90
100 100 0 cand 3.35
101 101 0 cand 3.07
102 102 0 cand 2.45
103 103 0 cand 3.48
104 104 0 cand 3.41
105 105 0 cand 3.06
106 106 0 cand 2.56
107 107 0 cand 3.07
108 108 0 cand 2.67
109 109 0 cand 2.97
110 110 0 cand 3.09
111 111 0 cand 3.32
112 112 0 cand 2.72
C-3 0 C-3 check 3.20
113 113 0 cand 3.43
114 114 0 cand 3.10
115 115 0 cand 3.38
116 116 0 cand 3.47
117 117 0 cand 3.66
118 118 0 cand 4.11
119 119 0 cand 3.50
C-2 0 C-2 check 3.74
120 120 0 cand 3.90
121 121 0 cand 3.03
122 122 0 cand 3.82
123 123 0 cand 3.19
124 124 0 cand 4.73
125 125 0 cand 2.65
126 126 0 cand 3.52
C-1 0 C-1 check 2.34
127 127 0 cand 3.68
128 128 0 cand 3.94
129 129 0 cand 3.75
130 130 0 cand 2.70
131 131 0 cand 3.99
132 132 0 cand 3.26
133 133 0 cand 3.89
C-4 0 C-4 check 2.70
134 134 0 cand 3.36
135 135 0 cand 3.77
136 136 0 cand 3.76
137 137 0 cand 2.71
138 138 0 cand 2.81
139 139 0 cand 2.48
140 140 0 cand 3.14
;
/* print data */ proc print data=augral;
title1 'Plot Data for Analysis'; run;
/* perform anova for the block and treatment (as a whole) effects */ proc glm data=augral;
class geno;
model yield = geno /ss3;
title1 'ANOVA for Augmented Design - RAL - Rancangan Acak Lengkap'; run;
/* perform anova for partitioning the treatment effect */ proc glm data=augral;
class line_vs_check check line;
model yield = line_vs_check check line(check) /ss1;
title1 'ANOVA for Augmented Design - RAL - Treatment-Partitions Adjusted'; run;
/* calculate adjusted means (lsmeans) using PROC MIXED */ proc mixed data=augral;
class line_vs_check check line; model yield = check line(check); lsmeans line(check) /pdiff;
ods output lsmeans=LSMEANS diffs=DIFFS tests3=DOF; ods listing exclude lsmeans diffs FitStatistics; title1 'PROC MIXED for calculating LSMEANS'; run;
/*
proc print data=lsmeans; run;
*/
/* prepare a list of adjusted means */ data adjmeans;
set lsmeans; AdjMean = Estimate;
keep line check AdjMean StdErr; run;
by descending AdjMean; run;
proc print data=adjmeans; var line check AdjMean StdErr;
title1 'Adjusted Means and Standard Error Associated'; run;
/* calculate LSD, grand mean, and CV */ proc means data=DIFFS mean noprint; output out=StdErrInd;
var StdErr; run;
data StandardError; set StdErrInd; if _stat_='MEAN'; Standard_Error=StdErr; keep Standard_Error; run;
proc means data=lsmeans mean noprint; output out=lmeansInd;
data GrandMean; set lmeansInd; if _stat_='MEAN'; Grand_Mean=Estimate; keep Grand_Mean; run;
data DOF1; set DOF;
effect=lowcase(effect); if effect ='line(check)'; Den_DF=DenDF;
keep Den_DF; run;
data LSD_CV;
merge StandardError DOF1 GrandMean; t=tinv(1-0.05/2,Den_DF);
LSD=t*Standard_Error;
CV=(Standard_Error/Grand_Mean)*100; run;
title1 'LSD, Grand Mean, and CV'; proc print Data = LSD_CV; run;
/* end of code */
OUTPUT SAS-AUGMENTED DESIGN-RAL
Plot Data for Analysis line_vs_
153 C-4 0 C-4 check 2.70 154 134 134 0 cand 3.36 155 135 135 0 cand 3.77 156 136 136 0 cand 3.76 157 137 137 0 cand 2.71 158 138 138 0 cand 2.81 159 139 139 0 cand 2.48 160 140 140 0 cand 3.14
ANOVA for Augmented Design - RAL - Rancangan Acak Lengkap
The GLM Procedure
Class Level Information Class Levels Values
geno 144 1 10 100 101 102 103 104 105 106 107 108 109 11 110 111 112 113 114 115 116 117 118 119 12 120 121 122 123 124 125 126 127 128 129 13 130 131 132 133 134 135 136 137 138 139 14 140 15 16 17 18 19 2 20 21 22 23 24 25 26 27 28 29 3 30 31 32 33 34 35 36 37 38 39 4 40 41 42 43 44 45 46 47 48 49 5 50 51 52 53 54 55 56 57 58 59 6 60 61 62 63 64 65 66 67 68 69 7 70 71 72 73 74 75 76 77 78 79 8 80 81 82 83 84 85 86 87 88 89 9 90 91 92 93 94 95 96 97 98 99 C-1 C-2 C-3 C-4
Number of observations 160 Dependent Variable: yield Sum of
Source DF Squares Mean Square F Value Pr > F Model 143 48.34311438 0.33806374 3.97 0.0014 Error 16 1.36228000 0.08514250
Corrected Total 159 49.70539437 R-Square Coeff Var Root MSE yield Mean 0.972593 9.207151 0.291792 3.169188
Source DF Type III SS Mean Square F Value Pr > F geno 143 48.34311437 0.33806374 3.97 0.0014
Class Level Information Class Levels Values
line_vs_check 2 cand check check 5 0 C-1 C-2 C-3 C-4
line 141 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 Number of observations 160
ANOVA for Augmented Design - RAL - Treatment-Partitions Adjusted
The GLM Procedure Dependent Variable: yield Sum of
Source DF Squares Mean Square F Value Pr > F Model 143 48.34311438 0.33806374 3.97 0.0014 Error 16 1.36228000 0.08514250
Corrected Total 159 49.70539437 R-Square Coeff Var Root MSE yield Mean 0.972593 9.207151 0.291792 3.169188
Source DF Type I SS Mean Square F Value Pr > F line_vs_check 1 0.05988937 0.05988937 0.70 0.4140 check 3 5.56964000 1.85654667 21.81 <.0001 line(check) 139 42.71358500 0.30729198 3.61 0.0025
PROC MIXED for calculating LSMEANS The Mixed Procedure
Model Information Data Set WORK.AUGRAL Dependent Variable yield Covariance Structure Diagonal Estimation Method REML Residual Variance Method Profile Fixed Effects SE Method Model-Based Degrees of Freedom Method Residual
Class Level Information Class Levels Values line_vs_check 2 cand check check 5 0 C-1 C-2 C-3 C-4
43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
Dimensions
Covariance Parameters 1
Columns in X 150
Columns in Z 0
Subjects 1
Max Obs Per Subject 160
Observations Used 160
Observations Not Used 0
Total Observations 160
Covariance Parameter Estimates Cov Parm Estimate Residual 0.08514
Type 3 Tests of Fixed Effects Num Den
Effect DF DF F Value Pr > F check 4 16 16.53 <.0001 line(check) 139 16 3.61 0.0025
Adjusted Means and Standard Error Associated Adj
128 80 0 2.500 0.2918 129 18 0 2.490 0.2918 130 139 0 2.480 0.2918 131 0 C-1 2.456 0.1305 132 102 0 2.450 0.2918 133 28 0 2.440 0.2918 134 73 0 2.410 0.2918 135 81 0 2.280 0.2918 136 59 0 2.240 0.2918 137 13 0 2.240 0.2918 138 31 0 2.170 0.2918 139 24 0 2.160 0.2918 140 56 0 2.030 0.2918 141 55 0 2.000 0.2918 142 27 0 1.980 0.2918 143 37 0 1.890 0.2918 144 39 0 1.850 0.2918
LSD, Grand Mean, and CV
Standard_ Grand_
Obs Error Den_DF Mean t LSD CV 1 0.40746 16 3.17488 2.11991 0.86379 12.8340
LISTING R PROGRAM-AUGMENTED DESIGN-RCBD
library(agricolae)
# 4 treatments and 5 blocks T1<-c("C-1","C-2","C-3","C-4") T1
T2<-c(1:140) T2
outdesign<-design.dau(T1,T2,r=5,serie=2) # field book
book<-outdesign$book
by(book,book[2],function(x) paste(x[,1],"-",as.character(x[,3]))) # write in hard disk
# write.table(book,"dau.txt", row.names=FALSE, sep="\t") # file.show("dau.txt")
# Augmented designs in Completely Randomized Design rtrt<-c(T1,T2)
r<-c(4,4,4,4,4) outdesign$book
OUTPUT R PROGRAM-AUGMENTED DESIGN-RCBD
> library(agricolae)
> # 4 treatments and 5 blocks > T1<-c("C-1","C-2","C-3","C-4") > T1
[1] "C-1" "C-2" "C-3" "C-4" > T2<-c(1:140)
> T2
[91] 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 [109] 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 [127] 127 128 129 130 131 132 133 134 135 136 137 138 139 140
> outdesign<-design.dau(T1,T2,r=5,serie=2) [25] "225 - C-4" "226 - 79" "227 - 136" "228 - 111" "229 - 128" "230 - 116" [31] "231 - 127" "232 - C-2"
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