Analyzing the line by tester data single plant observations evaluated in RCBD and Alpha lattice design. All the factors are considered as fixed.
w Analyzing Line x Tester data (crosses) based on single plant observations laid out in Alpha Lattice design.
# Load the package
library(gpbStat)
#Load the dataset
data("alphaltcs")
# Conduct Line x Tester analysis on single plant basis
result = ltcs(alphaltcs, replication, line, tester, obs, yield, block)
#> 
#> Analysis of Line x Tester on single plant basis: yield
# View the output
result
#> $Means
#>          Testers
#> Lines     DIL - 102  DIL 101 DIL 103
#>   DIL - 2  102.1350 88.25000 51.9725
#>   DIL 1     87.0875 79.15667 78.7075
#>   DIL-3    112.6950 67.84800 90.4550
#>   DIL-5     95.2250 98.95250 90.9700
#>   DIL4     115.0944 74.95250 80.9000
#> 
#> $`Overall ANOVA`
#>                            Df     Sum Sq    Mean Sq    F value       Pr(>F)
#> Replication                 3   3106.007  1035.3357   7.796336 5.906947e-05
#> Blocks within Replication  16  32900.156  2056.2598  15.484148 2.314397e-27
#> Crosses                    14  62242.942  4445.9244  16.481469 1.963948e-27
#> Lines                       4   6930.860  1732.7151  13.162521 1.514446e-09
#> Testers                     2  27283.744 13641.8721 103.630090 1.067923e-31
#> Lines X Testers             8  28028.337  3503.5422  12.987968 2.356614e-15
#> Replication x Hybrids      20  33665.196  1683.2598  12.786835 3.473874e-26
#> Error                     202  26591.294   131.6401         NA           NA
#> Total                     239 122937.349         NA         NA           NA
#> 
#> $`Coefficient of Variation`
#> [1] 13.09663
#> 
#> $`Line x Tester ANOVA`
#>                       Df   Sum Sq    Mean Sq   F value       Pr(>F)
#> Line                   4  6930.86  1732.7151  6.423341 6.530042e-05
#> Tester                 2 27283.74 13641.8721 50.571731 7.278756e-19
#> Line x Tester          8 28028.34  3503.5422 12.987968 2.356614e-15
#> Replication x Hybrid  20 33665.20  1683.2598 12.786835 3.473874e-26
#> Error                202 26591.29   131.6401        NA           NA
#> 
#> $`GCA lines`
#> DIL - 2   DIL 1   DIL-3   DIL-5    DIL4 
#>   -6.82   -5.73    1.00    7.13    3.57 
#> 
#> $`GCA testers`
#> DIL - 102   DIL 101   DIL 103 
#>     14.84     -6.34     -8.50 
#> 
#> $`SCA crosses`
#>          Testers
#> Lines      DIL - 102    DIL 101     DIL 103
#>   DIL - 2   6.528561  13.259003 -19.7875639
#>   DIL 1    -9.383661   3.300947   6.0827139
#>   DIL-3     7.541728 -16.689831   9.1481028
#>   DIL-5   -14.644772   9.698169   4.9466028
#>   DIL4      9.958144  -9.568289  -0.3898556
#> 
#> $`Proportional Contribution`
#>          Lines         Tester  Line x Tester 
#>      189.18737       21.06635       82.92892 
#> 
#> $`Critical differance`
#>      C.D. gca for line    C.D. gca for tester        C.D. sca effect 
#>               27.31147               21.15537               47.30485 
#>     C.D. (gi - gj)line   C.D. (gi - gj)tester C.D. (sij - skl)tester 
#>               38.62425               29.91821               66.89916 
#> 
#> $`Least Square Estimates`
#> GCA variance SCA Variance 
#>    1181.7889    -608.9477# Load the package
library(gpbStat)
#Load the dataset
data("rcbdltcs")
# Conduct Line x Tester analysis on single plant basis
result1 = ltcs(rcbdltcs, replication, line, tester, obs, yield)
#> 
#> Analysis of Line x Tester on single plant basis: yield
# View the output
result1
#> $Means
#>          Testers
#> Lines      DIL 101 DIL 102 DIL-103
#>   DIL - 1  87.0875 89.6500 78.7075
#>   DIL - 3  95.2250 98.9525 90.8200
#>   DIL - 5 102.1350 88.7000 51.9725
#>   DIL 2   112.6950 54.0775 90.4550
#>   DIL 4   115.0944 74.9525 83.5675
#> 
#> $`Overall ANOVA`
#>                        Df      Sum Sq      Mean Sq     F value        Pr(>F)
#> Replication             3   3552.3441  1184.114704    5.467401  1.211488e-03
#> Crosses                14  71304.8550  5093.203929   22.194761  5.508006e-35
#> Lines                   4   5837.4766  1459.369150  367.626329  1.988277e-85
#> Testers                 2  26618.4939 13309.246974 3352.701824 3.638136e-143
#> Lines X Testers         8  38848.8845  4856.110557   21.161574  7.997773e-24
#> Replication x Hybrids  42  47365.6030  1127.752451  284.089529 1.000267e-143
#> Error                 180    714.5474     3.969708          NA            NA
#> Total                 239 122937.3495           NA          NA            NA
#> 
#> $`Coefficient of Variation`
#> [1] 2.274285
#> 
#> $`Line x Tester ANOVA`
#>                       Df     Sum Sq      Mean Sq    F value        Pr(>F)
#> Line                   4  5837.4766  1459.369150   6.359523  7.262897e-05
#> Tester                 2 26618.4939 13309.246974  57.997984  4.855606e-21
#> Line x Tester          8 38848.8845  4856.110557  21.161574  7.997773e-24
#> Replication x Hybrid  42 47365.6030  1127.752451 284.089529 1.000267e-143
#> Error                180   714.5474     3.969708         NA            NA
#> 
#> $`GCA lines`
#> DIL - 1 DIL - 3 DIL - 5   DIL 2   DIL 4 
#>   -2.46    7.39   -6.67   -1.86    3.60 
#> 
#> $`GCA testers`
#> DIL 101 DIL 102 DIL-103 
#>   14.84   -6.34   -8.50 
#> 
#> $`SCA crosses`
#>          Testers
#> Lines        DIL 101    DIL 102     DIL-103
#>   DIL - 1 -12.902083  10.841292   2.0607917
#>   DIL - 3 -14.615417  10.292958   4.3224583
#>   DIL - 5   6.357917  14.103792 -20.4617083
#>   DIL 2    12.111250 -25.325375  13.2141250
#>   DIL 4     9.048333  -9.912667   0.8643333
#> 
#> $`Proportional Contribution`
#>          Lines         Tester  Line x Tester 
#>       8.186647      37.330549      54.482804 
#> 
#> $`Critical differance`
#>      C.D. gca for line    C.D. gca for tester        C.D. sca effect 
#>               19.56388               15.15412               33.88564 
#>     C.D. (gi - gj)line   C.D. (gi - gj)tester C.D. (sij - skl)tester 
#>               27.66751               21.43116               47.92153 
#> 
#> $`Least Square Estimates`
#> GCA variance SCA Variance 
#>    1108.8276     366.9422