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ltcs: Analysis of Line x Tester data on single plant basis containing only Crosses laid out in RCBD or Alpha Lattice design.

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

Example: Analyzing Line x Tester data (crosses) based on single plant observations laid out in RCBD.

# 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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.