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The function ltc conducts Line x Tester analysis when
the data contains only crosses. The experimental design may be
RCBD or Alpha lattice design.
# Load the package
library(gpbStat)
#Load the dataset
data("alphaltc")
# View the structure of dataframe.
str(alphaltc)
#> 'data.frame': 60 obs. of 5 variables:
#> $ replication: chr "r1" "r1" "r1" "r1" ...
#> $ block : chr "b1" "b1" "b1" "b2" ...
#> $ line : int 5 1 4 4 1 2 2 5 3 1 ...
#> $ tester : int 7 8 8 6 7 7 6 6 8 6 ...
#> $ yield : num 47.3 109.4 36.3 36.2 70.7 ...
# Conduct Line x Tester analysis
result = ltc(alphaltc, replication, line, tester, yield, block)
#>
#> Analysis of Line x Tester: yield
# View the output
result
#> $Means
#> Testers
#> Lines 6 7 8
#> 1 86.47500 88.95833 89.55000
#> 2 88.64667 55.48000 50.12667
#> 3 51.19917 53.28417 36.91583
#> 4 33.47500 34.29833 50.78417
#> 5 45.30417 42.14500 49.98000
#>
#> $`Overall ANOVA`
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 3 1586.4934 528.8311 3.1440495 4.213104e-02
#> Crosses 14 23862.0199 1704.4300 10.1333150 3.161969e-07
#> Blocks within Replication 16 2555.9198 159.7450 0.9497288 5.307851e-01
#> Lines 4 18835.3119 4708.8280 24.8833344 6.536498e-11
#> Testers 2 463.1458 231.5729 1.2237239 3.037332e-01
#> Lines X Testers 8 4563.5622 570.4453 3.0144615 8.508293e-03
#> Error 26 4373.2165 168.2006 NA NA
#> Total 59 2561.2067 NA NA NA
#>
#> $`Coefficient of Variation`
#> [1] 22.70992
#>
#> $`Genetic Variance`
#> Genotypic Variance Phenotypic Variance Environmental Variance
#> 293.8997 462.1004 168.2006
#>
#> $`Genetic Variability `
#> Phenotypic coefficient of Variation Genotypic coefficient of Variation
#> 37.6417608 30.0193557
#> Environmental coefficient of Variation <NA>
#> 22.7099195 0.6360084
#>
#> $`Line x Tester ANOVA`
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Lines 4 18835.3119 4708.8280 24.883334 6.536498e-11
#> Testers 2 463.1458 231.5729 1.223724 3.037332e-01
#> Lines X Testers 8 4563.5622 570.4453 3.014461 8.508293e-03
#> Error 26 4373.2165 168.2006 NA NA
#>
#> $`GCA lines`
#> 1 2 3 4 5
#> 31.220 7.643 -9.975 -17.589 -11.298
#>
#> $`GCA testers`
#> 6 7 8
#> 3.912 -2.275 -1.637
#>
#> $`SCA crosses`
#> Testers
#> Lines 6 7 8
#> 1 -5.765 2.906 2.859
#> 2 19.984 -6.996 -12.988
#> 3 0.154 8.426 -8.580
#> 4 -9.956 -2.946 12.902
#> 5 -4.417 -1.390 5.807
#>
#> $`Proportional Contribution`
#> Lines Tester Line x Tester
#> 78.934273 1.940933 19.124794
#>
#> $`GV Singh & Chaudhary`
#> Cov H.S. (line) Cov H.S. (tester)
#> 344.86523 -16.94362
#> Cov H.S. (average) Cov F.S. (average)
#> 30.06778 262.35565
#> F = 0, Adittive genetic variance F = 1, Adittive genetic variance
#> 120.27111 60.13555
#> F = 0, Variance due to Dominance F = 1, Variance due to Dominance
#> 201.12232 15.84306
#>
#> $`Standard Errors`
#> S.E. gca for line S.E. gca for tester S.E. sca effect
#> 3.743891 2.900005 6.484609
#> S.E. (gi - gj)line S.E. (gi - gj)tester S.E. (sij - skl)tester
#> 5.294661 4.101227 9.170622
#>
#> $`Critical differance`
#> C.D. gca for line C.D. gca for tester C.D. sca effect
#> 7.695678 5.961047 13.329305
#> C.D. (gi - gj)line C.D. (gi - gj)tester C.D. (sij - skl)tester
#> 10.883332 8.430193 18.850484# Load the package
library(gpbStat)
#Load the dataset
data("rcbdltc")
# View the structure of dataframe.
str(rcbdltc)
#> tibble [60 × 4] (S3: tbl_df/tbl/data.frame)
#> $ replication: num [1:60] 1 2 3 4 1 2 3 4 1 2 ...
#> $ line : num [1:60] 1 1 1 1 1 1 1 1 1 1 ...
#> $ tester : num [1:60] 6 6 6 6 7 7 7 7 8 8 ...
#> $ yield : num [1:60] 74.4 70.9 60.9 68 91.8 ...
# Conduct Line x Tester analysis
result1 = ltc(rcbdltc, replication, line, tester, yield)
#>
#> Analysis of Line x Tester: yield
# View the output
result1
#> $Means
#> Testers
#> Lines 6 7 8
#> 1 68.550 107.640 52.640
#> 2 73.265 97.640 85.650
#> 3 100.885 111.540 117.735
#> 4 105.795 64.450 46.855
#> 5 84.150 81.935 94.820
#>
#> $`Overall ANOVA`
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Replication 3 148.436 49.47866 0.509612 6.778194e-01
#> Crosses 14 26199.654 1871.40388 19.274772 6.737492e-14
#> Lines 4 10318.361 2579.59035 27.466791 1.421271e-11
#> Testers 2 1718.926 859.46289 9.151332 4.626865e-04
#> Lines X Testers 8 14162.367 1770.29589 18.849639 4.973396e-12
#> Error 42 4077.815 97.09084 NA NA
#> Total 59 30425.906 NA NA NA
#>
#> $`Coefficient of Variation`
#> [1] 11.42608
#>
#> $`Genetic Variance`
#> Genotypic Variance Phenotypic Variance Environmental Variance
#> 455.48131 552.57215 97.09084
#>
#> $`Genetic Variability `
#> Phenotypic coefficient of Variation Genotypic coefficient of Variation
#> 27.2585365 24.7481829
#> Environmental coefficient of Variation <NA>
#> 11.4260778 0.8242929
#>
#> $`Line x Tester ANOVA`
#> Df Sum Sq Mean Sq F value Pr(>F)
#> Lines 4 10318.361 2579.59035 27.466791 1.421271e-11
#> Testers 2 1718.926 859.46289 9.151332 4.626865e-04
#> Lines X Testers 8 14162.367 1770.29589 18.849639 4.973396e-12
#> Error 42 4077.815 97.09084 NA NA
#>
#> $`GCA lines`
#> 1 2 3 4 5
#> -9.960 -0.718 23.817 -13.870 0.732
#>
#> $`GCA testers`
#> 6 7 8
#> 0.292 6.404 -6.697
#>
#> $`SCA crosses`
#> Testers
#> Lines 6 7 8
#> 1 -8.019 24.959 -16.940
#> 2 -12.546 5.717 6.828
#> 3 -9.461 -4.918 14.378
#> 4 33.136 -14.321 -18.815
#> 5 -3.111 -11.438 14.548
#>
#> $`Proportional Contribution`
#> Lines Tester Line x Tester
#> 39.383578 6.560872 54.055550
#>
#> $`GV Singh & Chaudhary`
#> Cov H.S. (line) Cov H.S. (tester)
#> 67.441205 -45.541650
#> Cov H.S. (average) Cov F.S. (average)
#> 2.680894 408.052454
#> F = 0, Adittive genetic variance F = 1, Adittive genetic variance
#> 10.723574 5.361787
#> F = 0, Variance due to Dominance F = 1, Variance due to Dominance
#> 836.602526 418.301263
#>
#> $`Standard Errors`
#> S.E. gca for line S.E. gca for tester S.E. sca effect
#> 2.844451 2.203303 4.926734
#> S.E. (gi - gj)line S.E. (gi - gj)tester S.E. (sij - skl)tester
#> 4.022662 3.115940 6.967454
#>
#> $`Critical differance`
#> C.D. gca for line C.D. gca for tester C.D. sca effect
#> 5.740335 4.446445 9.942552
#> C.D. (gi - gj)line C.D. (gi - gj)tester C.D. (sij - skl)tester
#> 8.118060 6.288222 14.060892These 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.
Health stats visible at Monitor.