Reads inst/extdata/reference.csv: one row per published number, with its source, the R call
that recomputes it with this package (call), the expression that extracts the number from the result
(extract, in terms of x), the printed value and the digits to compare. The same file drives the test
of published values and the vignette that reproduces the published examples. Each distinct call is evaluated once.
Value
A data.table with columns id, source, quantity, call, extract,
value, digits, level, note (and computed, agrees).
See also
Other DiallelAnalysisR utilities:
da_export(),
da_notation(),
da_save(),
da_theme()
Author
Muhammad Yaseen (myaseen208@gmail.com)
Examples
ref <-
da_reference(
source = NULL
, evaluate = FALSE
, level = 1
)
ref
#> id source quantity
#> <char> <char> <char>
#> 1: g56_X1_ms_gca Griffing (1956), Table 8 X1 mean square gca (Model I)
#> 2: g56_X1_ms_sca Griffing (1956), Table 8 X1 mean square sca (Model I)
#> 3: g56_X1_comp_gca Griffing (1956), Table 8 X1 gca component (Model I)
#> 4: g56_X1_comp_sca Griffing (1956), Table 8 X1 sca component (Model I)
#> 5: g56_X1_g1 Griffing (1956), Table 9 X1 gca effect of parent 1
#> 6: g56_X1_g2 Griffing (1956), Table 9 X1 gca effect of parent 2
#> 7: g56_X1_g3 Griffing (1956), Table 9 X1 gca effect of parent 3
#> 8: g56_X1_g4 Griffing (1956), Table 9 X1 gca effect of parent 4
#> 9: g56_X1_g5 Griffing (1956), Table 9 X1 gca effect of parent 5
#> 10: g56_X1_g6 Griffing (1956), Table 9 X1 gca effect of parent 6
#> 11: g56_X1_g7 Griffing (1956), Table 9 X1 gca effect of parent 7
#> 12: g56_X1_g8 Griffing (1956), Table 9 X1 gca effect of parent 8
#> 13: g56_X1_g9 Griffing (1956), Table 9 X1 gca effect of parent 9
#> 14: g56_X1_se_gg Griffing (1956), Table 9 X1 SE(g_i - g_j)
#> 15: g56_X1_se_sik Griffing (1956), Table 10 X1 SE(s_ij - s_ik)
#> 16: g56_X1_se_skl Griffing (1956), Table 10 X1 SE(s_ij - s_kl)
#> 17: g56_X1_s12 Griffing (1956), Table 10 X1 sca effect of cross 1 x 2
#> 18: g56_X1_s13 Griffing (1956), Table 10 X1 sca effect of cross 1 x 3
#> 19: g56_X1_s19 Griffing (1956), Table 10 X1 sca effect of cross 1 x 9
#> 20: g56_X1_s29 Griffing (1956), Table 10 X1 sca effect of cross 2 x 9
#> 21: g56_X1_s34 Griffing (1956), Table 10 X1 sca effect of cross 3 x 4
#> 22: g56_X1_s49 Griffing (1956), Table 10 X1 sca effect of cross 4 x 9
#> 23: g56_X1_s56 Griffing (1956), Table 10 X1 sca effect of cross 5 x 6
#> 24: g56_X1_s68 Griffing (1956), Table 10 X1 sca effect of cross 6 x 8
#> 25: g56_X1_s89 Griffing (1956), Table 10 X1 sca effect of cross 8 x 9
#> 26: g56_X1_m2_2sg Griffing (1956), Table 13 X1 2 sigma^2_g (Model II)
#> 27: g56_X1_m2_ss Griffing (1956), Table 13 X1 sigma^2_s (Model II)
#> 28: g56_X2_ms_gca Griffing (1956), Table 8 X2 mean square gca (Model I)
#> 29: g56_X2_ms_sca Griffing (1956), Table 8 X2 mean square sca (Model I)
#> 30: g56_X2_comp_gca Griffing (1956), Table 8 X2 gca component (Model I)
#> 31: g56_X2_comp_sca Griffing (1956), Table 8 X2 sca component (Model I)
#> 32: g56_X2_g1 Griffing (1956), Table 9 X2 gca effect of parent 1
#> 33: g56_X2_g2 Griffing (1956), Table 9 X2 gca effect of parent 2
#> 34: g56_X2_g3 Griffing (1956), Table 9 X2 gca effect of parent 3
#> 35: g56_X2_g4 Griffing (1956), Table 9 X2 gca effect of parent 4
#> 36: g56_X2_g5 Griffing (1956), Table 9 X2 gca effect of parent 5
#> 37: g56_X2_g6 Griffing (1956), Table 9 X2 gca effect of parent 6
#> 38: g56_X2_g7 Griffing (1956), Table 9 X2 gca effect of parent 7
#> 39: g56_X2_g8 Griffing (1956), Table 9 X2 gca effect of parent 8
#> 40: g56_X2_g9 Griffing (1956), Table 9 X2 gca effect of parent 9
#> 41: g56_X2_se_gg Griffing (1956), Table 9 X2 SE(g_i - g_j)
#> 42: g56_X2_se_sik Griffing (1956), Table 10 X2 SE(s_ij - s_ik)
#> 43: g56_X2_se_skl Griffing (1956), Table 10 X2 SE(s_ij - s_kl)
#> 44: g56_X2_s12 Griffing (1956), Table 10 X2 sca effect of cross 1 x 2
#> 45: g56_X2_s13 Griffing (1956), Table 10 X2 sca effect of cross 1 x 3
#> 46: g56_X2_s29 Griffing (1956), Table 10 X2 sca effect of cross 2 x 9
#> 47: g56_X2_s34 Griffing (1956), Table 10 X2 sca effect of cross 3 x 4
#> 48: g56_X2_s49 Griffing (1956), Table 10 X2 sca effect of cross 4 x 9
#> 49: g56_X2_s56 Griffing (1956), Table 10 X2 sca effect of cross 5 x 6
#> 50: g56_X2_s68 Griffing (1956), Table 10 X2 sca effect of cross 6 x 8
#> 51: g56_X2_m2_2sg Griffing (1956), Table 13 X2 2 sigma^2_g (Model II)
#> 52: g56_X2_m2_ss Griffing (1956), Table 13 X2 sigma^2_s (Model II)
#> 53: g56_X3_ms_gca Griffing (1956), Table 8 X3 mean square gca (Model I)
#> 54: g56_X3_ms_sca Griffing (1956), Table 8 X3 mean square sca (Model I)
#> 55: g56_X3_comp_gca Griffing (1956), Table 8 X3 gca component (Model I)
#> 56: g56_X3_comp_sca Griffing (1956), Table 8 X3 sca component (Model I)
#> 57: g56_X3_g1 Griffing (1956), Table 9 X3 gca effect of parent 1
#> 58: g56_X3_g2 Griffing (1956), Table 9 X3 gca effect of parent 2
#> 59: g56_X3_g3 Griffing (1956), Table 9 X3 gca effect of parent 3
#> 60: g56_X3_g4 Griffing (1956), Table 9 X3 gca effect of parent 4
#> 61: g56_X3_g5 Griffing (1956), Table 9 X3 gca effect of parent 5
#> 62: g56_X3_g6 Griffing (1956), Table 9 X3 gca effect of parent 6
#> 63: g56_X3_g7 Griffing (1956), Table 9 X3 gca effect of parent 7
#> 64: g56_X3_g8 Griffing (1956), Table 9 X3 gca effect of parent 8
#> 65: g56_X3_g9 Griffing (1956), Table 9 X3 gca effect of parent 9
#> 66: g56_X3_se_gg Griffing (1956), Table 9 X3 SE(g_i - g_j)
#> 67: g56_X3_se_sik Griffing (1956), Table 10 X3 SE(s_ij - s_ik)
#> 68: g56_X3_se_skl Griffing (1956), Table 10 X3 SE(s_ij - s_kl)
#> 69: g56_X3_s12 Griffing (1956), Table 10 X3 sca effect of cross 1 x 2
#> 70: g56_X3_s13 Griffing (1956), Table 10 X3 sca effect of cross 1 x 3
#> 71: g56_X3_s29 Griffing (1956), Table 10 X3 sca effect of cross 2 x 9
#> 72: g56_X3_s34 Griffing (1956), Table 10 X3 sca effect of cross 3 x 4
#> 73: g56_X3_s49 Griffing (1956), Table 10 X3 sca effect of cross 4 x 9
#> 74: g56_X3_s56 Griffing (1956), Table 10 X3 sca effect of cross 5 x 6
#> 75: g56_X3_s68 Griffing (1956), Table 10 X3 sca effect of cross 6 x 8
#> 76: g56_X3_m2_2sg Griffing (1956), Table 13 X3 2 sigma^2_g (Model II)
#> 77: g56_X3_m2_ss Griffing (1956), Table 13 X3 sigma^2_s (Model II)
#> id source quantity
#> <char> <char> <char>
#> call
#> <char>
#> 1: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 2: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 3: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 4: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 5: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 6: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 7: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 8: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 9: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 10: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 11: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 12: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 13: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 14: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 15: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 16: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 17: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 18: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 19: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 20: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 21: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 22: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 23: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 24: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 25: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 26: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 11803.61 / 78, ErrorDf = 175)
#> 27: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 11803.61 / 78, ErrorDf = 175)
#> 28: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 29: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 30: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 31: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 32: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 33: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 34: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 35: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 36: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 37: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 38: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 39: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 40: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 41: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 42: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 43: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 44: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 45: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 46: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 47: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 48: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 49: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 50: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 51: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 235.82 / 78, ErrorDf = 175)
#> 52: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 235.82 / 78, ErrorDf = 175)
#> 53: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 54: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 55: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 56: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 57: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 58: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 59: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 60: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 61: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 62: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 63: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 64: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 65: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 66: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 67: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 68: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 69: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 70: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 71: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 72: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 73: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 74: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 75: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 76: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 9056.45 / 78, ErrorDf = 175)
#> 77: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 9056.45 / 78, ErrorDf = 175)
#> call
#> <char>
#> extract value digits level
#> <char> <num> <int> <int>
#> 1: x$ANOVA['gca', 'Mean Sq'] 2325.74 2 1
#> 2: x$ANOVA['sca', 'Mean Sq'] 339.44 2 1
#> 3: x$Genetic.Components['gca', 'Components'] 329.24 2 1
#> 4: x$Genetic.Components['sca', 'Components'] 318.39 2 1
#> 5: x$GCA[['1']] 9.35 2 1
#> 6: x$GCA[['2']] -20.65 2 1
#> 7: x$GCA[['3']] -10.16 2 1
#> 8: x$GCA[['4']] -22.55 2 1
#> 9: x$GCA[['5']] -1.51 2 1
#> 10: x$GCA[['6']] -1.89 2 1
#> 11: x$GCA[['7']] 28.12 2 1
#> 12: x$GCA[['8']] -6.93 2 1
#> 13: x$GCA[['9']] 26.22 2 1
#> 14: x$StdErr[['gi_gj']] 2.45 2 1
#> 15: x$StdErr[['sij_sik']] 6.01 2 1
#> 16: x$StdErr[['sij_skl']] 5.48 2 1
#> 17: x$SCA['1', '2'] 4.94 2 1
#> 18: x$SCA['1', '3'] 14.46 2 1
#> 19: x$SCA['1', '9'] 18.47 2 1
#> 20: x$SCA['2', '9'] -37.73 2 1
#> 21: x$SCA['3', '4'] -29.94 2 1
#> 22: x$SCA['4', '9'] 30.97 2 1
#> 23: x$SCA['5', '6'] -36.16 2 1
#> 24: x$SCA['6', '8'] 32.77 2 1
#> 25: x$SCA['8', '9'] -5.85 2 1
#> 26: x$Genetic.Components['Additive', 'Components'] 567.52 2 1
#> 27: x$Genetic.Components['sca', 'Components'] 188.11 2 1
#> 28: x$ANOVA['gca', 'Mean Sq'] 90.98 2 1
#> 29: x$ANOVA['sca', 'Mean Sq'] 6.99 2 1
#> 30: x$Genetic.Components['gca', 'Components'] 12.91 2 1
#> 31: x$Genetic.Components['sca', 'Components'] 6.39 2 1
#> 32: x$GCA[['1']] 0.27 2 1
#> 33: x$GCA[['2']] -5.48 2 1
#> 34: x$GCA[['3']] -3.96 2 1
#> 35: x$GCA[['4']] -2.23 2 1
#> 36: x$GCA[['5']] 6.64 2 1
#> 37: x$GCA[['6']] 2.24 2 1
#> 38: x$GCA[['7']] 0.97 2 1
#> 39: x$GCA[['8']] -0.23 2 1
#> 40: x$GCA[['9']] 1.78 2 1
#> 41: x$StdErr[['gi_gj']] 0.41 2 1
#> 42: x$StdErr[['sij_sik']] 1.01 2 1
#> 43: x$StdErr[['sij_skl']] 0.93 2 1
#> 44: x$SCA['1', '2'] 0.53 2 1
#> 45: x$SCA['1', '3'] 1.91 2 1
#> 46: x$SCA['2', '9'] -4.29 2 1
#> 47: x$SCA['3', '4'] -5.09 2 1
#> 48: x$SCA['4', '9'] 5.27 2 1
#> 49: x$SCA['5', '6'] -5.25 2 1
#> 50: x$SCA['6', '8'] 5.01 2 1
#> 51: x$Genetic.Components['Additive', 'Components'] 24.00 2 1
#> 52: x$Genetic.Components['sca', 'Components'] 3.97 2 1
#> 53: x$ANOVA['gca', 'Mean Sq'] 1948.66 2 1
#> 54: x$ANOVA['sca', 'Mean Sq'] 260.49 2 1
#> 55: x$Genetic.Components['gca', 'Components'] 276.09 2 1
#> 56: x$Genetic.Components['sca', 'Components'] 244.50 2 1
#> 57: x$GCA[['1']] 9.08 2 1
#> 58: x$GCA[['2']] -15.17 2 1
#> 59: x$GCA[['3']] -6.20 2 1
#> 60: x$GCA[['4']] -20.32 2 1
#> 61: x$GCA[['5']] -8.15 2 1
#> 62: x$GCA[['6']] -4.13 2 1
#> 63: x$GCA[['7']] 27.15 2 1
#> 64: x$GCA[['8']] -6.70 2 1
#> 65: x$GCA[['9']] 24.44 2 1
#> 66: x$StdErr[['gi_gj']] 2.14 2 1
#> 67: x$StdErr[['sij_sik']] 5.24 2 1
#> 68: x$StdErr[['sij_skl']] 4.78 2 1
#> 69: x$SCA['1', '2'] 4.41 2 1
#> 70: x$SCA['1', '3'] 12.55 2 1
#> 71: x$SCA['2', '9'] -33.44 2 1
#> 72: x$SCA['3', '4'] -24.85 2 1
#> 73: x$SCA['4', '9'] 25.70 2 1
#> 74: x$SCA['5', '6'] -30.91 2 1
#> 75: x$SCA['6', '8'] 27.76 2 1
#> 76: x$Genetic.Components['Additive', 'Components'] 482.32 2 1
#> 77: x$Genetic.Components['sca', 'Components'] 144.38 2 1
#> extract value digits level
#> <char> <num> <int> <int>
#> note
#> <char>
#> 1: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 2:
#> 3:
#> 4: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 5:
#> 6:
#> 7:
#> 8:
#> 9:
#> 10:
#> 11:
#> 12:
#> 13:
#> 14:
#> 15:
#> 16:
#> 17:
#> 18:
#> 19:
#> 20:
#> 21:
#> 22:
#> 23:
#> 24:
#> 25: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 26:
#> 27:
#> 28:
#> 29:
#> 30:
#> 31:
#> 32:
#> 33: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 34:
#> 35:
#> 36:
#> 37:
#> 38:
#> 39:
#> 40:
#> 41:
#> 42:
#> 43: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 44:
#> 45:
#> 46:
#> 47:
#> 48:
#> 49: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 50:
#> 51:
#> 52: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 53:
#> 54:
#> 55: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 56:
#> 57:
#> 58:
#> 59:
#> 60:
#> 61:
#> 62:
#> 63:
#> 64:
#> 65:
#> 66:
#> 67:
#> 68:
#> 69:
#> 70: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 71:
#> 72: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 73:
#> 74: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 75:
#> 76: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 77: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> note
#> <char>
# \donttest{
da_reference(
source = "."
, evaluate = TRUE
, level = 1
)
#> id source quantity
#> <char> <char> <char>
#> 1: g56_X1_ms_gca Griffing (1956), Table 8 X1 mean square gca (Model I)
#> 2: g56_X1_ms_sca Griffing (1956), Table 8 X1 mean square sca (Model I)
#> 3: g56_X1_comp_gca Griffing (1956), Table 8 X1 gca component (Model I)
#> 4: g56_X1_comp_sca Griffing (1956), Table 8 X1 sca component (Model I)
#> 5: g56_X1_g1 Griffing (1956), Table 9 X1 gca effect of parent 1
#> 6: g56_X1_g2 Griffing (1956), Table 9 X1 gca effect of parent 2
#> 7: g56_X1_g3 Griffing (1956), Table 9 X1 gca effect of parent 3
#> 8: g56_X1_g4 Griffing (1956), Table 9 X1 gca effect of parent 4
#> 9: g56_X1_g5 Griffing (1956), Table 9 X1 gca effect of parent 5
#> 10: g56_X1_g6 Griffing (1956), Table 9 X1 gca effect of parent 6
#> 11: g56_X1_g7 Griffing (1956), Table 9 X1 gca effect of parent 7
#> 12: g56_X1_g8 Griffing (1956), Table 9 X1 gca effect of parent 8
#> 13: g56_X1_g9 Griffing (1956), Table 9 X1 gca effect of parent 9
#> 14: g56_X1_se_gg Griffing (1956), Table 9 X1 SE(g_i - g_j)
#> 15: g56_X1_se_sik Griffing (1956), Table 10 X1 SE(s_ij - s_ik)
#> 16: g56_X1_se_skl Griffing (1956), Table 10 X1 SE(s_ij - s_kl)
#> 17: g56_X1_s12 Griffing (1956), Table 10 X1 sca effect of cross 1 x 2
#> 18: g56_X1_s13 Griffing (1956), Table 10 X1 sca effect of cross 1 x 3
#> 19: g56_X1_s19 Griffing (1956), Table 10 X1 sca effect of cross 1 x 9
#> 20: g56_X1_s29 Griffing (1956), Table 10 X1 sca effect of cross 2 x 9
#> 21: g56_X1_s34 Griffing (1956), Table 10 X1 sca effect of cross 3 x 4
#> 22: g56_X1_s49 Griffing (1956), Table 10 X1 sca effect of cross 4 x 9
#> 23: g56_X1_s56 Griffing (1956), Table 10 X1 sca effect of cross 5 x 6
#> 24: g56_X1_s68 Griffing (1956), Table 10 X1 sca effect of cross 6 x 8
#> 25: g56_X1_s89 Griffing (1956), Table 10 X1 sca effect of cross 8 x 9
#> 26: g56_X1_m2_2sg Griffing (1956), Table 13 X1 2 sigma^2_g (Model II)
#> 27: g56_X1_m2_ss Griffing (1956), Table 13 X1 sigma^2_s (Model II)
#> 28: g56_X2_ms_gca Griffing (1956), Table 8 X2 mean square gca (Model I)
#> 29: g56_X2_ms_sca Griffing (1956), Table 8 X2 mean square sca (Model I)
#> 30: g56_X2_comp_gca Griffing (1956), Table 8 X2 gca component (Model I)
#> 31: g56_X2_comp_sca Griffing (1956), Table 8 X2 sca component (Model I)
#> 32: g56_X2_g1 Griffing (1956), Table 9 X2 gca effect of parent 1
#> 33: g56_X2_g2 Griffing (1956), Table 9 X2 gca effect of parent 2
#> 34: g56_X2_g3 Griffing (1956), Table 9 X2 gca effect of parent 3
#> 35: g56_X2_g4 Griffing (1956), Table 9 X2 gca effect of parent 4
#> 36: g56_X2_g5 Griffing (1956), Table 9 X2 gca effect of parent 5
#> 37: g56_X2_g6 Griffing (1956), Table 9 X2 gca effect of parent 6
#> 38: g56_X2_g7 Griffing (1956), Table 9 X2 gca effect of parent 7
#> 39: g56_X2_g8 Griffing (1956), Table 9 X2 gca effect of parent 8
#> 40: g56_X2_g9 Griffing (1956), Table 9 X2 gca effect of parent 9
#> 41: g56_X2_se_gg Griffing (1956), Table 9 X2 SE(g_i - g_j)
#> 42: g56_X2_se_sik Griffing (1956), Table 10 X2 SE(s_ij - s_ik)
#> 43: g56_X2_se_skl Griffing (1956), Table 10 X2 SE(s_ij - s_kl)
#> 44: g56_X2_s12 Griffing (1956), Table 10 X2 sca effect of cross 1 x 2
#> 45: g56_X2_s13 Griffing (1956), Table 10 X2 sca effect of cross 1 x 3
#> 46: g56_X2_s29 Griffing (1956), Table 10 X2 sca effect of cross 2 x 9
#> 47: g56_X2_s34 Griffing (1956), Table 10 X2 sca effect of cross 3 x 4
#> 48: g56_X2_s49 Griffing (1956), Table 10 X2 sca effect of cross 4 x 9
#> 49: g56_X2_s56 Griffing (1956), Table 10 X2 sca effect of cross 5 x 6
#> 50: g56_X2_s68 Griffing (1956), Table 10 X2 sca effect of cross 6 x 8
#> 51: g56_X2_m2_2sg Griffing (1956), Table 13 X2 2 sigma^2_g (Model II)
#> 52: g56_X2_m2_ss Griffing (1956), Table 13 X2 sigma^2_s (Model II)
#> 53: g56_X3_ms_gca Griffing (1956), Table 8 X3 mean square gca (Model I)
#> 54: g56_X3_ms_sca Griffing (1956), Table 8 X3 mean square sca (Model I)
#> 55: g56_X3_comp_gca Griffing (1956), Table 8 X3 gca component (Model I)
#> 56: g56_X3_comp_sca Griffing (1956), Table 8 X3 sca component (Model I)
#> 57: g56_X3_g1 Griffing (1956), Table 9 X3 gca effect of parent 1
#> 58: g56_X3_g2 Griffing (1956), Table 9 X3 gca effect of parent 2
#> 59: g56_X3_g3 Griffing (1956), Table 9 X3 gca effect of parent 3
#> 60: g56_X3_g4 Griffing (1956), Table 9 X3 gca effect of parent 4
#> 61: g56_X3_g5 Griffing (1956), Table 9 X3 gca effect of parent 5
#> 62: g56_X3_g6 Griffing (1956), Table 9 X3 gca effect of parent 6
#> 63: g56_X3_g7 Griffing (1956), Table 9 X3 gca effect of parent 7
#> 64: g56_X3_g8 Griffing (1956), Table 9 X3 gca effect of parent 8
#> 65: g56_X3_g9 Griffing (1956), Table 9 X3 gca effect of parent 9
#> 66: g56_X3_se_gg Griffing (1956), Table 9 X3 SE(g_i - g_j)
#> 67: g56_X3_se_sik Griffing (1956), Table 10 X3 SE(s_ij - s_ik)
#> 68: g56_X3_se_skl Griffing (1956), Table 10 X3 SE(s_ij - s_kl)
#> 69: g56_X3_s12 Griffing (1956), Table 10 X3 sca effect of cross 1 x 2
#> 70: g56_X3_s13 Griffing (1956), Table 10 X3 sca effect of cross 1 x 3
#> 71: g56_X3_s29 Griffing (1956), Table 10 X3 sca effect of cross 2 x 9
#> 72: g56_X3_s34 Griffing (1956), Table 10 X3 sca effect of cross 3 x 4
#> 73: g56_X3_s49 Griffing (1956), Table 10 X3 sca effect of cross 4 x 9
#> 74: g56_X3_s56 Griffing (1956), Table 10 X3 sca effect of cross 5 x 6
#> 75: g56_X3_s68 Griffing (1956), Table 10 X3 sca effect of cross 6 x 8
#> 76: g56_X3_m2_2sg Griffing (1956), Table 13 X3 2 sigma^2_g (Model II)
#> 77: g56_X3_m2_ss Griffing (1956), Table 13 X3 sigma^2_s (Model II)
#> id source quantity
#> <char> <char> <char>
#> call
#> <char>
#> 1: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 2: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 3: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 4: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 5: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 6: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 7: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 8: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 9: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 10: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 11: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 12: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 13: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 14: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 15: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 16: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 17: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 18: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 19: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 20: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 21: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 22: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 23: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 24: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 25: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1642.48 / 78, ErrorDf = 2558)
#> 26: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 11803.61 / 78, ErrorDf = 175)
#> 27: GriffingMeans(y = TotalYield, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 11803.61 / 78, ErrorDf = 175)
#> 28: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 29: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 30: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 31: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 32: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 33: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 34: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 35: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 36: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 37: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 38: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 39: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 40: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 41: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 42: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 43: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 44: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 45: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 46: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 47: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 48: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 49: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 50: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 46.61 / 78, ErrorDf = 2558)
#> 51: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 235.82 / 78, ErrorDf = 175)
#> 52: GriffingMeans(y = CobWeight, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 235.82 / 78, ErrorDf = 175)
#> 53: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 54: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 55: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 56: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 57: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 58: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 59: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 60: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 61: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 62: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 63: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 64: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 65: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 66: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 67: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 68: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 69: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 70: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 71: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 72: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 73: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 74: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 75: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 1, ErrorMS = 1247.39 / 78, ErrorDf = 2558)
#> 76: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 9056.45 / 78, ErrorDf = 175)
#> 77: GriffingMeans(y = ShelledCorn, Cross1 = Cross1, Cross2 = Cross2, data = Griffing56, Method = 4, Model = 2, ErrorMS = 9056.45 / 78, ErrorDf = 175)
#> call
#> <char>
#> extract value digits level
#> <char> <num> <int> <int>
#> 1: x$ANOVA['gca', 'Mean Sq'] 2325.74 2 1
#> 2: x$ANOVA['sca', 'Mean Sq'] 339.44 2 1
#> 3: x$Genetic.Components['gca', 'Components'] 329.24 2 1
#> 4: x$Genetic.Components['sca', 'Components'] 318.39 2 1
#> 5: x$GCA[['1']] 9.35 2 1
#> 6: x$GCA[['2']] -20.65 2 1
#> 7: x$GCA[['3']] -10.16 2 1
#> 8: x$GCA[['4']] -22.55 2 1
#> 9: x$GCA[['5']] -1.51 2 1
#> 10: x$GCA[['6']] -1.89 2 1
#> 11: x$GCA[['7']] 28.12 2 1
#> 12: x$GCA[['8']] -6.93 2 1
#> 13: x$GCA[['9']] 26.22 2 1
#> 14: x$StdErr[['gi_gj']] 2.45 2 1
#> 15: x$StdErr[['sij_sik']] 6.01 2 1
#> 16: x$StdErr[['sij_skl']] 5.48 2 1
#> 17: x$SCA['1', '2'] 4.94 2 1
#> 18: x$SCA['1', '3'] 14.46 2 1
#> 19: x$SCA['1', '9'] 18.47 2 1
#> 20: x$SCA['2', '9'] -37.73 2 1
#> 21: x$SCA['3', '4'] -29.94 2 1
#> 22: x$SCA['4', '9'] 30.97 2 1
#> 23: x$SCA['5', '6'] -36.16 2 1
#> 24: x$SCA['6', '8'] 32.77 2 1
#> 25: x$SCA['8', '9'] -5.85 2 1
#> 26: x$Genetic.Components['Additive', 'Components'] 567.52 2 1
#> 27: x$Genetic.Components['sca', 'Components'] 188.11 2 1
#> 28: x$ANOVA['gca', 'Mean Sq'] 90.98 2 1
#> 29: x$ANOVA['sca', 'Mean Sq'] 6.99 2 1
#> 30: x$Genetic.Components['gca', 'Components'] 12.91 2 1
#> 31: x$Genetic.Components['sca', 'Components'] 6.39 2 1
#> 32: x$GCA[['1']] 0.27 2 1
#> 33: x$GCA[['2']] -5.48 2 1
#> 34: x$GCA[['3']] -3.96 2 1
#> 35: x$GCA[['4']] -2.23 2 1
#> 36: x$GCA[['5']] 6.64 2 1
#> 37: x$GCA[['6']] 2.24 2 1
#> 38: x$GCA[['7']] 0.97 2 1
#> 39: x$GCA[['8']] -0.23 2 1
#> 40: x$GCA[['9']] 1.78 2 1
#> 41: x$StdErr[['gi_gj']] 0.41 2 1
#> 42: x$StdErr[['sij_sik']] 1.01 2 1
#> 43: x$StdErr[['sij_skl']] 0.93 2 1
#> 44: x$SCA['1', '2'] 0.53 2 1
#> 45: x$SCA['1', '3'] 1.91 2 1
#> 46: x$SCA['2', '9'] -4.29 2 1
#> 47: x$SCA['3', '4'] -5.09 2 1
#> 48: x$SCA['4', '9'] 5.27 2 1
#> 49: x$SCA['5', '6'] -5.25 2 1
#> 50: x$SCA['6', '8'] 5.01 2 1
#> 51: x$Genetic.Components['Additive', 'Components'] 24.00 2 1
#> 52: x$Genetic.Components['sca', 'Components'] 3.97 2 1
#> 53: x$ANOVA['gca', 'Mean Sq'] 1948.66 2 1
#> 54: x$ANOVA['sca', 'Mean Sq'] 260.49 2 1
#> 55: x$Genetic.Components['gca', 'Components'] 276.09 2 1
#> 56: x$Genetic.Components['sca', 'Components'] 244.50 2 1
#> 57: x$GCA[['1']] 9.08 2 1
#> 58: x$GCA[['2']] -15.17 2 1
#> 59: x$GCA[['3']] -6.20 2 1
#> 60: x$GCA[['4']] -20.32 2 1
#> 61: x$GCA[['5']] -8.15 2 1
#> 62: x$GCA[['6']] -4.13 2 1
#> 63: x$GCA[['7']] 27.15 2 1
#> 64: x$GCA[['8']] -6.70 2 1
#> 65: x$GCA[['9']] 24.44 2 1
#> 66: x$StdErr[['gi_gj']] 2.14 2 1
#> 67: x$StdErr[['sij_sik']] 5.24 2 1
#> 68: x$StdErr[['sij_skl']] 4.78 2 1
#> 69: x$SCA['1', '2'] 4.41 2 1
#> 70: x$SCA['1', '3'] 12.55 2 1
#> 71: x$SCA['2', '9'] -33.44 2 1
#> 72: x$SCA['3', '4'] -24.85 2 1
#> 73: x$SCA['4', '9'] 25.70 2 1
#> 74: x$SCA['5', '6'] -30.91 2 1
#> 75: x$SCA['6', '8'] 27.76 2 1
#> 76: x$Genetic.Components['Additive', 'Components'] 482.32 2 1
#> 77: x$Genetic.Components['sca', 'Components'] 144.38 2 1
#> extract value digits level
#> <char> <num> <int> <int>
#> note
#> <char>
#> 1: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 2:
#> 3:
#> 4: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 5:
#> 6:
#> 7:
#> 8:
#> 9:
#> 10:
#> 11:
#> 12:
#> 13:
#> 14:
#> 15:
#> 16:
#> 17:
#> 18:
#> 19:
#> 20:
#> 21:
#> 22:
#> 23:
#> 24:
#> 25: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 26:
#> 27:
#> 28:
#> 29:
#> 30:
#> 31:
#> 32:
#> 33: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 34:
#> 35:
#> 36:
#> 37:
#> 38:
#> 39:
#> 40:
#> 41:
#> 42:
#> 43: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 44:
#> 45:
#> 46:
#> 47:
#> 48:
#> 49: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 50:
#> 51:
#> 52: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 53:
#> 54:
#> 55: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 56:
#> 57:
#> 58:
#> 59:
#> 60:
#> 61:
#> 62:
#> 63:
#> 64:
#> 65:
#> 66:
#> 67:
#> 68:
#> 69:
#> 70: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 71:
#> 72: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 73:
#> 74: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 75:
#> 76: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> 77: differs in the last printed digit: Table 6 gives the means to one decimal, Griffing worked from unrounded means
#> note
#> <char>
#> computed agrees
#> <num> <lgcl>
#> 1: 2325.7471825 FALSE
#> 2: 339.4389418 TRUE
#> 3: 329.2413924 TRUE
#> 4: 318.3815059 FALSE
#> 5: 9.3507937 TRUE
#> 6: -20.6492063 TRUE
#> 7: -10.1634921 TRUE
#> 8: -22.5492063 TRUE
#> 9: -1.5063492 TRUE
#> 10: -1.8920635 TRUE
#> 11: 28.1222222 TRUE
#> 12: -6.9349206 TRUE
#> 13: 26.2222222 TRUE
#> 14: 2.4528372 TRUE
#> 15: 6.0081995 TRUE
#> 16: 5.4847107 TRUE
#> 17: 4.9428571 TRUE
#> 18: 14.4571429 TRUE
#> 19: 18.4714286 TRUE
#> 20: -37.7285714 TRUE
#> 21: -29.9428571 TRUE
#> 22: 30.9714286 TRUE
#> 23: -36.1571429 TRUE
#> 24: 32.7714286 TRUE
#> 25: -5.8428571 FALSE
#> 26: 567.5166402 TRUE
#> 27: 188.1106085 TRUE
#> 28: 90.9809921 TRUE
#> 29: 6.9879365 TRUE
#> 30: 12.9119183 TRUE
#> 31: 6.3903724 TRUE
#> 32: 0.2682540 TRUE
#> 33: -5.4746032 FALSE
#> 34: -3.9603175 TRUE
#> 35: -2.2317460 TRUE
#> 36: 6.6396825 TRUE
#> 37: 2.2396825 TRUE
#> 38: 0.9682540 TRUE
#> 39: -0.2317460 TRUE
#> 40: 1.7825397 TRUE
#> 41: 0.4131980 TRUE
#> 42: 1.0121243 TRUE
#> 43: 0.9239389 FALSE
#> 44: 0.5285714 TRUE
#> 45: 1.9142857 TRUE
#> 46: -4.2857143 TRUE
#> 47: -5.0857143 TRUE
#> 48: 5.2714286 TRUE
#> 49: -5.2571429 FALSE
#> 50: 5.0142857 TRUE
#> 51: 23.9980159 TRUE
#> 52: 3.9646032 FALSE
#> 53: 1948.6577778 TRUE
#> 54: 260.4948148 TRUE
#> 55: 276.0950855 FALSE
#> 56: 244.5026353 TRUE
#> 57: 9.0825397 TRUE
#> 58: -15.1746032 TRUE
#> 59: -6.2031746 TRUE
#> 60: -20.3174603 TRUE
#> 61: -8.1460317 TRUE
#> 62: -4.1317460 TRUE
#> 63: 27.1539683 TRUE
#> 64: -6.7031746 TRUE
#> 65: 24.4396825 TRUE
#> 66: 2.1375673 TRUE
#> 67: 5.2359493 TRUE
#> 68: 4.7797459 TRUE
#> 69: 4.4142857 TRUE
#> 70: 12.5428571 FALSE
#> 71: -33.4428571 TRUE
#> 72: -24.8571429 FALSE
#> 73: 25.7000000 TRUE
#> 74: -30.9000000 FALSE
#> 75: 27.7571429 TRUE
#> 76: 482.3322751 FALSE
#> 77: 144.3864815 FALSE
#> computed agrees
#> <num> <lgcl>
# }