Nothing
inst/extdata/Alfalfa.O.forlme.R
In forsearch: Diagnostic Analysis Using Forward Search Procedure for Various Models
Alfalfa.O.forlme <-
list(`Number of rows included in Step 1` = 36, Subgroups = structure(list(
GroupNo = 1:18, Block = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("1",
"2", "3", "4", "5", "6"), class = "factor"), Variety = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("Cossack", "Ladak", "Ranger"), class = "factor")), class = "data.frame", row.names = c("1",
"5", "9", "13", "17", "21", "25", "29", "33", "37", "41", "45",
"49", "53", "57", "61", "65", "69")), `Rows by subgroup` = list(
structure(list(Observation = 1:4, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "1", class = "factor"), Yield = c(2.169499813529705,
1.57886424034908, 2.2892581493168307, 2.2298681661614683)), row.names = c(NA,
4L), formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 5:8, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "2", class = "factor"), Yield = c(1.8797458997235463,
1.259357644534691, 1.6006543608149515, 2.0102147529046683
)), row.names = 5:8, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 9:12, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "3", class = "factor"), Yield = c(1.6194712464220358,
1.2205047792648176, 1.668879881007385, 1.8209433055222459
)), row.names = 9:12, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 13:16, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "4", class = "factor"), Yield = c(2.3390459469655021,
1.5889868973164862, 1.9097744930347174, 2.1008141854354605
)), row.names = 13:16, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 17:20, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "5", class = "factor"), Yield = c(1.5804166317690926,
1.2499474222523117, 1.3907547048751305, 1.6590237975889943
)), row.names = 17:20, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 21:24, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "6", class = "factor"), Yield = c(1.6607724152983758,
0.94039122682117682, 1.1199750453581907, 1.0998145913551773
)), row.names = 21:24, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 25:28, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "1", class = "factor"), Yield = c(2.3300141638712111,
1.3795979976152408, 1.8590772868157366, 2.2689741776985199
)), row.names = 25:28, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 29:32, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "2", class = "factor"), Yield = c(2.0108889687734401,
1.3008084370035, 1.700956836947642, 1.8105517573704393)), row.names = 29:32, formula = Yield ~
1 | Block/Variety, formulaList = list(Block = ~Block,
Variety = ~Variety), order.groups = list(Block = TRUE,
Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 33:36, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "3", class = "factor"), Yield = c(1.7003247817263896,
1.8510772634953769, 1.8098578831823331, 2.0107397185233209
)), row.names = 33:36, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 37:40, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "4", class = "factor"), Yield = c(1.7808427831682969,
1.0906520904097918, 1.5395848397957161, 1.3991393571047492
)), row.names = 37:40, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 41:44, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "5", class = "factor"), Yield = c(1.4205276081062301,
1.1297666272699873, 1.6693399187827229, 1.3092533135084987
)), row.names = 41:44, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 45:48, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "6", class = "factor"), Yield = c(1.3489911723638548,
1.059039468134817, 0.87943670623840509, 1.0603118524994617
)), row.names = 45:48, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 49:52, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "1", class = "factor"), Yield = c(1.7503767696119295,
1.5209047232245476, 1.5504366380749905, 1.5603037522351157
)), row.names = 49:52, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 53:56, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "2", class = "factor"), Yield = c(1.9511144375333309,
1.4692015904508826, 1.6106152474723587, 1.7194024313811678
)), row.names = 53:56, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 57:60, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "3", class = "factor"), Yield = c(2.1291577150236645,
1.7996942231537436, 1.8194098413486923, 1.9899882783857175
)), row.names = 57:60, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 61:64, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "4", class = "factor"), Yield = c(1.7795938556533393,
1.3703050803974144, 1.5597517608164344, 1.5507407127731396
)), row.names = 61:64, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 65:68, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "5", class = "factor"), Yield = c(1.3096243541663386,
1.0096022962922739, 1.2292984347082463, 1.5098438188351098
)), row.names = 65:68, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 69:72, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "6", class = "factor"), Yield = c(1.2999826783250847,
1.3094017614998898, 1.1307424366353502, 1.3292403989133892
)), row.names = 69:72, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame"))), `Rows in stage` = list(NULL, NULL, NULL,
NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL,
NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL,
NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL,
NULL, NULL, c(2L, 5L, 7L, 8L, 9L, 10L, 11L, 14L, 15L, 17L,
18L, 21L, 26L, 27L, 31L, 32L, 34L, 35L, 37L, 39L, 44L, 49L,
50L, 51L, 52L, 53L, 54L, 55L, 56L, 59L, 61L, 62L, 63L, 64L,
65L, 68L), c(2L, 1L, 7L, 5L, 11L, 9L, 14L, 15L, 20L, 17L,
21L, 22L, 26L, 27L, 31L, 30L, 33L, 35L, 37L, 39L, 43L, 41L,
45L, 46L, 49L, 50L, 54L, 56L, 59L, 58L, 61L, 64L, 68L, 65L,
70L, 72L, 52L), c(2L, 1L, 7L, 6L, 9L, 11L, 15L, 14L, 17L,
20L, 21L, 22L, 26L, 27L, 31L, 30L, 33L, 35L, 37L, 40L, 43L,
44L, 45L, 46L, 52L, 49L, 54L, 55L, 59L, 58L, 64L, 62L, 68L,
65L, 70L, 72L, 61L, 50L), c(2L, 1L, 6L, 7L, 9L, 11L, 15L,
14L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L,
37L, 43L, 44L, 45L, 46L, 49L, 52L, 54L, 55L, 59L, 58L, 62L,
64L, 68L, 65L, 70L, 72L, 61L, 50L, 10L), c(2L, 1L, 6L, 7L,
9L, 11L, 15L, 14L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L,
33L, 35L, 40L, 37L, 43L, 44L, 45L, 48L, 49L, 50L, 54L, 55L,
59L, 58L, 62L, 61L, 68L, 65L, 72L, 70L, 64L, 52L, 10L, 63L
), c(2L, 1L, 7L, 6L, 9L, 11L, 14L, 15L, 17L, 18L, 21L, 24L,
26L, 27L, 31L, 30L, 33L, 35L, 40L, 37L, 44L, 43L, 48L, 45L,
49L, 50L, 54L, 55L, 59L, 58L, 62L, 61L, 68L, 65L, 72L, 70L,
63L, 51L, 39L, 64L, 10L), c(2L, 1L, 7L, 6L, 11L, 9L, 14L,
15L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L,
37L, 44L, 43L, 48L, 45L, 49L, 50L, 54L, 55L, 59L, 58L, 62L,
61L, 68L, 65L, 72L, 70L, 63L, 51L, 39L, 10L, 5L, 64L), c(2L,
1L, 7L, 6L, 11L, 9L, 14L, 15L, 17L, 18L, 21L, 24L, 26L, 27L,
31L, 30L, 33L, 35L, 40L, 37L, 44L, 43L, 48L, 45L, 49L, 50L,
54L, 55L, 59L, 58L, 62L, 61L, 68L, 65L, 72L, 70L, 63L, 51L,
39L, 5L, 10L, 64L, 52L), c(2L, 1L, 7L, 6L, 11L, 9L, 14L,
15L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L,
37L, 43L, 44L, 48L, 45L, 49L, 50L, 54L, 55L, 59L, 58L, 62L,
61L, 68L, 65L, 72L, 70L, 63L, 51L, 39L, 5L, 64L, 10L, 52L,
53L), c(2L, 1L, 7L, 6L, 11L, 9L, 14L, 15L, 17L, 18L, 21L,
24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L, 37L, 43L, 44L, 48L,
46L, 49L, 50L, 54L, 55L, 59L, 58L, 62L, 61L, 68L, 65L, 72L,
70L, 63L, 51L, 39L, 5L, 64L, 10L, 52L, 53L, 41L), c(2L, 1L,
7L, 6L, 11L, 9L, 14L, 15L, 17L, 18L, 21L, 24L, 26L, 27L,
31L, 30L, 33L, 35L, 40L, 37L, 43L, 44L, 48L, 46L, 49L, 50L,
54L, 55L, 59L, 58L, 62L, 61L, 68L, 66L, 72L, 70L, 63L, 51L,
39L, 5L, 10L, 64L, 52L, 53L, 42L, 29L), c(2L, 1L, 5L, 6L,
11L, 10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 31L, 30L,
33L, 35L, 37L, 40L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 54L,
59L, 57L, 62L, 61L, 68L, 66L, 72L, 70L, 7L, 63L, 53L, 39L,
50L, 9L, 64L, 29L, 52L, 42L, 38L), c(2L, 1L, 6L, 5L, 11L,
10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 30L, 31L, 35L,
33L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 53L, 59L,
57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L, 39L, 54L, 42L,
29L, 64L, 52L, 38L, 9L, 50L, 19L), c(2L, 1L, 6L, 5L, 11L,
10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 30L, 31L, 33L,
35L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 53L, 59L,
57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L, 39L, 54L, 42L,
29L, 64L, 52L, 38L, 9L, 50L, 19L, 20L), c(2L, 1L, 6L, 5L,
11L, 10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 30L, 31L,
33L, 35L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 53L,
59L, 57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L, 39L, 64L,
54L, 42L, 52L, 29L, 38L, 9L, 50L, 20L, 19L, 56L), c(2L, 1L,
6L, 5L, 11L, 10L, 15L, 14L, 18L, 20L, 21L, 24L, 26L, 27L,
30L, 31L, 33L, 35L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L,
55L, 53L, 59L, 57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L,
39L, 64L, 52L, 54L, 42L, 29L, 38L, 9L, 50L, 17L, 19L, 56L,
22L), c(2L, 1L, 6L, 5L, 11L, 10L, 15L, 14L, 18L, 20L, 21L,
24L, 26L, 27L, 30L, 31L, 33L, 35L, 40L, 37L, 43L, 44L, 46L,
48L, 49L, 51L, 55L, 53L, 59L, 57L, 61L, 62L, 68L, 66L, 70L,
72L, 7L, 63L, 39L, 64L, 52L, 42L, 29L, 38L, 54L, 9L, 50L,
17L, 19L, 56L, 22L, 32L), c(2L, 1L, 6L, 5L, 11L, 10L, 15L,
14L, 18L, 20L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L,
40L, 43L, 44L, 46L, 48L, 49L, 52L, 55L, 53L, 59L, 57L, 61L,
62L, 68L, 66L, 70L, 72L, 7L, 64L, 63L, 51L, 39L, 42L, 29L,
38L, 54L, 9L, 50L, 17L, 56L, 19L, 32L, 24L, 12L), c(2L, 1L,
6L, 5L, 11L, 10L, 15L, 14L, 18L, 20L, 21L, 22L, 26L, 27L,
30L, 31L, 33L, 35L, 37L, 40L, 43L, 42L, 46L, 48L, 52L, 49L,
55L, 53L, 59L, 57L, 61L, 64L, 68L, 66L, 70L, 72L, 7L, 62L,
63L, 51L, 39L, 44L, 29L, 38L, 54L, 9L, 56L, 50L, 17L, 19L,
32L, 12L, 24L, 41L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L,
18L, 20L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 40L,
43L, 42L, 46L, 48L, 49L, 52L, 55L, 53L, 59L, 57L, 61L, 64L,
68L, 66L, 70L, 72L, 5L, 62L, 63L, 51L, 39L, 44L, 9L, 38L,
54L, 29L, 17L, 56L, 50L, 19L, 32L, 12L, 41L, 24L, 67L), c(2L,
1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 20L, 21L, 22L, 26L,
27L, 30L, 31L, 33L, 35L, 37L, 40L, 43L, 42L, 46L, 48L, 49L,
52L, 55L, 53L, 59L, 57L, 61L, 64L, 68L, 66L, 70L, 72L, 5L,
63L, 51L, 62L, 39L, 44L, 9L, 38L, 54L, 29L, 17L, 56L, 50L,
19L, 32L, 12L, 41L, 24L, 67L, 45L), c(2L, 1L, 6L, 7L, 11L,
10L, 15L, 14L, 18L, 20L, 21L, 22L, 26L, 27L, 30L, 31L, 33L,
35L, 37L, 40L, 43L, 42L, 46L, 45L, 49L, 52L, 55L, 53L, 59L,
57L, 61L, 64L, 68L, 66L, 70L, 72L, 63L, 51L, 62L, 39L, 5L,
9L, 44L, 38L, 54L, 17L, 56L, 29L, 50L, 19L, 32L, 12L, 41L,
24L, 67L, 48L, 65L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L,
18L, 17L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 40L,
43L, 42L, 46L, 45L, 49L, 52L, 55L, 54L, 59L, 57L, 61L, 64L,
68L, 66L, 70L, 72L, 63L, 51L, 62L, 39L, 9L, 5L, 20L, 44L,
38L, 53L, 56L, 50L, 19L, 29L, 41L, 32L, 12L, 24L, 67L, 48L,
65L, 69L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 17L,
21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 40L, 43L, 42L,
46L, 45L, 49L, 52L, 55L, 54L, 59L, 57L, 64L, 61L, 68L, 66L,
70L, 72L, 63L, 51L, 62L, 9L, 39L, 5L, 20L, 44L, 38L, 56L,
53L, 50L, 19L, 41L, 29L, 32L, 12L, 24L, 65L, 67L, 69L, 48L,
60L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 17L, 21L,
22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 39L, 43L, 42L, 46L,
45L, 52L, 49L, 55L, 54L, 59L, 57L, 64L, 61L, 68L, 66L, 70L,
72L, 63L, 51L, 62L, 9L, 40L, 20L, 5L, 38L, 56L, 44L, 53L,
50L, 19L, 32L, 41L, 12L, 29L, 65L, 67L, 69L, 24L, 48L, 60L,
8L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 20L, 21L,
22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 39L, 43L, 42L, 46L,
45L, 52L, 49L, 55L, 56L, 59L, 57L, 64L, 61L, 68L, 66L, 70L,
72L, 63L, 51L, 62L, 9L, 17L, 40L, 5L, 38L, 54L, 53L, 50L,
19L, 44L, 32L, 12L, 41L, 29L, 65L, 67L, 69L, 60L, 8L, 36L,
24L, 48L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 20L,
21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 39L, 43L, 42L,
46L, 45L, 52L, 49L, 55L, 56L, 59L, 60L, 64L, 61L, 68L, 66L,
70L, 72L, 63L, 51L, 62L, 9L, 17L, 5L, 38L, 54L, 40L, 32L,
12L, 53L, 50L, 19L, 44L, 41L, 29L, 65L, 67L, 69L, 57L, 8L,
36L, 24L, 71L, 16L), c(2L, 1L, 7L, 6L, 10L, 11L, 15L, 14L,
20L, 18L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 39L, 37L,
43L, 42L, 46L, 45L, 49L, 51L, 55L, 56L, 59L, 60L, 63L, 61L,
68L, 66L, 70L, 72L, 62L, 9L, 52L, 64L, 17L, 32L, 12L, 5L,
38L, 54L, 19L, 53L, 50L, 40L, 41L, 29L, 44L, 8L, 36L, 67L,
65L, 69L, 57L, 16L, 71L, 23L, 58L), c(2L, 1L, 7L, 6L, 10L,
9L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L, 30L, 32L, 33L,
35L, 39L, 37L, 43L, 42L, 46L, 45L, 51L, 49L, 56L, 55L, 59L,
60L, 63L, 62L, 68L, 66L, 70L, 72L, 61L, 11L, 12L, 31L, 17L,
52L, 64L, 54L, 5L, 19L, 50L, 38L, 53L, 41L, 29L, 40L, 8L,
36L, 67L, 44L, 16L, 65L, 69L, 57L, 71L, 23L, 58L, 4L), c(2L,
1L, 7L, 6L, 12L, 10L, 14L, 15L, 18L, 20L, 21L, 22L, 26L,
27L, 30L, 32L, 33L, 35L, 39L, 37L, 43L, 42L, 46L, 45L, 51L,
49L, 56L, 55L, 59L, 60L, 63L, 62L, 68L, 66L, 70L, 72L, 61L,
9L, 11L, 31L, 17L, 54L, 5L, 52L, 19L, 64L, 50L, 38L, 53L,
8L, 36L, 41L, 29L, 40L, 67L, 16L, 65L, 69L, 57L, 44L, 71L,
23L, 58L, 4L, 28L), c(2L, 1L, 7L, 6L, 12L, 10L, 14L, 16L,
18L, 20L, 21L, 22L, 26L, 27L, 30L, 32L, 33L, 35L, 39L, 37L,
43L, 42L, 46L, 45L, 51L, 49L, 55L, 56L, 60L, 59L, 63L, 62L,
68L, 66L, 70L, 69L, 61L, 9L, 11L, 31L, 17L, 54L, 5L, 19L,
52L, 50L, 64L, 38L, 53L, 8L, 36L, 41L, 29L, 15L, 67L, 40L,
65L, 57L, 72L, 71L, 23L, 44L, 4L, 58L, 28L, 34L), c(2L, 1L,
7L, 6L, 12L, 9L, 14L, 16L, 18L, 20L, 21L, 22L, 26L, 27L,
30L, 32L, 33L, 35L, 39L, 37L, 43L, 42L, 46L, 45L, 51L, 49L,
55L, 56L, 60L, 59L, 63L, 62L, 68L, 66L, 70L, 69L, 61L, 11L,
10L, 31L, 54L, 17L, 5L, 50L, 19L, 52L, 64L, 53L, 8L, 36L,
38L, 41L, 29L, 15L, 67L, 40L, 65L, 57L, 72L, 71L, 23L, 44L,
58L, 4L, 28L, 34L, 25L), c(2L, 1L, 7L, 6L, 12L, 11L, 14L,
16L, 18L, 20L, 21L, 22L, 26L, 27L, 30L, 32L, 33L, 35L, 37L,
39L, 43L, 42L, 46L, 45L, 49L, 51L, 55L, 56L, 60L, 59L, 63L,
62L, 68L, 66L, 70L, 72L, 61L, 10L, 31L, 54L, 9L, 5L, 17L,
50L, 19L, 53L, 52L, 64L, 8L, 36L, 38L, 29L, 41L, 15L, 67L,
40L, 57L, 65L, 71L, 69L, 23L, 44L, 58L, 4L, 28L, 34L, 25L,
13L), c(2L, 1L, 7L, 6L, 12L, 11L, 14L, 16L, 18L, 20L, 21L,
22L, 26L, 27L, 30L, 32L, 33L, 35L, 37L, 39L, 43L, 42L, 46L,
45L, 51L, 49L, 55L, 56L, 60L, 59L, 61L, 63L, 68L, 66L, 70L,
72L, 62L, 5L, 10L, 31L, 54L, 9L, 53L, 50L, 19L, 17L, 52L,
64L, 29L, 8L, 36L, 38L, 15L, 67L, 57L, 41L, 40L, 71L, 23L,
44L, 58L, 4L, 65L, 69L, 28L, 34L, 25L, 13L, 24L), c(2L, 1L,
7L, 6L, 12L, 11L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L,
30L, 32L, 33L, 35L, 37L, 39L, 43L, 42L, 46L, 45L, 51L, 49L,
56L, 55L, 59L, 60L, 61L, 63L, 68L, 66L, 70L, 72L, 62L, 5L,
10L, 31L, 54L, 9L, 52L, 53L, 64L, 50L, 19L, 17L, 29L, 38L,
8L, 36L, 40L, 67L, 57L, 41L, 16L, 44L, 71L, 23L, 58L, 65L,
69L, 4L, 34L, 28L, 25L, 13L, 24L, 48L), c(2L, 1L, 7L, 6L,
11L, 10L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L, 30L, 32L,
33L, 35L, 37L, 39L, 43L, 42L, 46L, 45L, 51L, 49L, 56L, 55L,
59L, 60L, 61L, 63L, 68L, 66L, 70L, 72L, 62L, 5L, 31L, 54L,
12L, 52L, 64L, 9L, 53L, 50L, 19L, 17L, 29L, 38L, 40L, 8L,
36L, 67L, 57L, 41L, 44L, 16L, 71L, 23L, 58L, 65L, 69L, 34L,
4L, 25L, 13L, 28L, 24L, 48L, 47L), c(2L, 1L, 7L, 6L, 10L,
12L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L, 30L, 32L, 33L,
35L, 37L, 39L, 43L, 42L, 46L, 45L, 51L, 49L, 56L, 55L, 59L,
60L, 61L, 63L, 68L, 67L, 70L, 72L, 62L, 5L, 54L, 52L, 11L,
64L, 19L, 9L, 31L, 53L, 50L, 17L, 29L, 38L, 40L, 8L, 36L,
66L, 57L, 41L, 44L, 71L, 16L, 23L, 58L, 65L, 69L, 34L, 4L,
25L, 13L, 28L, 24L, 48L, 47L, 3L)), Sigma = 0.32445697002307838,
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63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 2, 5, 7,
8, 9, 10, 11, 14, 15, 17, 18, 21, 26, 27, 31, 32, 34, 35,
37, 39, 44, 49, 50, 51, 52, 53, 54, 55, 56, 59, 61, 62, 63,
64, 65, 68, 2, 1, 7, 5, 11, 9, 14, 15, 20, 17, 21, 22, 26,
27, 31, 30, 33, 35, 37, 39, 43, 41, 45, 46, 49, 50, 54, 56,
59, 58, 61, 64, 68, 65, 70, 72, 52, 2, 1, 7, 6, 9, 11, 15,
14, 17, 20, 21, 22, 26, 27, 31, 30, 33, 35, 37, 40, 43, 44,
45, 46, 52, 49, 54, 55, 59, 58, 64, 62, 68, 65, 70, 72, 61,
50, 2, 1, 6, 7, 9, 11, 15, 14, 17, 18, 21, 24, 26, 27, 31,
30, 33, 35, 40, 37, 43, 44, 45, 46, 49, 52, 54, 55, 59, 58,
62, 64, 68, 65, 70, 72, 61, 50, 10, 2, 1, 6, 7, 9, 11, 15,
14, 17, 18, 21, 24, 26, 27, 31, 30, 33, 35, 40, 37, 43, 44,
45, 48, 49, 50, 54, 55, 59, 58, 62, 61, 68, 65, 72, 70, 64,
52, 10, 63, 2, 1, 7, 6, 9, 11, 14, 15, 17, 18, 21, 24, 26,
27, 31, 30, 33, 35, 40, 37, 44, 43, 48, 45, 49, 50, 54, 55,
59, 58, 62, 61, 68, 65, 72, 70, 63, 51, 39, 64, 10, 2, 1,
7, 6, 11, 9, 14, 15, 17, 18, 21, 24, 26, 27, 31, 30, 33,
35, 40, 37, 44, 43, 48, 45, 49, 50, 54, 55, 59, 58, 62, 61,
68, 65, 72, 70, 63, 51, 39, 10, 5, 64, 2, 1, 7, 6, 11, 9,
14, 15, 17, 18, 21, 24, 26, 27, 31, 30, 33, 35, 40, 37, 44,
43, 48, 45, 49, 50, 54, 55, 59, 58, 62, 61, 68, 65, 72, 70,
63, 51, 39, 5, 10, 64, 52, 2, 1, 7, 6, 11, 9, 14, 15, 17,
18, 21, 24, 26, 27, 31, 30, 33, 35, 40, 37, 43, 44, 48, 45,
49, 50, 54, 55, 59, 58, 62, 61, 68, 65, 72, 70, 63, 51, 39,
5, 64, 10, 52, 53, 2, 1, 7, 6, 11, 9, 14, 15, 17, 18, 21,
24, 26, 27, 31, 30, 33, 35, 40, 37, 43, 44, 48, 46, 49, 50,
54, 55, 59, 58, 62, 61, 68, 65, 72, 70, 63, 51, 39, 5, 64,
10, 52, 53, 41, 2, 1, 7, 6, 11, 9, 14, 15, 17, 18, 21, 24,
26, 27, 31, 30, 33, 35, 40, 37, 43, 44, 48, 46, 49, 50, 54,
55, 59, 58, 62, 61, 68, 66, 72, 70, 63, 51, 39, 5, 10, 64,
52, 53, 42, 29, 2, 1, 5, 6, 11, 10, 15, 14, 18, 17, 21, 24,
26, 27, 31, 30, 33, 35, 37, 40, 43, 44, 46, 48, 49, 51, 55,
54, 59, 57, 62, 61, 68, 66, 72, 70, 7, 63, 53, 39, 50, 9,
64, 29, 52, 42, 38, 2, 1, 6, 5, 11, 10, 15, 14, 18, 17, 21,
24, 26, 27, 30, 31, 35, 33, 40, 37, 43, 44, 46, 48, 49, 51,
55, 53, 59, 57, 61, 62, 68, 66, 70, 72, 7, 63, 39, 54, 42,
29, 64, 52, 38, 9, 50, 19, 2, 1, 6, 5, 11, 10, 15, 14, 18,
17, 21, 24, 26, 27, 30, 31, 33, 35, 40, 37, 43, 44, 46, 48,
49, 51, 55, 53, 59, 57, 61, 62, 68, 66, 70, 72, 7, 63, 39,
54, 42, 29, 64, 52, 38, 9, 50, 19, 20, 2, 1, 6, 5, 11, 10,
15, 14, 18, 17, 21, 24, 26, 27, 30, 31, 33, 35, 40, 37, 43,
44, 46, 48, 49, 51, 55, 53, 59, 57, 61, 62, 68, 66, 70, 72,
7, 63, 39, 64, 54, 42, 52, 29, 38, 9, 50, 20, 19, 56, 2,
1, 6, 5, 11, 10, 15, 14, 18, 20, 21, 24, 26, 27, 30, 31,
33, 35, 40, 37, 43, 44, 46, 48, 49, 51, 55, 53, 59, 57, 61,
62, 68, 66, 70, 72, 7, 63, 39, 64, 52, 54, 42, 29, 38, 9,
50, 17, 19, 56, 22, 2, 1, 6, 5, 11, 10, 15, 14, 18, 20, 21,
24, 26, 27, 30, 31, 33, 35, 40, 37, 43, 44, 46, 48, 49, 51,
55, 53, 59, 57, 61, 62, 68, 66, 70, 72, 7, 63, 39, 64, 52,
42, 29, 38, 54, 9, 50, 17, 19, 56, 22, 32, 2, 1, 6, 5, 11,
10, 15, 14, 18, 20, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40,
43, 44, 46, 48, 49, 52, 55, 53, 59, 57, 61, 62, 68, 66, 70,
72, 7, 64, 63, 51, 39, 42, 29, 38, 54, 9, 50, 17, 56, 19,
32, 24, 12, 2, 1, 6, 5, 11, 10, 15, 14, 18, 20, 21, 22, 26,
27, 30, 31, 33, 35, 37, 40, 43, 42, 46, 48, 52, 49, 55, 53,
59, 57, 61, 64, 68, 66, 70, 72, 7, 62, 63, 51, 39, 44, 29,
38, 54, 9, 56, 50, 17, 19, 32, 12, 24, 41, 2, 1, 6, 7, 11,
10, 15, 14, 18, 20, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40,
43, 42, 46, 48, 49, 52, 55, 53, 59, 57, 61, 64, 68, 66, 70,
72, 5, 62, 63, 51, 39, 44, 9, 38, 54, 29, 17, 56, 50, 19,
32, 12, 41, 24, 67, 2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21,
22, 26, 27, 30, 31, 33, 35, 37, 40, 43, 42, 46, 48, 49, 52,
55, 53, 59, 57, 61, 64, 68, 66, 70, 72, 5, 63, 51, 62, 39,
44, 9, 38, 54, 29, 17, 56, 50, 19, 32, 12, 41, 24, 67, 45,
2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21, 22, 26, 27, 30, 31,
33, 35, 37, 40, 43, 42, 46, 45, 49, 52, 55, 53, 59, 57, 61,
64, 68, 66, 70, 72, 63, 51, 62, 39, 5, 9, 44, 38, 54, 17,
56, 29, 50, 19, 32, 12, 41, 24, 67, 48, 65, 2, 1, 6, 7, 11,
10, 15, 14, 18, 17, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40,
43, 42, 46, 45, 49, 52, 55, 54, 59, 57, 61, 64, 68, 66, 70,
72, 63, 51, 62, 39, 9, 5, 20, 44, 38, 53, 56, 50, 19, 29,
41, 32, 12, 24, 67, 48, 65, 69, 2, 1, 6, 7, 11, 10, 15, 14,
18, 17, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40, 43, 42, 46,
45, 49, 52, 55, 54, 59, 57, 64, 61, 68, 66, 70, 72, 63, 51,
62, 9, 39, 5, 20, 44, 38, 56, 53, 50, 19, 41, 29, 32, 12,
24, 65, 67, 69, 48, 60, 2, 1, 6, 7, 11, 10, 15, 14, 18, 17,
21, 22, 26, 27, 30, 31, 33, 35, 37, 39, 43, 42, 46, 45, 52,
49, 55, 54, 59, 57, 64, 61, 68, 66, 70, 72, 63, 51, 62, 9,
40, 20, 5, 38, 56, 44, 53, 50, 19, 32, 41, 12, 29, 65, 67,
69, 24, 48, 60, 8, 2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21,
22, 26, 27, 30, 31, 33, 35, 37, 39, 43, 42, 46, 45, 52, 49,
55, 56, 59, 57, 64, 61, 68, 66, 70, 72, 63, 51, 62, 9, 17,
40, 5, 38, 54, 53, 50, 19, 44, 32, 12, 41, 29, 65, 67, 69,
60, 8, 36, 24, 48, 2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21,
22, 26, 27, 30, 31, 33, 35, 37, 39, 43, 42, 46, 45, 52, 49,
55, 56, 59, 60, 64, 61, 68, 66, 70, 72, 63, 51, 62, 9, 17,
5, 38, 54, 40, 32, 12, 53, 50, 19, 44, 41, 29, 65, 67, 69,
57, 8, 36, 24, 71, 16, 2, 1, 7, 6, 10, 11, 15, 14, 20, 18,
21, 22, 26, 27, 30, 31, 33, 35, 39, 37, 43, 42, 46, 45, 49,
51, 55, 56, 59, 60, 63, 61, 68, 66, 70, 72, 62, 9, 52, 64,
17, 32, 12, 5, 38, 54, 19, 53, 50, 40, 41, 29, 44, 8, 36,
67, 65, 69, 57, 16, 71, 23, 58, 2, 1, 7, 6, 10, 9, 14, 15,
20, 18, 21, 22, 26, 27, 30, 32, 33, 35, 39, 37, 43, 42, 46,
45, 51, 49, 56, 55, 59, 60, 63, 62, 68, 66, 70, 72, 61, 11,
12, 31, 17, 52, 64, 54, 5, 19, 50, 38, 53, 41, 29, 40, 8,
36, 67, 44, 16, 65, 69, 57, 71, 23, 58, 4, 2, 1, 7, 6, 12,
10, 14, 15, 18, 20, 21, 22, 26, 27, 30, 32, 33, 35, 39, 37,
43, 42, 46, 45, 51, 49, 56, 55, 59, 60, 63, 62, 68, 66, 70,
72, 61, 9, 11, 31, 17, 54, 5, 52, 19, 64, 50, 38, 53, 8,
36, 41, 29, 40, 67, 16, 65, 69, 57, 44, 71, 23, 58, 4, 28,
2, 1, 7, 6, 12, 10, 14, 16, 18, 20, 21, 22, 26, 27, 30, 32,
33, 35, 39, 37, 43, 42, 46, 45, 51, 49, 55, 56, 60, 59, 63,
62, 68, 66, 70, 69, 61, 9, 11, 31, 17, 54, 5, 19, 52, 50,
64, 38, 53, 8, 36, 41, 29, 15, 67, 40, 65, 57, 72, 71, 23,
44, 4, 58, 28, 34, 2, 1, 7, 6, 12, 9, 14, 16, 18, 20, 21,
22, 26, 27, 30, 32, 33, 35, 39, 37, 43, 42, 46, 45, 51, 49,
55, 56, 60, 59, 63, 62, 68, 66, 70, 69, 61, 11, 10, 31, 54,
17, 5, 50, 19, 52, 64, 53, 8, 36, 38, 41, 29, 15, 67, 40,
65, 57, 72, 71, 23, 44, 58, 4, 28, 34, 25, 2, 1, 7, 6, 12,
11, 14, 16, 18, 20, 21, 22, 26, 27, 30, 32, 33, 35, 37, 39,
43, 42, 46, 45, 49, 51, 55, 56, 60, 59, 63, 62, 68, 66, 70,
72, 61, 10, 31, 54, 9, 5, 17, 50, 19, 53, 52, 64, 8, 36,
38, 29, 41, 15, 67, 40, 57, 65, 71, 69, 23, 44, 58, 4, 28,
34, 25, 13, 2, 1, 7, 6, 12, 11, 14, 16, 18, 20, 21, 22, 26,
27, 30, 32, 33, 35, 37, 39, 43, 42, 46, 45, 51, 49, 55, 56,
60, 59, 61, 63, 68, 66, 70, 72, 62, 5, 10, 31, 54, 9, 53,
50, 19, 17, 52, 64, 29, 8, 36, 38, 15, 67, 57, 41, 40, 71,
23, 44, 58, 4, 65, 69, 28, 34, 25, 13, 24, 2, 1, 7, 6, 12,
11, 14, 15, 20, 18, 21, 22, 26, 27, 30, 32, 33, 35, 37, 39,
43, 42, 46, 45, 51, 49, 56, 55, 59, 60, 61, 63, 68, 66, 70,
72, 62, 5, 10, 31, 54, 9, 52, 53, 64, 50, 19, 17, 29, 38,
8, 36, 40, 67, 57, 41, 16, 44, 71, 23, 58, 65, 69, 4, 34,
28, 25, 13, 24, 48, 2, 1, 7, 6, 11, 10, 14, 15, 20, 18, 21,
22, 26, 27, 30, 32, 33, 35, 37, 39, 43, 42, 46, 45, 51, 49,
56, 55, 59, 60, 61, 63, 68, 66, 70, 72, 62, 5, 31, 54, 12,
52, 64, 9, 53, 50, 19, 17, 29, 38, 40, 8, 36, 67, 57, 41,
44, 16, 71, 23, 58, 65, 69, 34, 4, 25, 13, 28, 24, 48, 47,
2, 1, 7, 6, 10, 12, 14, 15, 20, 18, 21, 22, 26, 27, 30, 32,
33, 35, 37, 39, 43, 42, 46, 45, 51, 49, 56, 55, 59, 60, 61,
63, 68, 67, 70, 72, 62, 5, 54, 52, 11, 64, 19, 9, 31, 53,
50, 17, 29, 38, 40, 8, 36, 66, 57, 41, 44, 71, 16, 23, 58,
65, 69, 34, 4, 25, 13, 28, 24, 48, 47, 3, 0.11111111111111109,
0.11111111111111106, 0.090909090909090912, 0.14285714285714288,
0.11111111111111106, 0.11111111111111109, 0.090909090909090912,
0.11111111111111109, 0.090909090909090912, 0.11111111111111106,
0.11111111111111109, 0.11111111111111106, 0.11111111111111109,
0.090909090909090912, 0.090909090909090912, 0.14285714285714288,
0.11111111111111109, 0.090909090909090912, 0.11111111111111106,
0.090909090909090912, 0.14285714285714288, 0.11111111111111106,
0.11111111111111109, 0.090909090909090912, 0.14285714285714288,
0.11111111111111106, 0.11111111111111109, 0.090909090909090912,
0.14285714285714288, 0.090909090909090912, 0.11111111111111106,
0.11111111111111109, 0.090909090909090912, 0.14285714285714288,
0.11111111111111106, 0.14285714285714288, 0.10000000000000001,
0.083333333333333343, 0.1111111111111111, 0.083333333333333343,
0.1111111111111111, 0.083333333333333343, 0.10000000000000001,
0.1111111111111111, 0.16666666666666666, 0.083333333333333343,
0.083333333333333343, 0.10000000000000001, 0.10000000000000001,
0.1111111111111111, 0.1111111111111111, 0.10000000000000001,
0.083333333333333343, 0.1111111111111111, 0.083333333333333343,
0.1111111111111111, 0.1111111111111111, 0.083333333333333343,
0.083333333333333343, 0.10000000000000001, 0.083333333333333343,
0.10000000000000001, 0.10000000000000001, 0.16666666666666666,
0.1111111111111111, 0.10000000000000001, 0.083333333333333343,
0.16666666666666666, 0.16666666666666666, 0.083333333333333343,
0.10000000000000001, 0.16666666666666666, 0.16666666666666666,
0.083333333333333315, 0.09999999999999998, 0.1111111111111111,
0.083333333333333315, 0.09999999999999998, 0.1111111111111111,
0.1111111111111111, 0.083333333333333315, 0.09999999999999998,
0.14285714285714288, 0.09999999999999998, 0.083333333333333315,
0.083333333333333315, 0.1111111111111111, 0.1111111111111111,
0.083333333333333315, 0.09999999999999998, 0.1111111111111111,
0.09999999999999998, 0.14285714285714288, 0.1111111111111111,
0.14285714285714288, 0.09999999999999998, 0.083333333333333315,
0.14285714285714288, 0.09999999999999998, 0.083333333333333315,
0.1111111111111111, 0.1111111111111111, 0.083333333333333315,
0.14285714285714288, 0.083333333333333315, 0.14285714285714288,
0.09999999999999998, 0.083333333333333315, 0.14285714285714288,
0.09999999999999998, 0.083333333333333315, 0.076923076923076913,
0.09999999999999999, 0.076923076923076913, 0.1111111111111111,
0.09999999999999999, 0.1111111111111111, 0.1111111111111111,
0.076923076923076913, 0.09999999999999999, 0.076923076923076913,
0.09999999999999999, 0.14285714285714285, 0.076923076923076913,
0.1111111111111111, 0.1111111111111111, 0.076923076923076913,
0.09999999999999999, 0.1111111111111111, 0.14285714285714285,
0.09999999999999999, 0.1111111111111111, 0.14285714285714285,
0.09999999999999999, 0.076923076923076913, 0.09999999999999999,
0.14285714285714285, 0.076923076923076913, 0.1111111111111111,
0.1111111111111111, 0.076923076923076913, 0.076923076923076913,
0.14285714285714285, 0.14285714285714285, 0.09999999999999999,
0.076923076923076913, 0.14285714285714285, 0.09999999999999999,
0.076923076923076913, 0.076923076923076913, 0.083333333333333384,
0.10000000000000005, 0.083333333333333384, 0.10000000000000001,
0.10000000000000005, 0.10000000000000001, 0.10000000000000001,
0.083333333333333384, 0.10000000000000005, 0.083333333333333384,
0.10000000000000005, 0.125, 0.083333333333333384, 0.10000000000000001,
0.10000000000000001, 0.083333333333333384, 0.10000000000000005,
0.10000000000000001, 0.125, 0.10000000000000005, 0.10000000000000001,
0.125, 0.10000000000000005, 0.125, 0.10000000000000005, 0.083333333333333384,
0.083333333333333384, 0.10000000000000001, 0.10000000000000001,
0.083333333333333384, 0.083333333333333384, 0.10000000000000005,
0.125, 0.10000000000000005, 0.125, 0.083333333333333384,
0.125, 0.125, 0.083333333333333384, 0.10000000000000001,
0.083333333333333329, 0.10000000000000002, 0.083333333333333329,
0.083333333333333329, 0.10000000000000002, 0.083333333333333329,
0.083333333333333329, 0.083333333333333329, 0.10000000000000002,
0.083333333333333329, 0.10000000000000002, 0.14285714285714285,
0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.10000000000000002, 0.083333333333333329,
0.14285714285714285, 0.10000000000000002, 0.14285714285714285,
0.083333333333333329, 0.14285714285714285, 0.10000000000000002,
0.10000000000000002, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.10000000000000002, 0.14285714285714285,
0.10000000000000002, 0.14285714285714285, 0.083333333333333329,
0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.14285714285714285, 0.083333333333333329, 0.083333333333333356,
0.090909090909090898, 0.083333333333333315, 0.083333333333333356,
0.083333333333333315, 0.090909090909090898, 0.083333333333333356,
0.083333333333333315, 0.090909090909090898, 0.083333333333333356,
0.090909090909090898, 0.14285714285714285, 0.083333333333333356,
0.083333333333333315, 0.083333333333333315, 0.083333333333333356,
0.090909090909090898, 0.083333333333333315, 0.14285714285714285,
0.090909090909090898, 0.14285714285714285, 0.083333333333333315,
0.14285714285714285, 0.090909090909090898, 0.090909090909090898,
0.083333333333333356, 0.083333333333333356, 0.083333333333333315,
0.083333333333333315, 0.083333333333333356, 0.083333333333333356,
0.090909090909090898, 0.14285714285714285, 0.090909090909090898,
0.14285714285714285, 0.083333333333333356, 0.083333333333333315,
0.083333333333333315, 0.083333333333333315, 0.083333333333333356,
0.090909090909090898, 0.14285714285714285, 0.083333333333333315,
0.090909090909090884, 0.083333333333333315, 0.083333333333333315,
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"", "", "", "", "", "", "", "", "", "", "", "", "", ""),
c("m", "Observation", "leverage"))), `Modified Cook distance` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3973895182702053,
0.19436020804881535, 0.28360266908399562, 0.071623876325946842,
0.23262560732173548, 0.14066064598286127, 0.0081464743244302751,
0.121689534958224, 0.0094068356197118657, 1.7874168325396351,
0.29017395429594683, 0.0096890675496751708, 0.13898905795267058,
0.042389798197176858, 0.0061698054654052469, 0.047942935704690687,
0.087510472505997264, 0.14719026824520026, 0.0078302809069124325,
0.027367370556315482, 0.099622390425340532, 0.075015367583249018,
0.023110354073981355, 0.03474616343372014, 0.028313443017820614,
0.047352230790548198, 0.039605357966639949, 0.029974197552594693,
0.018813669229200639, 0.023042529384362333, 0.099455688113922788,
0.084321586187053613, 0.019548051118953993, 0.023819153199391787,
0.013929617228502486, 0.022956028393686868), Dims = list(
N = 72L, Q = 2L, qvec = c(Variety = 1, Block = 1, 0,
0), ngrps = c(Variety = 18L, Block = 6L, X = 1L, y = 1L
), ncol = c(Variety = 1, Block = 1, 4, 1)), Call = quote(forsearch_lme(fixed = Yield ~
Date, data = Alfalfa.O, random = ~1 | Block/Variety,
formula = Yield ~ 1 | Block/Variety, response.column = 5,
initial.sample = 4, robs = 2)))
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Alfalfa.O.forlme <-
list(`Number of rows included in Step 1` = 36, Subgroups = structure(list(
GroupNo = 1:18, Block = structure(c(1L, 2L, 3L, 4L, 5L, 6L,
1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("1",
"2", "3", "4", "5", "6"), class = "factor"), Variety = structure(c(2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L,
3L, 3L), .Label = c("Cossack", "Ladak", "Ranger"), class = "factor")), class = "data.frame", row.names = c("1",
"5", "9", "13", "17", "21", "25", "29", "33", "37", "41", "45",
"49", "53", "57", "61", "65", "69")), `Rows by subgroup` = list(
structure(list(Observation = 1:4, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "1", class = "factor"), Yield = c(2.169499813529705,
1.57886424034908, 2.2892581493168307, 2.2298681661614683)), row.names = c(NA,
4L), formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 5:8, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "2", class = "factor"), Yield = c(1.8797458997235463,
1.259357644534691, 1.6006543608149515, 2.0102147529046683
)), row.names = 5:8, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 9:12, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "3", class = "factor"), Yield = c(1.6194712464220358,
1.2205047792648176, 1.668879881007385, 1.8209433055222459
)), row.names = 9:12, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 13:16, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "4", class = "factor"), Yield = c(2.3390459469655021,
1.5889868973164862, 1.9097744930347174, 2.1008141854354605
)), row.names = 13:16, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 17:20, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "5", class = "factor"), Yield = c(1.5804166317690926,
1.2499474222523117, 1.3907547048751305, 1.6590237975889943
)), row.names = 17:20, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 21:24, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ladak", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "6", class = "factor"), Yield = c(1.6607724152983758,
0.94039122682117682, 1.1199750453581907, 1.0998145913551773
)), row.names = 21:24, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 25:28, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "1", class = "factor"), Yield = c(2.3300141638712111,
1.3795979976152408, 1.8590772868157366, 2.2689741776985199
)), row.names = 25:28, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 29:32, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "2", class = "factor"), Yield = c(2.0108889687734401,
1.3008084370035, 1.700956836947642, 1.8105517573704393)), row.names = 29:32, formula = Yield ~
1 | Block/Variety, formulaList = list(Block = ~Block,
Variety = ~Variety), order.groups = list(Block = TRUE,
Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 33:36, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "3", class = "factor"), Yield = c(1.7003247817263896,
1.8510772634953769, 1.8098578831823331, 2.0107397185233209
)), row.names = 33:36, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 37:40, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "4", class = "factor"), Yield = c(1.7808427831682969,
1.0906520904097918, 1.5395848397957161, 1.3991393571047492
)), row.names = 37:40, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 41:44, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "5", class = "factor"), Yield = c(1.4205276081062301,
1.1297666272699873, 1.6693399187827229, 1.3092533135084987
)), row.names = 41:44, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 45:48, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Cossack", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "6", class = "factor"), Yield = c(1.3489911723638548,
1.059039468134817, 0.87943670623840509, 1.0603118524994617
)), row.names = 45:48, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 49:52, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "1", class = "factor"), Yield = c(1.7503767696119295,
1.5209047232245476, 1.5504366380749905, 1.5603037522351157
)), row.names = 49:52, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 53:56, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "2", class = "factor"), Yield = c(1.9511144375333309,
1.4692015904508826, 1.6106152474723587, 1.7194024313811678
)), row.names = 53:56, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 57:60, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "3", class = "factor"), Yield = c(2.1291577150236645,
1.7996942231537436, 1.8194098413486923, 1.9899882783857175
)), row.names = 57:60, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 61:64, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "4", class = "factor"), Yield = c(1.7795938556533393,
1.3703050803974144, 1.5597517608164344, 1.5507407127731396
)), row.names = 61:64, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 65:68, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "5", class = "factor"), Yield = c(1.3096243541663386,
1.0096022962922739, 1.2292984347082463, 1.5098438188351098
)), row.names = 65:68, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
Block = TRUE, Variety = TRUE), FUN = function (x)
max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
"data.frame")), structure(list(Observation = 69:72, Variety = structure(c(1L,
1L, 1L, 1L), .Label = "Ranger", class = "factor"), Date = structure(1:4, .Label = c("None",
"S1", "S20", "O7"), class = "factor"), Block = structure(c(1L,
1L, 1L, 1L), .Label = "6", class = "factor"), Yield = c(1.2999826783250847,
1.3094017614998898, 1.1307424366353502, 1.3292403989133892
)), row.names = 69:72, formula = Yield ~ 1 | Block/Variety, formulaList = list(
Block = ~Block, Variety = ~Variety), order.groups = list(
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max(x, na.rm = TRUE), class = c("nmGroupedData", "groupedData",
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NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL,
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NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL,
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50L, 51L, 52L, 53L, 54L, 55L, 56L, 59L, 61L, 62L, 63L, 64L,
65L, 68L), c(2L, 1L, 7L, 5L, 11L, 9L, 14L, 15L, 20L, 17L,
21L, 22L, 26L, 27L, 31L, 30L, 33L, 35L, 37L, 39L, 43L, 41L,
45L, 46L, 49L, 50L, 54L, 56L, 59L, 58L, 61L, 64L, 68L, 65L,
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44L, 45L, 46L, 52L, 49L, 54L, 55L, 59L, 58L, 64L, 62L, 68L,
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14L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L,
37L, 43L, 44L, 45L, 46L, 49L, 52L, 54L, 55L, 59L, 58L, 62L,
64L, 68L, 65L, 70L, 72L, 61L, 50L, 10L), c(2L, 1L, 6L, 7L,
9L, 11L, 15L, 14L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L,
33L, 35L, 40L, 37L, 43L, 44L, 45L, 48L, 49L, 50L, 54L, 55L,
59L, 58L, 62L, 61L, 68L, 65L, 72L, 70L, 64L, 52L, 10L, 63L
), c(2L, 1L, 7L, 6L, 9L, 11L, 14L, 15L, 17L, 18L, 21L, 24L,
26L, 27L, 31L, 30L, 33L, 35L, 40L, 37L, 44L, 43L, 48L, 45L,
49L, 50L, 54L, 55L, 59L, 58L, 62L, 61L, 68L, 65L, 72L, 70L,
63L, 51L, 39L, 64L, 10L), c(2L, 1L, 7L, 6L, 11L, 9L, 14L,
15L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L,
37L, 44L, 43L, 48L, 45L, 49L, 50L, 54L, 55L, 59L, 58L, 62L,
61L, 68L, 65L, 72L, 70L, 63L, 51L, 39L, 10L, 5L, 64L), c(2L,
1L, 7L, 6L, 11L, 9L, 14L, 15L, 17L, 18L, 21L, 24L, 26L, 27L,
31L, 30L, 33L, 35L, 40L, 37L, 44L, 43L, 48L, 45L, 49L, 50L,
54L, 55L, 59L, 58L, 62L, 61L, 68L, 65L, 72L, 70L, 63L, 51L,
39L, 5L, 10L, 64L, 52L), c(2L, 1L, 7L, 6L, 11L, 9L, 14L,
15L, 17L, 18L, 21L, 24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L,
37L, 43L, 44L, 48L, 45L, 49L, 50L, 54L, 55L, 59L, 58L, 62L,
61L, 68L, 65L, 72L, 70L, 63L, 51L, 39L, 5L, 64L, 10L, 52L,
53L), c(2L, 1L, 7L, 6L, 11L, 9L, 14L, 15L, 17L, 18L, 21L,
24L, 26L, 27L, 31L, 30L, 33L, 35L, 40L, 37L, 43L, 44L, 48L,
46L, 49L, 50L, 54L, 55L, 59L, 58L, 62L, 61L, 68L, 65L, 72L,
70L, 63L, 51L, 39L, 5L, 64L, 10L, 52L, 53L, 41L), c(2L, 1L,
7L, 6L, 11L, 9L, 14L, 15L, 17L, 18L, 21L, 24L, 26L, 27L,
31L, 30L, 33L, 35L, 40L, 37L, 43L, 44L, 48L, 46L, 49L, 50L,
54L, 55L, 59L, 58L, 62L, 61L, 68L, 66L, 72L, 70L, 63L, 51L,
39L, 5L, 10L, 64L, 52L, 53L, 42L, 29L), c(2L, 1L, 5L, 6L,
11L, 10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 31L, 30L,
33L, 35L, 37L, 40L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 54L,
59L, 57L, 62L, 61L, 68L, 66L, 72L, 70L, 7L, 63L, 53L, 39L,
50L, 9L, 64L, 29L, 52L, 42L, 38L), c(2L, 1L, 6L, 5L, 11L,
10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 30L, 31L, 35L,
33L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 53L, 59L,
57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L, 39L, 54L, 42L,
29L, 64L, 52L, 38L, 9L, 50L, 19L), c(2L, 1L, 6L, 5L, 11L,
10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 30L, 31L, 33L,
35L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 53L, 59L,
57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L, 39L, 54L, 42L,
29L, 64L, 52L, 38L, 9L, 50L, 19L, 20L), c(2L, 1L, 6L, 5L,
11L, 10L, 15L, 14L, 18L, 17L, 21L, 24L, 26L, 27L, 30L, 31L,
33L, 35L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L, 55L, 53L,
59L, 57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L, 39L, 64L,
54L, 42L, 52L, 29L, 38L, 9L, 50L, 20L, 19L, 56L), c(2L, 1L,
6L, 5L, 11L, 10L, 15L, 14L, 18L, 20L, 21L, 24L, 26L, 27L,
30L, 31L, 33L, 35L, 40L, 37L, 43L, 44L, 46L, 48L, 49L, 51L,
55L, 53L, 59L, 57L, 61L, 62L, 68L, 66L, 70L, 72L, 7L, 63L,
39L, 64L, 52L, 54L, 42L, 29L, 38L, 9L, 50L, 17L, 19L, 56L,
22L), c(2L, 1L, 6L, 5L, 11L, 10L, 15L, 14L, 18L, 20L, 21L,
24L, 26L, 27L, 30L, 31L, 33L, 35L, 40L, 37L, 43L, 44L, 46L,
48L, 49L, 51L, 55L, 53L, 59L, 57L, 61L, 62L, 68L, 66L, 70L,
72L, 7L, 63L, 39L, 64L, 52L, 42L, 29L, 38L, 54L, 9L, 50L,
17L, 19L, 56L, 22L, 32L), c(2L, 1L, 6L, 5L, 11L, 10L, 15L,
14L, 18L, 20L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L,
40L, 43L, 44L, 46L, 48L, 49L, 52L, 55L, 53L, 59L, 57L, 61L,
62L, 68L, 66L, 70L, 72L, 7L, 64L, 63L, 51L, 39L, 42L, 29L,
38L, 54L, 9L, 50L, 17L, 56L, 19L, 32L, 24L, 12L), c(2L, 1L,
6L, 5L, 11L, 10L, 15L, 14L, 18L, 20L, 21L, 22L, 26L, 27L,
30L, 31L, 33L, 35L, 37L, 40L, 43L, 42L, 46L, 48L, 52L, 49L,
55L, 53L, 59L, 57L, 61L, 64L, 68L, 66L, 70L, 72L, 7L, 62L,
63L, 51L, 39L, 44L, 29L, 38L, 54L, 9L, 56L, 50L, 17L, 19L,
32L, 12L, 24L, 41L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L,
18L, 20L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 40L,
43L, 42L, 46L, 48L, 49L, 52L, 55L, 53L, 59L, 57L, 61L, 64L,
68L, 66L, 70L, 72L, 5L, 62L, 63L, 51L, 39L, 44L, 9L, 38L,
54L, 29L, 17L, 56L, 50L, 19L, 32L, 12L, 41L, 24L, 67L), c(2L,
1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 20L, 21L, 22L, 26L,
27L, 30L, 31L, 33L, 35L, 37L, 40L, 43L, 42L, 46L, 48L, 49L,
52L, 55L, 53L, 59L, 57L, 61L, 64L, 68L, 66L, 70L, 72L, 5L,
63L, 51L, 62L, 39L, 44L, 9L, 38L, 54L, 29L, 17L, 56L, 50L,
19L, 32L, 12L, 41L, 24L, 67L, 45L), c(2L, 1L, 6L, 7L, 11L,
10L, 15L, 14L, 18L, 20L, 21L, 22L, 26L, 27L, 30L, 31L, 33L,
35L, 37L, 40L, 43L, 42L, 46L, 45L, 49L, 52L, 55L, 53L, 59L,
57L, 61L, 64L, 68L, 66L, 70L, 72L, 63L, 51L, 62L, 39L, 5L,
9L, 44L, 38L, 54L, 17L, 56L, 29L, 50L, 19L, 32L, 12L, 41L,
24L, 67L, 48L, 65L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L,
18L, 17L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 40L,
43L, 42L, 46L, 45L, 49L, 52L, 55L, 54L, 59L, 57L, 61L, 64L,
68L, 66L, 70L, 72L, 63L, 51L, 62L, 39L, 9L, 5L, 20L, 44L,
38L, 53L, 56L, 50L, 19L, 29L, 41L, 32L, 12L, 24L, 67L, 48L,
65L, 69L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 17L,
21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 40L, 43L, 42L,
46L, 45L, 49L, 52L, 55L, 54L, 59L, 57L, 64L, 61L, 68L, 66L,
70L, 72L, 63L, 51L, 62L, 9L, 39L, 5L, 20L, 44L, 38L, 56L,
53L, 50L, 19L, 41L, 29L, 32L, 12L, 24L, 65L, 67L, 69L, 48L,
60L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 17L, 21L,
22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 39L, 43L, 42L, 46L,
45L, 52L, 49L, 55L, 54L, 59L, 57L, 64L, 61L, 68L, 66L, 70L,
72L, 63L, 51L, 62L, 9L, 40L, 20L, 5L, 38L, 56L, 44L, 53L,
50L, 19L, 32L, 41L, 12L, 29L, 65L, 67L, 69L, 24L, 48L, 60L,
8L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 20L, 21L,
22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 39L, 43L, 42L, 46L,
45L, 52L, 49L, 55L, 56L, 59L, 57L, 64L, 61L, 68L, 66L, 70L,
72L, 63L, 51L, 62L, 9L, 17L, 40L, 5L, 38L, 54L, 53L, 50L,
19L, 44L, 32L, 12L, 41L, 29L, 65L, 67L, 69L, 60L, 8L, 36L,
24L, 48L), c(2L, 1L, 6L, 7L, 11L, 10L, 15L, 14L, 18L, 20L,
21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 37L, 39L, 43L, 42L,
46L, 45L, 52L, 49L, 55L, 56L, 59L, 60L, 64L, 61L, 68L, 66L,
70L, 72L, 63L, 51L, 62L, 9L, 17L, 5L, 38L, 54L, 40L, 32L,
12L, 53L, 50L, 19L, 44L, 41L, 29L, 65L, 67L, 69L, 57L, 8L,
36L, 24L, 71L, 16L), c(2L, 1L, 7L, 6L, 10L, 11L, 15L, 14L,
20L, 18L, 21L, 22L, 26L, 27L, 30L, 31L, 33L, 35L, 39L, 37L,
43L, 42L, 46L, 45L, 49L, 51L, 55L, 56L, 59L, 60L, 63L, 61L,
68L, 66L, 70L, 72L, 62L, 9L, 52L, 64L, 17L, 32L, 12L, 5L,
38L, 54L, 19L, 53L, 50L, 40L, 41L, 29L, 44L, 8L, 36L, 67L,
65L, 69L, 57L, 16L, 71L, 23L, 58L), c(2L, 1L, 7L, 6L, 10L,
9L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L, 30L, 32L, 33L,
35L, 39L, 37L, 43L, 42L, 46L, 45L, 51L, 49L, 56L, 55L, 59L,
60L, 63L, 62L, 68L, 66L, 70L, 72L, 61L, 11L, 12L, 31L, 17L,
52L, 64L, 54L, 5L, 19L, 50L, 38L, 53L, 41L, 29L, 40L, 8L,
36L, 67L, 44L, 16L, 65L, 69L, 57L, 71L, 23L, 58L, 4L), c(2L,
1L, 7L, 6L, 12L, 10L, 14L, 15L, 18L, 20L, 21L, 22L, 26L,
27L, 30L, 32L, 33L, 35L, 39L, 37L, 43L, 42L, 46L, 45L, 51L,
49L, 56L, 55L, 59L, 60L, 63L, 62L, 68L, 66L, 70L, 72L, 61L,
9L, 11L, 31L, 17L, 54L, 5L, 52L, 19L, 64L, 50L, 38L, 53L,
8L, 36L, 41L, 29L, 40L, 67L, 16L, 65L, 69L, 57L, 44L, 71L,
23L, 58L, 4L, 28L), c(2L, 1L, 7L, 6L, 12L, 10L, 14L, 16L,
18L, 20L, 21L, 22L, 26L, 27L, 30L, 32L, 33L, 35L, 39L, 37L,
43L, 42L, 46L, 45L, 51L, 49L, 55L, 56L, 60L, 59L, 63L, 62L,
68L, 66L, 70L, 69L, 61L, 9L, 11L, 31L, 17L, 54L, 5L, 19L,
52L, 50L, 64L, 38L, 53L, 8L, 36L, 41L, 29L, 15L, 67L, 40L,
65L, 57L, 72L, 71L, 23L, 44L, 4L, 58L, 28L, 34L), c(2L, 1L,
7L, 6L, 12L, 9L, 14L, 16L, 18L, 20L, 21L, 22L, 26L, 27L,
30L, 32L, 33L, 35L, 39L, 37L, 43L, 42L, 46L, 45L, 51L, 49L,
55L, 56L, 60L, 59L, 63L, 62L, 68L, 66L, 70L, 69L, 61L, 11L,
10L, 31L, 54L, 17L, 5L, 50L, 19L, 52L, 64L, 53L, 8L, 36L,
38L, 41L, 29L, 15L, 67L, 40L, 65L, 57L, 72L, 71L, 23L, 44L,
58L, 4L, 28L, 34L, 25L), c(2L, 1L, 7L, 6L, 12L, 11L, 14L,
16L, 18L, 20L, 21L, 22L, 26L, 27L, 30L, 32L, 33L, 35L, 37L,
39L, 43L, 42L, 46L, 45L, 49L, 51L, 55L, 56L, 60L, 59L, 63L,
62L, 68L, 66L, 70L, 72L, 61L, 10L, 31L, 54L, 9L, 5L, 17L,
50L, 19L, 53L, 52L, 64L, 8L, 36L, 38L, 29L, 41L, 15L, 67L,
40L, 57L, 65L, 71L, 69L, 23L, 44L, 58L, 4L, 28L, 34L, 25L,
13L), c(2L, 1L, 7L, 6L, 12L, 11L, 14L, 16L, 18L, 20L, 21L,
22L, 26L, 27L, 30L, 32L, 33L, 35L, 37L, 39L, 43L, 42L, 46L,
45L, 51L, 49L, 55L, 56L, 60L, 59L, 61L, 63L, 68L, 66L, 70L,
72L, 62L, 5L, 10L, 31L, 54L, 9L, 53L, 50L, 19L, 17L, 52L,
64L, 29L, 8L, 36L, 38L, 15L, 67L, 57L, 41L, 40L, 71L, 23L,
44L, 58L, 4L, 65L, 69L, 28L, 34L, 25L, 13L, 24L), c(2L, 1L,
7L, 6L, 12L, 11L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L,
30L, 32L, 33L, 35L, 37L, 39L, 43L, 42L, 46L, 45L, 51L, 49L,
56L, 55L, 59L, 60L, 61L, 63L, 68L, 66L, 70L, 72L, 62L, 5L,
10L, 31L, 54L, 9L, 52L, 53L, 64L, 50L, 19L, 17L, 29L, 38L,
8L, 36L, 40L, 67L, 57L, 41L, 16L, 44L, 71L, 23L, 58L, 65L,
69L, 4L, 34L, 28L, 25L, 13L, 24L, 48L), c(2L, 1L, 7L, 6L,
11L, 10L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L, 30L, 32L,
33L, 35L, 37L, 39L, 43L, 42L, 46L, 45L, 51L, 49L, 56L, 55L,
59L, 60L, 61L, 63L, 68L, 66L, 70L, 72L, 62L, 5L, 31L, 54L,
12L, 52L, 64L, 9L, 53L, 50L, 19L, 17L, 29L, 38L, 40L, 8L,
36L, 67L, 57L, 41L, 44L, 16L, 71L, 23L, 58L, 65L, 69L, 34L,
4L, 25L, 13L, 28L, 24L, 48L, 47L), c(2L, 1L, 7L, 6L, 10L,
12L, 14L, 15L, 20L, 18L, 21L, 22L, 26L, 27L, 30L, 32L, 33L,
35L, 37L, 39L, 43L, 42L, 46L, 45L, 51L, 49L, 56L, 55L, 59L,
60L, 61L, 63L, 68L, 67L, 70L, 72L, 62L, 5L, 54L, 52L, 11L,
64L, 19L, 9L, 31L, 53L, 50L, 17L, 29L, 38L, 40L, 8L, 36L,
66L, 57L, 41L, 44L, 71L, 16L, 23L, 58L, 65L, 69L, 34L, 4L,
25L, 13L, 28L, 24L, 48L, 47L, 3L)), Sigma = 0.32445697002307838,
`Standardized residuals` = structure(c(0, 0, 0, 0, 0, 0,
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40, 40, 40, 40, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41,
41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41,
41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41, 41,
42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42,
42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42,
42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 43, 43, 43,
43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43,
43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43,
43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44,
44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44,
44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44, 44,
44, 44, 44, 44, 44, 44, 44, 44, 44, 45, 45, 45, 45, 45, 45,
45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,
45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45,
45, 45, 45, 45, 45, 45, 45, 45, 45, 46, 46, 46, 46, 46, 46,
46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46,
46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46,
46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47,
47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47,
47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47,
47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 47, 48, 48, 48,
48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48,
48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48,
48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48,
49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49,
49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49,
49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49,
49, 49, 49, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50,
50, 50, 50, 50, 50, 50, 50, 50, 50, 51, 51, 51, 51, 51, 51,
51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51,
51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51,
51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 51,
52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52,
52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52,
52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52,
52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53, 53, 53,
53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53,
53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53,
53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53,
54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54,
54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54,
54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54, 54,
54, 54, 54, 54, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 55,
55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55,
55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56,
56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56,
56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56,
56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56,
57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57,
57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57,
57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57,
57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 57, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58,
58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59,
59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59,
59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59,
59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59,
59, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60, 60,
60, 60, 60, 60, 60, 60, 60, 60, 60, 61, 61, 61, 61, 61, 61,
61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61,
61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61,
61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61,
61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62,
62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62,
62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62,
62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62,
62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 63, 63, 63,
63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63, 63,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64,
64, 64, 64, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65,
65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 67,
67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69,
69, 69, 69, 69, 69, 69, 69, 69, 69, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70, 70,
70, 70, 70, 70, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72,
72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 2, 5, 7,
8, 9, 10, 11, 14, 15, 17, 18, 21, 26, 27, 31, 32, 34, 35,
37, 39, 44, 49, 50, 51, 52, 53, 54, 55, 56, 59, 61, 62, 63,
64, 65, 68, 2, 1, 7, 5, 11, 9, 14, 15, 20, 17, 21, 22, 26,
27, 31, 30, 33, 35, 37, 39, 43, 41, 45, 46, 49, 50, 54, 56,
59, 58, 61, 64, 68, 65, 70, 72, 52, 2, 1, 7, 6, 9, 11, 15,
14, 17, 20, 21, 22, 26, 27, 31, 30, 33, 35, 37, 40, 43, 44,
45, 46, 52, 49, 54, 55, 59, 58, 64, 62, 68, 65, 70, 72, 61,
50, 2, 1, 6, 7, 9, 11, 15, 14, 17, 18, 21, 24, 26, 27, 31,
30, 33, 35, 40, 37, 43, 44, 45, 46, 49, 52, 54, 55, 59, 58,
62, 64, 68, 65, 70, 72, 61, 50, 10, 2, 1, 6, 7, 9, 11, 15,
14, 17, 18, 21, 24, 26, 27, 31, 30, 33, 35, 40, 37, 43, 44,
45, 48, 49, 50, 54, 55, 59, 58, 62, 61, 68, 65, 72, 70, 64,
52, 10, 63, 2, 1, 7, 6, 9, 11, 14, 15, 17, 18, 21, 24, 26,
27, 31, 30, 33, 35, 40, 37, 44, 43, 48, 45, 49, 50, 54, 55,
59, 58, 62, 61, 68, 65, 72, 70, 63, 51, 39, 64, 10, 2, 1,
7, 6, 11, 9, 14, 15, 17, 18, 21, 24, 26, 27, 31, 30, 33,
35, 40, 37, 44, 43, 48, 45, 49, 50, 54, 55, 59, 58, 62, 61,
68, 65, 72, 70, 63, 51, 39, 10, 5, 64, 2, 1, 7, 6, 11, 9,
14, 15, 17, 18, 21, 24, 26, 27, 31, 30, 33, 35, 40, 37, 44,
43, 48, 45, 49, 50, 54, 55, 59, 58, 62, 61, 68, 65, 72, 70,
63, 51, 39, 5, 10, 64, 52, 2, 1, 7, 6, 11, 9, 14, 15, 17,
18, 21, 24, 26, 27, 31, 30, 33, 35, 40, 37, 43, 44, 48, 45,
49, 50, 54, 55, 59, 58, 62, 61, 68, 65, 72, 70, 63, 51, 39,
5, 64, 10, 52, 53, 2, 1, 7, 6, 11, 9, 14, 15, 17, 18, 21,
24, 26, 27, 31, 30, 33, 35, 40, 37, 43, 44, 48, 46, 49, 50,
54, 55, 59, 58, 62, 61, 68, 65, 72, 70, 63, 51, 39, 5, 64,
10, 52, 53, 41, 2, 1, 7, 6, 11, 9, 14, 15, 17, 18, 21, 24,
26, 27, 31, 30, 33, 35, 40, 37, 43, 44, 48, 46, 49, 50, 54,
55, 59, 58, 62, 61, 68, 66, 72, 70, 63, 51, 39, 5, 10, 64,
52, 53, 42, 29, 2, 1, 5, 6, 11, 10, 15, 14, 18, 17, 21, 24,
26, 27, 31, 30, 33, 35, 37, 40, 43, 44, 46, 48, 49, 51, 55,
54, 59, 57, 62, 61, 68, 66, 72, 70, 7, 63, 53, 39, 50, 9,
64, 29, 52, 42, 38, 2, 1, 6, 5, 11, 10, 15, 14, 18, 17, 21,
24, 26, 27, 30, 31, 35, 33, 40, 37, 43, 44, 46, 48, 49, 51,
55, 53, 59, 57, 61, 62, 68, 66, 70, 72, 7, 63, 39, 54, 42,
29, 64, 52, 38, 9, 50, 19, 2, 1, 6, 5, 11, 10, 15, 14, 18,
17, 21, 24, 26, 27, 30, 31, 33, 35, 40, 37, 43, 44, 46, 48,
49, 51, 55, 53, 59, 57, 61, 62, 68, 66, 70, 72, 7, 63, 39,
54, 42, 29, 64, 52, 38, 9, 50, 19, 20, 2, 1, 6, 5, 11, 10,
15, 14, 18, 17, 21, 24, 26, 27, 30, 31, 33, 35, 40, 37, 43,
44, 46, 48, 49, 51, 55, 53, 59, 57, 61, 62, 68, 66, 70, 72,
7, 63, 39, 64, 54, 42, 52, 29, 38, 9, 50, 20, 19, 56, 2,
1, 6, 5, 11, 10, 15, 14, 18, 20, 21, 24, 26, 27, 30, 31,
33, 35, 40, 37, 43, 44, 46, 48, 49, 51, 55, 53, 59, 57, 61,
62, 68, 66, 70, 72, 7, 63, 39, 64, 52, 54, 42, 29, 38, 9,
50, 17, 19, 56, 22, 2, 1, 6, 5, 11, 10, 15, 14, 18, 20, 21,
24, 26, 27, 30, 31, 33, 35, 40, 37, 43, 44, 46, 48, 49, 51,
55, 53, 59, 57, 61, 62, 68, 66, 70, 72, 7, 63, 39, 64, 52,
42, 29, 38, 54, 9, 50, 17, 19, 56, 22, 32, 2, 1, 6, 5, 11,
10, 15, 14, 18, 20, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40,
43, 44, 46, 48, 49, 52, 55, 53, 59, 57, 61, 62, 68, 66, 70,
72, 7, 64, 63, 51, 39, 42, 29, 38, 54, 9, 50, 17, 56, 19,
32, 24, 12, 2, 1, 6, 5, 11, 10, 15, 14, 18, 20, 21, 22, 26,
27, 30, 31, 33, 35, 37, 40, 43, 42, 46, 48, 52, 49, 55, 53,
59, 57, 61, 64, 68, 66, 70, 72, 7, 62, 63, 51, 39, 44, 29,
38, 54, 9, 56, 50, 17, 19, 32, 12, 24, 41, 2, 1, 6, 7, 11,
10, 15, 14, 18, 20, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40,
43, 42, 46, 48, 49, 52, 55, 53, 59, 57, 61, 64, 68, 66, 70,
72, 5, 62, 63, 51, 39, 44, 9, 38, 54, 29, 17, 56, 50, 19,
32, 12, 41, 24, 67, 2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21,
22, 26, 27, 30, 31, 33, 35, 37, 40, 43, 42, 46, 48, 49, 52,
55, 53, 59, 57, 61, 64, 68, 66, 70, 72, 5, 63, 51, 62, 39,
44, 9, 38, 54, 29, 17, 56, 50, 19, 32, 12, 41, 24, 67, 45,
2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21, 22, 26, 27, 30, 31,
33, 35, 37, 40, 43, 42, 46, 45, 49, 52, 55, 53, 59, 57, 61,
64, 68, 66, 70, 72, 63, 51, 62, 39, 5, 9, 44, 38, 54, 17,
56, 29, 50, 19, 32, 12, 41, 24, 67, 48, 65, 2, 1, 6, 7, 11,
10, 15, 14, 18, 17, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40,
43, 42, 46, 45, 49, 52, 55, 54, 59, 57, 61, 64, 68, 66, 70,
72, 63, 51, 62, 39, 9, 5, 20, 44, 38, 53, 56, 50, 19, 29,
41, 32, 12, 24, 67, 48, 65, 69, 2, 1, 6, 7, 11, 10, 15, 14,
18, 17, 21, 22, 26, 27, 30, 31, 33, 35, 37, 40, 43, 42, 46,
45, 49, 52, 55, 54, 59, 57, 64, 61, 68, 66, 70, 72, 63, 51,
62, 9, 39, 5, 20, 44, 38, 56, 53, 50, 19, 41, 29, 32, 12,
24, 65, 67, 69, 48, 60, 2, 1, 6, 7, 11, 10, 15, 14, 18, 17,
21, 22, 26, 27, 30, 31, 33, 35, 37, 39, 43, 42, 46, 45, 52,
49, 55, 54, 59, 57, 64, 61, 68, 66, 70, 72, 63, 51, 62, 9,
40, 20, 5, 38, 56, 44, 53, 50, 19, 32, 41, 12, 29, 65, 67,
69, 24, 48, 60, 8, 2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21,
22, 26, 27, 30, 31, 33, 35, 37, 39, 43, 42, 46, 45, 52, 49,
55, 56, 59, 57, 64, 61, 68, 66, 70, 72, 63, 51, 62, 9, 17,
40, 5, 38, 54, 53, 50, 19, 44, 32, 12, 41, 29, 65, 67, 69,
60, 8, 36, 24, 48, 2, 1, 6, 7, 11, 10, 15, 14, 18, 20, 21,
22, 26, 27, 30, 31, 33, 35, 37, 39, 43, 42, 46, 45, 52, 49,
55, 56, 59, 60, 64, 61, 68, 66, 70, 72, 63, 51, 62, 9, 17,
5, 38, 54, 40, 32, 12, 53, 50, 19, 44, 41, 29, 65, 67, 69,
57, 8, 36, 24, 71, 16, 2, 1, 7, 6, 10, 11, 15, 14, 20, 18,
21, 22, 26, 27, 30, 31, 33, 35, 39, 37, 43, 42, 46, 45, 49,
51, 55, 56, 59, 60, 63, 61, 68, 66, 70, 72, 62, 9, 52, 64,
17, 32, 12, 5, 38, 54, 19, 53, 50, 40, 41, 29, 44, 8, 36,
67, 65, 69, 57, 16, 71, 23, 58, 2, 1, 7, 6, 10, 9, 14, 15,
20, 18, 21, 22, 26, 27, 30, 32, 33, 35, 39, 37, 43, 42, 46,
45, 51, 49, 56, 55, 59, 60, 63, 62, 68, 66, 70, 72, 61, 11,
12, 31, 17, 52, 64, 54, 5, 19, 50, 38, 53, 41, 29, 40, 8,
36, 67, 44, 16, 65, 69, 57, 71, 23, 58, 4, 2, 1, 7, 6, 12,
10, 14, 15, 18, 20, 21, 22, 26, 27, 30, 32, 33, 35, 39, 37,
43, 42, 46, 45, 51, 49, 56, 55, 59, 60, 63, 62, 68, 66, 70,
72, 61, 9, 11, 31, 17, 54, 5, 52, 19, 64, 50, 38, 53, 8,
36, 41, 29, 40, 67, 16, 65, 69, 57, 44, 71, 23, 58, 4, 28,
2, 1, 7, 6, 12, 10, 14, 16, 18, 20, 21, 22, 26, 27, 30, 32,
33, 35, 39, 37, 43, 42, 46, 45, 51, 49, 55, 56, 60, 59, 63,
62, 68, 66, 70, 69, 61, 9, 11, 31, 17, 54, 5, 19, 52, 50,
64, 38, 53, 8, 36, 41, 29, 15, 67, 40, 65, 57, 72, 71, 23,
44, 4, 58, 28, 34, 2, 1, 7, 6, 12, 9, 14, 16, 18, 20, 21,
22, 26, 27, 30, 32, 33, 35, 39, 37, 43, 42, 46, 45, 51, 49,
55, 56, 60, 59, 63, 62, 68, 66, 70, 69, 61, 11, 10, 31, 54,
17, 5, 50, 19, 52, 64, 53, 8, 36, 38, 41, 29, 15, 67, 40,
65, 57, 72, 71, 23, 44, 58, 4, 28, 34, 25, 2, 1, 7, 6, 12,
11, 14, 16, 18, 20, 21, 22, 26, 27, 30, 32, 33, 35, 37, 39,
43, 42, 46, 45, 49, 51, 55, 56, 60, 59, 63, 62, 68, 66, 70,
72, 61, 10, 31, 54, 9, 5, 17, 50, 19, 53, 52, 64, 8, 36,
38, 29, 41, 15, 67, 40, 57, 65, 71, 69, 23, 44, 58, 4, 28,
34, 25, 13, 2, 1, 7, 6, 12, 11, 14, 16, 18, 20, 21, 22, 26,
27, 30, 32, 33, 35, 37, 39, 43, 42, 46, 45, 51, 49, 55, 56,
60, 59, 61, 63, 68, 66, 70, 72, 62, 5, 10, 31, 54, 9, 53,
50, 19, 17, 52, 64, 29, 8, 36, 38, 15, 67, 57, 41, 40, 71,
23, 44, 58, 4, 65, 69, 28, 34, 25, 13, 24, 2, 1, 7, 6, 12,
11, 14, 15, 20, 18, 21, 22, 26, 27, 30, 32, 33, 35, 37, 39,
43, 42, 46, 45, 51, 49, 56, 55, 59, 60, 61, 63, 68, 66, 70,
72, 62, 5, 10, 31, 54, 9, 52, 53, 64, 50, 19, 17, 29, 38,
8, 36, 40, 67, 57, 41, 16, 44, 71, 23, 58, 65, 69, 4, 34,
28, 25, 13, 24, 48, 2, 1, 7, 6, 11, 10, 14, 15, 20, 18, 21,
22, 26, 27, 30, 32, 33, 35, 37, 39, 43, 42, 46, 45, 51, 49,
56, 55, 59, 60, 61, 63, 68, 66, 70, 72, 62, 5, 31, 54, 12,
52, 64, 9, 53, 50, 19, 17, 29, 38, 40, 8, 36, 67, 57, 41,
44, 16, 71, 23, 58, 65, 69, 34, 4, 25, 13, 28, 24, 48, 47,
2, 1, 7, 6, 10, 12, 14, 15, 20, 18, 21, 22, 26, 27, 30, 32,
33, 35, 37, 39, 43, 42, 46, 45, 51, 49, 56, 55, 59, 60, 61,
63, 68, 67, 70, 72, 62, 5, 54, 52, 11, 64, 19, 9, 31, 53,
50, 17, 29, 38, 40, 8, 36, 66, 57, 41, 44, 71, 16, 23, 58,
65, 69, 34, 4, 25, 13, 28, 24, 48, 47, 3, 0.11111111111111109,
0.11111111111111106, 0.090909090909090912, 0.14285714285714288,
0.11111111111111106, 0.11111111111111109, 0.090909090909090912,
0.11111111111111109, 0.090909090909090912, 0.11111111111111106,
0.11111111111111109, 0.11111111111111106, 0.11111111111111109,
0.090909090909090912, 0.090909090909090912, 0.14285714285714288,
0.11111111111111109, 0.090909090909090912, 0.11111111111111106,
0.090909090909090912, 0.14285714285714288, 0.11111111111111106,
0.11111111111111109, 0.090909090909090912, 0.14285714285714288,
0.11111111111111106, 0.11111111111111109, 0.090909090909090912,
0.14285714285714288, 0.090909090909090912, 0.11111111111111106,
0.11111111111111109, 0.090909090909090912, 0.14285714285714288,
0.11111111111111106, 0.14285714285714288, 0.10000000000000001,
0.083333333333333343, 0.1111111111111111, 0.083333333333333343,
0.1111111111111111, 0.083333333333333343, 0.10000000000000001,
0.1111111111111111, 0.16666666666666666, 0.083333333333333343,
0.083333333333333343, 0.10000000000000001, 0.10000000000000001,
0.1111111111111111, 0.1111111111111111, 0.10000000000000001,
0.083333333333333343, 0.1111111111111111, 0.083333333333333343,
0.1111111111111111, 0.1111111111111111, 0.083333333333333343,
0.083333333333333343, 0.10000000000000001, 0.083333333333333343,
0.10000000000000001, 0.10000000000000001, 0.16666666666666666,
0.1111111111111111, 0.10000000000000001, 0.083333333333333343,
0.16666666666666666, 0.16666666666666666, 0.083333333333333343,
0.10000000000000001, 0.16666666666666666, 0.16666666666666666,
0.083333333333333315, 0.09999999999999998, 0.1111111111111111,
0.083333333333333315, 0.09999999999999998, 0.1111111111111111,
0.1111111111111111, 0.083333333333333315, 0.09999999999999998,
0.14285714285714288, 0.09999999999999998, 0.083333333333333315,
0.083333333333333315, 0.1111111111111111, 0.1111111111111111,
0.083333333333333315, 0.09999999999999998, 0.1111111111111111,
0.09999999999999998, 0.14285714285714288, 0.1111111111111111,
0.14285714285714288, 0.09999999999999998, 0.083333333333333315,
0.14285714285714288, 0.09999999999999998, 0.083333333333333315,
0.1111111111111111, 0.1111111111111111, 0.083333333333333315,
0.14285714285714288, 0.083333333333333315, 0.14285714285714288,
0.09999999999999998, 0.083333333333333315, 0.14285714285714288,
0.09999999999999998, 0.083333333333333315, 0.076923076923076913,
0.09999999999999999, 0.076923076923076913, 0.1111111111111111,
0.09999999999999999, 0.1111111111111111, 0.1111111111111111,
0.076923076923076913, 0.09999999999999999, 0.076923076923076913,
0.09999999999999999, 0.14285714285714285, 0.076923076923076913,
0.1111111111111111, 0.1111111111111111, 0.076923076923076913,
0.09999999999999999, 0.1111111111111111, 0.14285714285714285,
0.09999999999999999, 0.1111111111111111, 0.14285714285714285,
0.09999999999999999, 0.076923076923076913, 0.09999999999999999,
0.14285714285714285, 0.076923076923076913, 0.1111111111111111,
0.1111111111111111, 0.076923076923076913, 0.076923076923076913,
0.14285714285714285, 0.14285714285714285, 0.09999999999999999,
0.076923076923076913, 0.14285714285714285, 0.09999999999999999,
0.076923076923076913, 0.076923076923076913, 0.083333333333333384,
0.10000000000000005, 0.083333333333333384, 0.10000000000000001,
0.10000000000000005, 0.10000000000000001, 0.10000000000000001,
0.083333333333333384, 0.10000000000000005, 0.083333333333333384,
0.10000000000000005, 0.125, 0.083333333333333384, 0.10000000000000001,
0.10000000000000001, 0.083333333333333384, 0.10000000000000005,
0.10000000000000001, 0.125, 0.10000000000000005, 0.10000000000000001,
0.125, 0.10000000000000005, 0.125, 0.10000000000000005, 0.083333333333333384,
0.083333333333333384, 0.10000000000000001, 0.10000000000000001,
0.083333333333333384, 0.083333333333333384, 0.10000000000000005,
0.125, 0.10000000000000005, 0.125, 0.083333333333333384,
0.125, 0.125, 0.083333333333333384, 0.10000000000000001,
0.083333333333333329, 0.10000000000000002, 0.083333333333333329,
0.083333333333333329, 0.10000000000000002, 0.083333333333333329,
0.083333333333333329, 0.083333333333333329, 0.10000000000000002,
0.083333333333333329, 0.10000000000000002, 0.14285714285714285,
0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.10000000000000002, 0.083333333333333329,
0.14285714285714285, 0.10000000000000002, 0.14285714285714285,
0.083333333333333329, 0.14285714285714285, 0.10000000000000002,
0.10000000000000002, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.10000000000000002, 0.14285714285714285,
0.10000000000000002, 0.14285714285714285, 0.083333333333333329,
0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.14285714285714285, 0.083333333333333329, 0.083333333333333356,
0.090909090909090898, 0.083333333333333315, 0.083333333333333356,
0.083333333333333315, 0.090909090909090898, 0.083333333333333356,
0.083333333333333315, 0.090909090909090898, 0.083333333333333356,
0.090909090909090898, 0.14285714285714285, 0.083333333333333356,
0.083333333333333315, 0.083333333333333315, 0.083333333333333356,
0.090909090909090898, 0.083333333333333315, 0.14285714285714285,
0.090909090909090898, 0.14285714285714285, 0.083333333333333315,
0.14285714285714285, 0.090909090909090898, 0.090909090909090898,
0.083333333333333356, 0.083333333333333356, 0.083333333333333315,
0.083333333333333315, 0.083333333333333356, 0.083333333333333356,
0.090909090909090898, 0.14285714285714285, 0.090909090909090898,
0.14285714285714285, 0.083333333333333356, 0.083333333333333315,
0.083333333333333315, 0.083333333333333315, 0.083333333333333356,
0.090909090909090898, 0.14285714285714285, 0.083333333333333315,
0.090909090909090884, 0.083333333333333315, 0.083333333333333315,
0.083333333333333315, 0.090909090909090884, 0.083333333333333315,
0.083333333333333315, 0.090909090909090884, 0.083333333333333315,
0.090909090909090884, 0.125, 0.083333333333333315, 0.083333333333333315,
0.083333333333333315, 0.083333333333333315, 0.090909090909090884,
0.083333333333333315, 0.125, 0.090909090909090884, 0.125,
0.083333333333333315, 0.125, 0.090909090909090884, 0.090909090909090884,
0.083333333333333315, 0.083333333333333315, 0.083333333333333315,
0.083333333333333315, 0.083333333333333315, 0.083333333333333315,
0.090909090909090884, 0.125, 0.090909090909090884, 0.125,
0.083333333333333315, 0.083333333333333315, 0.083333333333333315,
0.083333333333333315, 0.090909090909090884, 0.083333333333333315,
0.125, 0.125, 0.083333333333333343, 0.083333333333333343,
0.083333333333333329, 0.083333333333333343, 0.083333333333333329,
0.083333333333333343, 0.083333333333333343, 0.083333333333333329,
0.083333333333333343, 0.083333333333333343, 0.083333333333333343,
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0.083333333333333343, 0.083333333333333343, 0.083333333333333329,
0.125, 0.083333333333333343, 0.083333333333333329, 0.125,
0.125, 0.083333333333333343, 0.083333333333333343, 0.083333333333333343,
0.083333333333333343, 0.083333333333333329, 0.083333333333333329,
0.083333333333333343, 0.083333333333333343, 0.083333333333333343,
0.125, 0.083333333333333343, 0.125, 0.083333333333333343,
0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.083333333333333343, 0.125, 0.083333333333333343, 0.125,
0.083333333333333343, 0.076923076923076927, 0.083333333333333329,
0.083333333333333329, 0.076923076923076927, 0.083333333333333329,
0.083333333333333329, 0.076923076923076927, 0.083333333333333329,
0.083333333333333329, 0.076923076923076927, 0.083333333333333329,
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0.076923076923076927, 0.083333333333333329, 0.083333333333333329,
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0.076923076923076927, 0.076923076923076927, 0.083333333333333329,
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0.083333333333333329, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.125, 0.076923076923076927, 0.125,
0.083333333333333329, 0.083333333333333329, 0.066666666666666693,
0.090909090909090912, 0.083333333333333329, 0.066666666666666693,
0.083333333333333329, 0.090909090909090912, 0.066666666666666693,
0.083333333333333329, 0.090909090909090912, 0.066666666666666693,
0.090909090909090912, 0.125, 0.066666666666666693, 0.083333333333333329,
0.083333333333333329, 0.066666666666666693, 0.090909090909090912,
0.083333333333333329, 0.125, 0.090909090909090912, 0.083333333333333329,
0.125, 0.125, 0.066666666666666693, 0.090909090909090912,
0.066666666666666693, 0.066666666666666693, 0.083333333333333329,
0.083333333333333329, 0.066666666666666693, 0.066666666666666693,
0.090909090909090912, 0.125, 0.066666666666666693, 0.125,
0.066666666666666693, 0.083333333333333329, 0.083333333333333329,
0.083333333333333329, 0.090909090909090912, 0.066666666666666693,
0.125, 0.125, 0.090909090909090912, 0.066666666666666693,
0.090909090909090912, 0.066666666666666666, 0.083333333333333329,
0.083333333333333329, 0.066666666666666666, 0.083333333333333343,
0.066666666666666666, 0.083333333333333343, 0.066666666666666666,
0.066666666666666666, 0.083333333333333329, 0.083333333333333329,
0.125, 0.066666666666666666, 0.083333333333333343, 0.083333333333333343,
0.066666666666666666, 0.083333333333333329, 0.083333333333333343,
0.083333333333333329, 0.125, 0.083333333333333343, 0.125,
0.066666666666666666, 0.125, 0.083333333333333329, 0.083333333333333343,
0.083333333333333343, 0.066666666666666666, 0.083333333333333343,
0.083333333333333329, 0.066666666666666666, 0.083333333333333329,
0.125, 0.066666666666666666, 0.125, 0.066666666666666666,
0.083333333333333343, 0.083333333333333343, 0.083333333333333329,
0.083333333333333343, 0.066666666666666666, 0.083333333333333329,
0.125, 0.083333333333333329, 0.125, 0.066666666666666666,
0.066666666666666666, 0.066666666666666666, 0.083333333333333329,
0.066666666666666666, 0.083333333333333329, 0.0769230769230769,
0.066666666666666666, 0.0769230769230769, 0.066666666666666666,
0.066666666666666666, 0.083333333333333329, 0.083333333333333329,
0.12499999999999999, 0.066666666666666666, 0.0769230769230769,
0.066666666666666666, 0.0769230769230769, 0.0769230769230769,
0.083333333333333329, 0.12499999999999999, 0.083333333333333329,
0.0769230769230769, 0.12499999999999999, 0.066666666666666666,
0.12499999999999999, 0.083333333333333329, 0.0769230769230769,
0.0769230769230769, 0.083333333333333329, 0.0769230769230769,
0.083333333333333329, 0.083333333333333329, 0.066666666666666666,
0.12499999999999999, 0.066666666666666666, 0.066666666666666666,
0.12499999999999999, 0.0769230769230769, 0.0769230769230769,
0.0769230769230769, 0.066666666666666666, 0.066666666666666666,
0.083333333333333329, 0.12499999999999999, 0.12499999999999999,
0.066666666666666666, 0.083333333333333329, 0.066666666666666666,
0.0769230769230769, 0.06666666666666668, 0.083333333333333287,
0.06666666666666668, 0.083333333333333287, 0.076923076923076941,
0.06666666666666668, 0.076923076923076941, 0.06666666666666668,
0.06666666666666668, 0.083333333333333287, 0.083333333333333287,
0.11111111111111112, 0.06666666666666668, 0.076923076923076941,
0.06666666666666668, 0.076923076923076941, 0.083333333333333287,
0.076923076923076941, 0.11111111111111112, 0.083333333333333287,
0.076923076923076941, 0.11111111111111112, 0.06666666666666668,
0.11111111111111112, 0.083333333333333287, 0.076923076923076941,
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0.083333333333333287, 0.083333333333333287, 0.06666666666666668,
0.11111111111111112, 0.06666666666666668, 0.06666666666666668,
0.11111111111111112, 0.076923076923076941, 0.076923076923076941,
0.076923076923076941, 0.06666666666666668, 0.06666666666666668,
0.083333333333333287, 0.11111111111111112, 0.11111111111111112,
0.06666666666666668, 0.083333333333333287, 0.06666666666666668,
0.076923076923076941, 0.11111111111111112, 0.066666666666666666,
0.083333333333333315, 0.066666666666666666, 0.083333333333333315,
0.076923076923076913, 0.066666666666666666, 0.076923076923076913,
0.066666666666666666, 0.066666666666666666, 0.083333333333333315,
0.083333333333333315, 0.10000000000000001, 0.066666666666666666,
0.076923076923076913, 0.066666666666666666, 0.076923076923076913,
0.083333333333333315, 0.076923076923076913, 0.10000000000000001,
0.083333333333333315, 0.076923076923076913, 0.10000000000000001,
0.066666666666666666, 0.10000000000000001, 0.083333333333333315,
0.076923076923076913, 0.076923076923076913, 0.083333333333333315,
0.076923076923076913, 0.083333333333333315, 0.083333333333333315,
0.066666666666666666, 0.10000000000000001, 0.066666666666666666,
0.066666666666666666, 0.10000000000000001, 0.076923076923076913,
0.076923076923076913, 0.076923076923076913, 0.10000000000000001,
0.066666666666666666, 0.066666666666666666, 0.10000000000000001,
0.083333333333333315, 0.066666666666666666, 0.083333333333333315,
0.066666666666666666, 0.10000000000000001, 0.076923076923076913,
0.10000000000000001, 0.0625, 0.083333333333333315, 0.0625,
0.083333333333333315, 0.076923076923076927, 0.0625, 0.076923076923076927,
0.0625, 0.0625, 0.10000000000000001, 0.083333333333333315,
0.10000000000000001, 0.0625, 0.076923076923076927, 0.0625,
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0.10000000000000001, 0.083333333333333315, 0.076923076923076927,
0.10000000000000001, 0.0625, 0.10000000000000001, 0.083333333333333315,
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0.0625, 0.10000000000000001, 0.0625, 0.0625, 0.10000000000000001,
0.076923076923076927, 0.076923076923076927, 0.076923076923076927,
0.10000000000000001, 0.10000000000000001, 0.0625, 0.0625,
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