Generates an Spatial Un-replicated Optimized Arrangement Design

Randomly generates a spatial un-replicated optimized arrangement design, where the distance between checks is maximized in such a way that each row and column have control plots. Note that design generation needs the dimension of the field (number of rows and columns).

Usage

optimized_arrangement(
  nrows = NULL,
  ncols = NULL,
  lines = NULL,
  amountChecks = NULL,
  checks = NULL,
  planter = "serpentine",
  l = 1,
  plotNumber = 101,
  seed = NULL,
  exptName = NULL,
  locationNames = NULL,
  optim = TRUE,
  data = NULL
)

Arguments

nrows: Number of rows in the field.
ncols: Number of columns in the field.
lines: Number of genotypes, experimental lines or treatments.
amountChecks: Integer with the amount total of checks or a numeric vector with the replicates of each check label.
checks: Number of genotypes as checks.
planter: Option for serpentine or cartesian arrangement. By default planter = 'serpentine'.
l: Number of locations. By default l = 1.
plotNumber: Numeric vector with the starting plot number for each location. By default plotNumber = 101.
seed: (optional) Real number that specifies the starting seed to obtain reproducible designs.
exptName: (optional) Name of the experiment.
locationNames: (optional) Name for each location.
optim: By default optim = TRUE.
data: (optional) Data frame with 3 columns: ENTRY | NAME | REPS.

Value

A list with five elements.

infoDesign is a list with information on the design parameters.
layoutRandom is a matrix with the randomization layout.
plotNumber is a matrix with the layout plot number.
dataEntry is a data frame with the data input.
genEntries is a list with the entries for replicated and no replicated part.
fieldBook is a data frame with field book design. This includes the index (Row, Column).

References

Clarke, G. P. Y., & Stefanova, K. T. (2011). Optimal design for early-generation plant breeding trials with unreplicated or partially replicated test lines. Australian & New Zealand Journal of Statistics, 53(4), 461–480.

Author

Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]

Examples

# Example 1: Generates a spatial unreplicated optimized arrangement design in one location
# with 108 genotypes + 12 check plots (4 checks) for a field with dimension 10 rows x 12 cols.
OptimAd1 <- optimized_arrangement(
  nrows = 10, 
  ncols = 12, 
  lines = 108, 
  amountChecks = 12, 
  checks = 1:4,
  planter = "cartesian", 
  plotNumber = 101,
  seed = 14,
  exptName = "20RW1",
  locationNames = "CASSELTON"
 )
#> [1] 120
OptimAd1$infoDesign
#> $rows
#> [1] 10
#> 
#> $columns
#> [1] 12
#> 
#> $treatments
#> [1] 108
#> 
#> $checks
#> [1] 4
#> 
#> $entry_checks
#> [1] 1 2 3 4
#> 
#> $rep_checks
#> [1] 3 3 3 3
#> 
#> $locations
#> [1] 1
#> 
#> $planter
#> [1] "cartesian"
#> 
#> $seed
#> [1] 14
#> 
#> $id_design
#> [1] 16
#> 
OptimAd1$layoutRandom
#> [[1]]
#>       Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12
#> Row10   69  106   64    4   28  109    6   41    8    25    34   104
#> Row9    80   47   55  112   22    2   89   68   15    95    98    61
#> Row8   110    4   48   26   20   66   35   46   81     1    18    76
#> Row7     5  111   65   36   92   54   38   87  100    84     3    75
#> Row6    14   59   40   19    1   63   91    7   10   105    29    30
#> Row5    17   67    3   58   11   94   13   57   99    12   102    16
#> Row4    73   56   50  108   74  101   27   43    2    79    83    90
#> Row3     4   71   37   51  107   21   93    3   70    86    42    39
#> Row2    85   44   24   82   96   53  103   23   77    33     9     1
#> Row1    31   32   60   52   78   72    2   62   45    49    97    88
#> 
OptimAd1$plotNumber
#> [[1]]
#>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
#>  [1,]  209  210  211  212  213  214  215  216  217   218   219   220
#>  [2,]  197  198  199  200  201  202  203  204  205   206   207   208
#>  [3,]  185  186  187  188  189  190  191  192  193   194   195   196
#>  [4,]  173  174  175  176  177  178  179  180  181   182   183   184
#>  [5,]  161  162  163  164  165  166  167  168  169   170   171   172
#>  [6,]  149  150  151  152  153  154  155  156  157   158   159   160
#>  [7,]  137  138  139  140  141  142  143  144  145   146   147   148
#>  [8,]  125  126  127  128  129  130  131  132  133   134   135   136
#>  [9,]  113  114  115  116  117  118  119  120  121   122   123   124
#> [10,]  101  102  103  104  105  106  107  108  109   110   111   112
#> 
head(OptimAd1$fieldBook,12)
#>    ID  EXPT  LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
#> 1   1 20RW1 CASSELTON 2022  101   1      1      0    31       G31
#> 2   2 20RW1 CASSELTON 2022  102   1      2      0    32       G32
#> 3   3 20RW1 CASSELTON 2022  103   1      3      0    60       G60
#> 4   4 20RW1 CASSELTON 2022  104   1      4      0    52       G52
#> 5   5 20RW1 CASSELTON 2022  105   1      5      0    78       G78
#> 6   6 20RW1 CASSELTON 2022  106   1      6      0    72       G72
#> 7   7 20RW1 CASSELTON 2022  107   1      7      2     2       CH2
#> 8   8 20RW1 CASSELTON 2022  108   1      8      0    62       G62
#> 9   9 20RW1 CASSELTON 2022  109   1      9      0    45       G45
#> 10 10 20RW1 CASSELTON 2022  110   1     10      0    49       G49
#> 11 11 20RW1 CASSELTON 2022  111   1     11      0    97       G97
#> 12 12 20RW1 CASSELTON 2022  112   1     12      0    88       G88
                  
# Example 2: Generates a spatial unreplicated optimized arrangement design in one location
# with 200 genotypes + 20 check plots (4 checks) for a field with dimension 10 rows x 22 cols.
# As example, we set up the data option with the entries list.
checks <- 4
list_checks <- paste("CH", 1:checks, sep = "")
treatments <- paste("G", 5:204, sep = "")
REPS <- c(5, 5, 5, 5, rep(1, 200))
treatment_list <- data.frame(list(ENTRY = 1:204, NAME = c(list_checks, treatments), REPS = REPS))
head(treatment_list, 12) 
#>    ENTRY NAME REPS
#> 1      1  CH1    5
#> 2      2  CH2    5
#> 3      3  CH3    5
#> 4      4  CH4    5
#> 5      5   G5    1
#> 6      6   G6    1
#> 7      7   G7    1
#> 8      8   G8    1
#> 9      9   G9    1
#> 10    10  G10    1
#> 11    11  G11    1
#> 12    12  G12    1
tail(treatment_list, 12)
#>     ENTRY NAME REPS
#> 193   193 G193    1
#> 194   194 G194    1
#> 195   195 G195    1
#> 196   196 G196    1
#> 197   197 G197    1
#> 198   198 G198    1
#> 199   199 G199    1
#> 200   200 G200    1
#> 201   201 G201    1
#> 202   202 G202    1
#> 203   203 G203    1
#> 204   204 G204    1
OptimAd2 <- optimized_arrangement(
  nrows = 10, 
  ncols = 22, 
  planter = "serpentine", 
  plotNumber = 101,
  seed = 120,
  exptName = "20YWA2",
  locationNames = "MINOT",
  data = treatment_list
)
#> [1] 220
OptimAd2$infoDesign
#> $rows
#> [1] 10
#> 
#> $columns
#> [1] 22
#> 
#> $treatments
#> [1] 200
#> 
#> $checks
#> [1] 4
#> 
#> $entry_checks
#> [1] 1 2 3 4
#> 
#> $rep_checks
#> [1] 5 5 5 5
#> 
#> $locations
#> [1] 1
#> 
#> $planter
#> [1] "serpentine"
#> 
#> $seed
#> [1] 120
#> 
#> $id_design
#> [1] 16
#> 
OptimAd2$layoutRandom
#> [[1]]
#>       Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row10   75  141  179  140   34  197   70   31    3    91   186     1   132
#> Row9   162  154   48   15    1  125   62  109   13    85   121   110    46
#> Row8   142   33  190   36  135  182   63  111   79   108     2    83     9
#> Row7    69  171   88   44   20  148  167  103  122    94   101   164     3
#> Row6    57   22  126   50  106  202    3  159  176    82     8   189    73
#> Row5   193   90    4  130  187  124  178   98   21   170   200   181    89
#> Row4     4  174  147  138   42  151  139  149  185   117   153    71    64
#> Row3   136    5   60   29  112   12   68    2   38   161   177    39   119
#> Row2    99   43  144    3  165   18  168   77  114    84   203   143    59
#> Row1   123    2   27  133  118   54   67   47   96     2    24   120   102
#>       Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22
#> Row10    74    26    58    11     6   158   180   194   113
#> Row9    196   166    37   172    95     3    92    80   145
#> Row8    155   107     4    76   129    10   169   104   173
#> Row7     49    51    53   195     7    65    81     4   199
#> Row6    127    40    45    61     4    17    25   184    32
#> Row5    201   175   128     1   198   163   204   152   160
#> Row4    134   146   192   105   157    56     1    35    19
#> Row3    183    78   100    41    28    97   137   188     1
#> Row2    116     2    72    66   156    93    16   191    86
#> Row1     30   131    23    52    55   150    87    14   115
#> 
OptimAd2$plotNumber
#> [[1]]
#>       [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#>  [1,]  320  319  318  317  316  315  314  313  312   311   310   309   308
#>  [2,]  277  278  279  280  281  282  283  284  285   286   287   288   289
#>  [3,]  276  275  274  273  272  271  270  269  268   267   266   265   264
#>  [4,]  233  234  235  236  237  238  239  240  241   242   243   244   245
#>  [5,]  232  231  230  229  228  227  226  225  224   223   222   221   220
#>  [6,]  189  190  191  192  193  194  195  196  197   198   199   200   201
#>  [7,]  188  187  186  185  184  183  182  181  180   179   178   177   176
#>  [8,]  145  146  147  148  149  150  151  152  153   154   155   156   157
#>  [9,]  144  143  142  141  140  139  138  137  136   135   134   133   132
#> [10,]  101  102  103  104  105  106  107  108  109   110   111   112   113
#>       [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22]
#>  [1,]   307   306   305   304   303   302   301   300   299
#>  [2,]   290   291   292   293   294   295   296   297   298
#>  [3,]   263   262   261   260   259   258   257   256   255
#>  [4,]   246   247   248   249   250   251   252   253   254
#>  [5,]   219   218   217   216   215   214   213   212   211
#>  [6,]   202   203   204   205   206   207   208   209   210
#>  [7,]   175   174   173   172   171   170   169   168   167
#>  [8,]   158   159   160   161   162   163   164   165   166
#>  [9,]   131   130   129   128   127   126   125   124   123
#> [10,]   114   115   116   117   118   119   120   121   122
#> 
head(OptimAd2$fieldBook,12)
#>    ID   EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
#> 1   1 20YWA2    MINOT 2022  101   1      1      0   123      G123
#> 2   2 20YWA2    MINOT 2022  102   1      2      2     2       CH2
#> 3   3 20YWA2    MINOT 2022  103   1      3      0    27       G27
#> 4   4 20YWA2    MINOT 2022  104   1      4      0   133      G133
#> 5   5 20YWA2    MINOT 2022  105   1      5      0   118      G118
#> 6   6 20YWA2    MINOT 2022  106   1      6      0    54       G54
#> 7   7 20YWA2    MINOT 2022  107   1      7      0    67       G67
#> 8   8 20YWA2    MINOT 2022  108   1      8      0    47       G47
#> 9   9 20YWA2    MINOT 2022  109   1      9      0    96       G96
#> 10 10 20YWA2    MINOT 2022  110   1     10      2     2       CH2
#> 11 11 20YWA2    MINOT 2022  111   1     11      0    24       G24
#> 12 12 20YWA2    MINOT 2022  112   1     12      0   120      G120