Generates data set based on generalized logistic regression DIF and DDF models.
Source:R/genNLR.R
genNLR.RdGenerates dichotomous, nominal, and ordinal data based on generalized logistic regression models for DIF and DDF detection.
Arguments
- N
numeric: number of rows representing respondents. (default is 1000).
- ratio
numeric: ratio of respondents number in reference and focal group.
- itemtype
character: type of items to be generated. Options are
"dich"(default) for dichotomous item based on non-linear regression model for DIF detection (seedifNLRfor details),"nominal"for nominal items based on multinomial model for DDF detection (seeddfMLRfor detail), and"ordinal"for ordinal data based on adjacent category logit model (for details seedifORD).- a
numeric: matrix representing discriminations with m rows (where m is number of items). Needs to be provided. See Details.
- b
numeric: numeric: matrix representing difficulties with m rows (where m is number of items). Needs to be provided. See Details.
- c
numeric: matrix representing guessings (lower asymptotes) with m rows (where m is number of items). Default is
NULL. See Details.- d
numeric: matrix representing inattentions (upper asymptotes) with m rows (where m is number of items). Default is
NULL. See Details.- mu
numeric: a mean vector of the underlying distribution. The first value corresponds to reference group, the second to focal group. Default is 0 value for both groups.
- sigma
numeric: a standard deviation vector of the underlying distribution. The first value corresponds to reference group, the second to focal group. Default is 1 value for both groups.
Value
A data.frame containing N rows representing respondents and m + 1 columns representing
m items. The last column is group membership variable with coding "0" for reference group and
"1" for focal group.
Details
The a, b, c and d are numeric matrices with m rows (where m is number of items)
representing parameters of regression models for DIF and DDF detection.
For option itemtype = "dich", matrices should have two columns. The first column represents
parameters of the reference group and the second of the focal group. In case that only one column is
provided, parameters are set to be the same for both groups.
For options itemtype = "nominal" and itemtype = "ordinal", matrices c and d
are ignored. Matrices a and b contain parameters for distractors. For example, when
item with 4 different choices is supposed to be generated, user provide matrices with 6 columns.
First 3 columns correspond to distractors parameters for reference group and last three columns
for focal group. The number of choices can differ for items. Matrices a and b
need to consist of as many columns as is the maximum number of distractors. Items with less
choices can contain NAs.
References
Drabinova, A. & Martinkova, P. (2017). Detection of differential item functioning with nonlinear regression: A non-IRT approach accounting for guessing. Journal of Educational Measurement, 54(4), 498–517, doi:10.1111/jedm.12158 .
Author
Adela Hladka (nee Drabinova)
Institute of Computer Science of the Czech Academy of Sciences
Faculty of Mathematics and Physics, Charles University
hladka@cs.cas.cz
Patricia Martinkova
Institute of Computer Science of the Czech Academy of Sciences
martinkova@cs.cas.cz
Examples
# seed
set.seed(123)
# generating parameters for dichotomous data with DIF, 5 items
a <- matrix(runif(10, 0.8, 2), ncol = 2)
b <- matrix(runif(10, -2, 2), ncol = 2)
c <- matrix(runif(10, 0, 0.25), ncol = 2)
d <- matrix(runif(10, 0.8, 1), ncol = 2)
# generating dichotomous data set with 300 observations (150 each group)
genNLR(N = 300, a = a, b = b, c = c, d = d)
#> Item1 Item2 Item3 Item4 Item5 group
#> 1 0 1 0 0 1 0
#> 2 1 1 0 1 1 0
#> 3 1 0 0 0 1 0
#> 4 0 0 0 0 0 0
#> 5 0 0 1 1 1 0
#> 6 0 0 0 0 0 0
#> 7 1 1 1 1 1 0
#> 8 0 1 0 1 1 0
#> 9 0 0 0 0 0 0
#> 10 1 1 0 0 1 0
#> 11 1 1 1 0 0 0
#> 12 1 1 0 0 0 0
#> 13 0 0 1 1 1 0
#> 14 0 1 1 1 1 0
#> 15 1 1 1 1 1 0
#> 16 1 0 0 1 1 0
#> 17 1 1 1 1 0 0
#> 18 0 1 0 0 1 0
#> 19 0 1 1 0 1 0
#> 20 0 1 0 1 0 0
#> 21 1 0 0 1 1 0
#> 22 1 1 0 0 1 0
#> 23 0 0 0 1 1 0
#> 24 0 1 1 1 0 0
#> 25 0 1 0 1 1 0
#> 26 0 0 0 0 1 0
#> 27 1 0 0 0 1 0
#> 28 0 0 1 1 0 0
#> 29 1 1 1 1 1 0
#> 30 0 1 1 1 1 0
#> 31 0 0 0 1 1 0
#> 32 0 1 1 1 0 0
#> 33 0 0 0 1 1 0
#> 34 1 1 1 1 1 0
#> 35 0 1 1 0 1 0
#> 36 0 1 1 1 0 0
#> 37 1 1 0 0 0 0
#> 38 0 1 0 1 1 0
#> 39 0 1 0 1 0 0
#> 40 0 1 0 1 1 0
#> 41 1 1 0 0 0 0
#> 42 0 1 1 0 1 0
#> 43 1 0 0 0 1 0
#> 44 1 0 0 0 1 0
#> 45 1 1 0 1 1 0
#> 46 0 0 0 0 1 0
#> 47 0 1 1 1 1 0
#> 48 0 1 0 1 1 0
#> 49 0 1 1 1 1 0
#> 50 1 1 1 1 1 0
#> 51 0 0 1 1 1 0
#> 52 0 0 0 1 1 0
#> 53 0 1 1 1 1 0
#> 54 0 0 1 0 0 0
#> 55 0 0 0 0 0 0
#> 56 1 1 1 1 1 0
#> 57 0 0 1 1 1 0
#> 58 0 0 0 0 0 0
#> 59 0 0 1 0 1 0
#> 60 0 1 0 0 1 0
#> 61 1 1 0 0 1 0
#> 62 0 1 0 1 1 0
#> 63 0 1 0 0 1 0
#> 64 1 0 0 0 0 0
#> 65 0 0 1 1 1 0
#> 66 0 1 1 1 1 0
#> 67 1 1 1 0 1 0
#> 68 0 1 0 1 1 0
#> 69 0 1 1 1 1 0
#> 70 0 1 1 1 1 0
#> 71 1 1 1 1 1 0
#> 72 0 1 1 0 1 0
#> 73 0 1 1 1 1 0
#> 74 0 0 0 1 1 0
#> 75 1 1 0 0 1 0
#> 76 0 1 0 0 1 0
#> 77 1 1 1 1 1 0
#> 78 1 1 1 1 1 0
#> 79 1 1 0 0 0 0
#> 80 0 0 0 0 1 0
#> 81 0 0 1 0 0 0
#> 82 1 0 0 1 1 0
#> 83 1 0 1 0 0 0
#> 84 1 1 0 1 1 0
#> 85 0 1 1 0 1 0
#> 86 1 1 0 1 1 0
#> 87 1 0 0 0 1 0
#> 88 0 0 0 0 0 0
#> 89 1 1 0 1 1 0
#> 90 0 1 0 0 0 0
#> 91 0 0 0 1 1 0
#> 92 1 1 1 1 1 0
#> 93 0 0 0 0 0 0
#> 94 1 1 0 1 1 0
#> 95 0 1 1 0 1 0
#> 96 0 1 0 0 1 0
#> 97 1 1 1 1 1 0
#> 98 1 0 1 0 0 0
#> 99 0 0 1 0 1 0
#> 100 0 0 0 1 0 0
#> 101 1 0 0 1 1 0
#> 102 0 1 0 1 1 0
#> 103 0 0 0 1 1 0
#> 104 0 1 0 1 1 0
#> 105 1 1 1 1 1 0
#> 106 0 0 1 1 0 0
#> 107 1 1 0 0 1 0
#> 108 0 1 1 0 1 0
#> 109 0 1 0 0 0 0
#> 110 1 0 0 0 1 0
#> 111 1 1 1 1 1 0
#> 112 0 0 0 0 1 0
#> 113 0 0 0 1 1 0
#> 114 0 1 0 0 1 0
#> 115 0 0 0 0 0 0
#> 116 0 1 1 1 1 0
#> 117 0 0 0 0 1 0
#> 118 1 1 0 0 1 0
#> 119 1 1 1 1 1 0
#> 120 0 0 0 1 1 0
#> 121 0 1 0 0 0 0
#> 122 0 1 0 0 1 0
#> 123 0 0 1 0 0 0
#> 124 0 0 0 0 1 0
#> 125 0 0 0 1 0 0
#> 126 0 1 0 0 1 0
#> 127 0 0 0 1 1 0
#> 128 0 1 0 1 1 0
#> 129 0 0 1 1 0 0
#> 130 0 0 0 0 1 0
#> 131 1 1 1 1 1 0
#> 132 1 1 1 1 0 0
#> 133 1 0 0 1 1 0
#> 134 1 1 0 1 1 0
#> 135 0 0 1 0 0 0
#> 136 0 0 1 1 1 0
#> 137 0 1 1 1 1 0
#> 138 0 1 1 1 1 0
#> 139 1 1 1 1 0 0
#> 140 1 1 0 0 1 0
#> 141 0 1 1 1 1 0
#> 142 1 0 0 0 1 0
#> 143 0 0 1 1 1 0
#> 144 1 1 1 1 1 0
#> 145 1 1 1 0 1 0
#> 146 0 1 0 1 1 0
#> 147 0 1 1 1 1 0
#> 148 0 0 1 0 1 0
#> 149 0 0 1 0 1 0
#> 150 1 1 0 0 1 0
#> 151 1 0 0 1 0 1
#> 152 0 1 1 1 0 1
#> 153 0 1 1 1 0 1
#> 154 1 1 1 0 1 1
#> 155 1 0 1 1 0 1
#> 156 0 0 0 1 0 1
#> 157 0 0 1 0 0 1
#> 158 0 1 1 1 0 1
#> 159 0 1 1 1 0 1
#> 160 0 1 1 1 0 1
#> 161 0 0 1 1 0 1
#> 162 0 1 1 1 0 1
#> 163 0 1 1 1 0 1
#> 164 0 0 1 1 0 1
#> 165 0 0 1 0 0 1
#> 166 1 1 1 0 0 1
#> 167 0 1 1 1 0 1
#> 168 0 0 0 1 0 1
#> 169 1 0 1 0 0 1
#> 170 0 1 0 0 0 1
#> 171 0 1 1 1 0 1
#> 172 0 0 0 1 0 1
#> 173 0 1 1 1 0 1
#> 174 1 1 1 1 0 1
#> 175 1 0 1 0 1 1
#> 176 1 1 1 0 0 1
#> 177 0 1 1 1 0 1
#> 178 0 1 1 0 0 1
#> 179 0 1 1 1 0 1
#> 180 0 1 1 0 0 1
#> 181 0 1 0 1 0 1
#> 182 0 1 1 1 0 1
#> 183 0 1 1 1 1 1
#> 184 1 1 0 1 0 1
#> 185 1 1 1 1 0 1
#> 186 0 1 1 0 0 1
#> 187 0 0 1 0 0 1
#> 188 0 1 0 1 0 1
#> 189 1 1 0 1 0 1
#> 190 1 1 1 1 0 1
#> 191 0 1 0 0 0 1
#> 192 1 1 1 1 0 1
#> 193 0 1 1 1 0 1
#> 194 0 1 0 0 0 1
#> 195 0 0 1 1 0 1
#> 196 0 1 1 1 1 1
#> 197 0 1 1 1 0 1
#> 198 0 1 0 1 1 1
#> 199 0 1 1 1 1 1
#> 200 0 0 1 1 0 1
#> 201 1 0 1 1 0 1
#> 202 1 1 1 0 0 1
#> 203 1 1 1 1 0 1
#> 204 0 0 1 1 0 1
#> 205 0 1 1 1 0 1
#> 206 0 1 1 1 1 1
#> 207 1 1 1 1 0 1
#> 208 0 1 1 1 0 1
#> 209 0 1 1 0 0 1
#> 210 0 1 1 0 0 1
#> 211 1 1 1 0 1 1
#> 212 1 1 1 0 0 1
#> 213 1 1 1 0 1 1
#> 214 0 0 1 1 0 1
#> 215 0 1 1 0 0 1
#> 216 0 0 0 0 0 1
#> 217 1 1 1 0 0 1
#> 218 0 1 1 1 0 1
#> 219 0 1 0 1 0 1
#> 220 1 1 1 1 0 1
#> 221 0 1 0 1 0 1
#> 222 0 1 1 1 0 1
#> 223 0 1 1 1 0 1
#> 224 0 1 1 0 0 1
#> 225 0 1 1 1 0 1
#> 226 1 1 0 0 0 1
#> 227 0 1 0 0 0 1
#> 228 0 0 1 0 0 1
#> 229 0 1 1 1 0 1
#> 230 0 0 1 0 1 1
#> 231 0 1 1 0 0 1
#> 232 0 1 1 0 0 1
#> 233 1 1 1 1 0 1
#> 234 0 0 1 1 0 1
#> 235 0 1 1 1 1 1
#> 236 0 1 1 1 0 1
#> 237 0 1 1 1 0 1
#> 238 1 1 0 1 0 1
#> 239 1 1 0 0 1 1
#> 240 0 1 0 0 0 1
#> 241 0 1 1 1 0 1
#> 242 1 1 1 1 0 1
#> 243 1 1 0 0 0 1
#> 244 0 1 1 1 0 1
#> 245 0 1 0 1 1 1
#> 246 0 1 1 1 1 1
#> 247 0 1 1 0 0 1
#> 248 0 1 1 0 1 1
#> 249 0 1 1 0 0 1
#> 250 0 1 0 0 0 1
#> 251 0 1 1 1 0 1
#> 252 1 1 1 1 0 1
#> 253 1 1 0 1 0 1
#> 254 0 1 0 1 0 1
#> 255 1 1 1 1 0 1
#> 256 1 1 0 1 0 1
#> 257 0 0 0 1 1 1
#> 258 0 1 1 0 0 1
#> 259 0 0 1 1 0 1
#> 260 1 1 1 1 0 1
#> 261 1 1 0 0 0 1
#> 262 0 1 0 0 0 1
#> 263 1 1 1 1 0 1
#> 264 0 1 1 0 1 1
#> 265 1 1 1 1 0 1
#> 266 0 0 1 1 0 1
#> 267 1 1 1 0 0 1
#> 268 0 1 1 1 0 1
#> 269 1 0 1 0 0 1
#> 270 0 1 1 1 0 1
#> 271 0 1 1 1 0 1
#> 272 0 0 0 1 0 1
#> 273 1 1 0 1 0 1
#> 274 0 1 1 1 0 1
#> 275 1 1 1 1 1 1
#> 276 1 0 1 1 0 1
#> 277 0 1 1 1 1 1
#> 278 0 1 1 0 0 1
#> 279 0 1 1 1 0 1
#> 280 0 0 1 1 1 1
#> 281 1 1 1 1 0 1
#> 282 0 1 1 0 0 1
#> 283 0 1 1 0 0 1
#> 284 1 0 1 1 0 1
#> 285 0 0 1 0 0 1
#> 286 1 1 1 0 0 1
#> 287 0 0 0 0 0 1
#> 288 0 1 1 0 0 1
#> 289 0 1 1 1 0 1
#> 290 0 1 1 1 0 1
#> 291 1 1 1 0 0 1
#> 292 0 1 1 1 0 1
#> 293 0 0 0 1 0 1
#> 294 0 1 1 0 0 1
#> 295 1 1 1 1 0 1
#> 296 0 1 1 1 0 1
#> 297 0 1 1 0 0 1
#> 298 1 1 1 0 0 1
#> 299 0 0 0 0 1 1
#> 300 0 1 1 1 0 1
# generating dichotomous data set with 300 observations (150 each group)
# and different mean and standard deviation for underlying distribution
genNLR(N = 300, a = a, b = b, c = c, d = d, mu = c(1, 0), sigma = c(1, 2))
#> Item1 Item2 Item3 Item4 Item5 group
#> 1 1 1 1 1 0 0
#> 2 0 1 0 1 1 0
#> 3 0 1 1 0 1 0
#> 4 0 1 0 1 1 0
#> 5 0 1 1 1 1 0
#> 6 1 1 0 0 1 0
#> 7 1 1 1 1 0 0
#> 8 0 1 0 1 0 0
#> 9 0 1 0 1 1 0
#> 10 0 0 0 0 1 0
#> 11 1 1 0 1 0 0
#> 12 1 1 1 1 0 0
#> 13 0 0 0 1 1 0
#> 14 0 0 0 1 1 0
#> 15 1 1 1 0 1 0
#> 16 0 0 0 0 1 0
#> 17 0 1 0 1 1 0
#> 18 0 1 1 1 1 0
#> 19 1 1 0 0 1 0
#> 20 0 1 0 0 1 0
#> 21 1 1 1 1 1 0
#> 22 0 0 0 1 0 0
#> 23 0 1 0 1 0 0
#> 24 1 1 0 0 1 0
#> 25 1 1 0 1 1 0
#> 26 1 1 1 1 1 0
#> 27 0 1 0 1 1 0
#> 28 1 1 1 1 1 0
#> 29 0 1 1 1 0 0
#> 30 0 1 0 0 1 0
#> 31 1 1 1 1 1 0
#> 32 1 1 1 1 0 0
#> 33 0 1 0 1 1 0
#> 34 0 1 1 1 0 0
#> 35 1 1 1 0 0 0
#> 36 1 0 0 1 1 0
#> 37 1 1 0 0 1 0
#> 38 0 1 0 1 0 0
#> 39 0 0 0 0 0 0
#> 40 0 1 1 1 1 0
#> 41 0 0 1 0 1 0
#> 42 0 1 1 1 1 0
#> 43 1 1 1 1 0 0
#> 44 1 1 0 0 1 0
#> 45 0 1 1 1 1 0
#> 46 1 1 1 1 0 0
#> 47 1 0 0 0 1 0
#> 48 0 1 1 0 1 0
#> 49 0 1 1 1 1 0
#> 50 1 0 0 0 1 0
#> 51 0 0 1 1 1 0
#> 52 0 1 0 0 1 0
#> 53 1 1 1 1 1 0
#> 54 0 1 1 1 0 0
#> 55 1 1 0 1 1 0
#> 56 1 1 1 1 1 0
#> 57 0 1 1 1 1 0
#> 58 1 1 0 0 1 0
#> 59 0 1 1 1 1 0
#> 60 1 0 0 0 1 0
#> 61 1 1 1 1 1 0
#> 62 0 0 1 1 0 0
#> 63 1 1 1 0 1 0
#> 64 0 0 0 1 0 0
#> 65 1 1 0 0 1 0
#> 66 0 1 0 0 1 0
#> 67 0 0 1 1 0 0
#> 68 0 1 1 1 1 0
#> 69 1 1 0 1 1 0
#> 70 1 1 1 1 0 0
#> 71 1 1 1 1 1 0
#> 72 0 1 0 1 0 0
#> 73 1 1 1 0 1 0
#> 74 1 1 0 1 1 0
#> 75 1 1 0 1 1 0
#> 76 0 0 1 1 0 0
#> 77 0 1 1 0 0 0
#> 78 0 1 1 0 1 0
#> 79 0 1 1 1 1 0
#> 80 1 1 1 1 1 0
#> 81 0 1 1 0 1 0
#> 82 0 1 0 1 1 0
#> 83 0 1 0 1 1 0
#> 84 1 0 1 0 1 0
#> 85 1 1 1 1 1 0
#> 86 0 1 1 1 1 0
#> 87 1 1 0 1 1 0
#> 88 0 1 1 1 1 0
#> 89 0 1 0 0 1 0
#> 90 0 0 1 1 1 0
#> 91 0 1 1 1 0 0
#> 92 0 1 1 1 1 0
#> 93 1 1 1 1 1 0
#> 94 1 1 0 1 1 0
#> 95 0 1 0 1 1 0
#> 96 0 0 0 1 0 0
#> 97 0 1 0 0 1 0
#> 98 1 1 0 1 1 0
#> 99 1 1 0 1 1 0
#> 100 0 1 1 1 1 0
#> 101 1 1 1 0 1 0
#> 102 1 1 1 1 1 0
#> 103 1 1 0 0 0 0
#> 104 1 1 1 1 1 0
#> 105 1 1 1 1 0 0
#> 106 0 1 1 1 1 0
#> 107 1 1 0 1 1 0
#> 108 0 0 1 1 1 0
#> 109 0 0 0 1 1 0
#> 110 0 1 0 1 0 0
#> 111 1 1 1 0 1 0
#> 112 0 0 0 1 1 0
#> 113 0 1 1 0 1 0
#> 114 0 1 1 1 1 0
#> 115 0 1 1 1 1 0
#> 116 1 1 1 1 1 0
#> 117 0 1 0 0 0 0
#> 118 1 1 0 1 1 0
#> 119 1 1 1 1 1 0
#> 120 0 0 0 0 1 0
#> 121 1 1 0 1 1 0
#> 122 1 1 1 1 1 0
#> 123 1 1 1 1 1 0
#> 124 0 1 1 0 1 0
#> 125 1 1 1 0 1 0
#> 126 1 1 0 0 1 0
#> 127 0 1 1 1 0 0
#> 128 0 1 1 0 1 0
#> 129 0 1 0 0 1 0
#> 130 1 1 1 1 1 0
#> 131 0 1 1 1 1 0
#> 132 0 1 0 0 1 0
#> 133 1 1 0 1 1 0
#> 134 0 0 1 1 1 0
#> 135 0 1 1 1 0 0
#> 136 0 1 1 1 0 0
#> 137 0 0 0 0 0 0
#> 138 1 1 1 1 0 0
#> 139 0 1 1 1 1 0
#> 140 1 1 0 1 1 0
#> 141 0 1 1 1 1 0
#> 142 1 0 0 1 1 0
#> 143 1 1 1 0 1 0
#> 144 0 1 0 0 0 0
#> 145 0 1 1 0 1 0
#> 146 0 1 1 1 0 0
#> 147 0 0 1 0 1 0
#> 148 0 0 1 0 1 0
#> 149 0 1 1 1 1 0
#> 150 1 1 0 0 1 0
#> 151 0 1 1 1 0 1
#> 152 1 0 0 0 0 1
#> 153 0 0 0 0 0 1
#> 154 0 1 0 1 0 1
#> 155 0 0 1 0 0 1
#> 156 1 1 1 0 1 1
#> 157 0 0 1 0 0 1
#> 158 0 1 1 1 0 1
#> 159 0 1 1 1 0 1
#> 160 0 1 1 1 0 1
#> 161 1 0 1 1 0 1
#> 162 1 1 1 1 1 1
#> 163 1 0 0 1 0 1
#> 164 0 0 0 0 1 1
#> 165 1 1 1 1 0 1
#> 166 0 0 0 0 0 1
#> 167 1 1 1 1 1 1
#> 168 0 0 1 0 0 1
#> 169 1 1 1 1 0 1
#> 170 0 1 1 1 1 1
#> 171 0 0 0 0 0 1
#> 172 1 1 1 1 0 1
#> 173 0 0 1 1 1 1
#> 174 0 1 1 0 0 1
#> 175 1 1 1 0 0 1
#> 176 1 1 1 1 0 1
#> 177 1 0 1 1 0 1
#> 178 1 1 1 1 0 1
#> 179 1 1 1 0 1 1
#> 180 1 1 1 1 1 1
#> 181 1 1 1 0 0 1
#> 182 1 1 1 1 1 1
#> 183 1 1 1 1 0 1
#> 184 0 1 1 1 0 1
#> 185 0 1 1 1 0 1
#> 186 0 1 0 0 0 1
#> 187 0 0 0 0 0 1
#> 188 1 1 1 1 1 1
#> 189 0 1 0 1 0 1
#> 190 1 1 0 1 0 1
#> 191 1 1 1 0 0 1
#> 192 1 1 0 1 0 1
#> 193 0 1 1 0 0 1
#> 194 0 1 1 0 0 1
#> 195 1 1 1 1 0 1
#> 196 1 1 1 0 1 1
#> 197 0 1 0 1 0 1
#> 198 1 1 1 1 1 1
#> 199 0 1 1 0 0 1
#> 200 1 1 1 1 0 1
#> 201 1 1 1 1 1 1
#> 202 1 1 0 1 0 1
#> 203 0 0 1 0 0 1
#> 204 0 0 1 1 0 1
#> 205 0 0 0 1 0 1
#> 206 1 1 0 1 0 1
#> 207 0 0 1 1 0 1
#> 208 0 1 1 1 0 1
#> 209 0 1 1 1 0 1
#> 210 0 1 1 0 0 1
#> 211 0 1 0 1 0 1
#> 212 0 1 1 1 0 1
#> 213 1 1 1 1 0 1
#> 214 0 0 0 0 0 1
#> 215 0 1 0 0 0 1
#> 216 1 1 0 1 0 1
#> 217 0 1 1 0 0 1
#> 218 0 1 0 0 0 1
#> 219 0 0 0 0 0 1
#> 220 0 1 1 1 1 1
#> 221 1 1 1 1 0 1
#> 222 1 1 1 1 1 1
#> 223 1 1 1 0 0 1
#> 224 1 1 1 1 1 1
#> 225 1 1 1 0 0 1
#> 226 0 0 0 1 0 1
#> 227 1 1 1 1 0 1
#> 228 0 0 0 1 0 1
#> 229 0 1 1 0 0 1
#> 230 1 1 1 0 1 1
#> 231 0 0 0 0 0 1
#> 232 0 1 1 1 0 1
#> 233 0 1 1 0 0 1
#> 234 0 1 0 1 0 1
#> 235 0 1 0 0 0 1
#> 236 0 1 1 1 1 1
#> 237 1 0 0 0 0 1
#> 238 0 1 1 0 0 1
#> 239 1 0 0 0 0 1
#> 240 0 1 1 0 0 1
#> 241 0 1 1 0 0 1
#> 242 0 1 1 0 0 1
#> 243 1 1 1 1 0 1
#> 244 0 1 0 1 0 1
#> 245 0 0 0 1 0 1
#> 246 1 1 1 1 0 1
#> 247 1 1 1 0 0 1
#> 248 0 0 0 0 0 1
#> 249 1 1 1 1 1 1
#> 250 1 1 1 1 0 1
#> 251 0 0 1 1 0 1
#> 252 0 0 0 0 0 1
#> 253 0 1 1 1 1 1
#> 254 1 1 1 1 1 1
#> 255 1 1 1 1 0 1
#> 256 1 1 0 1 0 1
#> 257 0 1 1 1 0 1
#> 258 0 0 0 0 0 1
#> 259 0 1 1 1 0 1
#> 260 0 1 0 0 0 1
#> 261 0 0 1 0 0 1
#> 262 1 1 1 1 0 1
#> 263 0 1 1 1 0 1
#> 264 0 1 1 1 0 1
#> 265 0 0 1 1 0 1
#> 266 0 0 0 0 0 1
#> 267 0 1 0 1 0 1
#> 268 0 0 0 0 0 1
#> 269 1 1 0 0 0 1
#> 270 1 1 1 1 0 1
#> 271 1 1 1 1 0 1
#> 272 1 1 1 1 0 1
#> 273 0 1 1 0 0 1
#> 274 0 1 1 0 0 1
#> 275 0 0 0 0 0 1
#> 276 0 0 1 0 0 1
#> 277 1 1 0 0 0 1
#> 278 0 1 1 1 0 1
#> 279 1 0 1 1 0 1
#> 280 0 1 1 0 0 1
#> 281 0 0 1 1 0 1
#> 282 0 0 0 0 0 1
#> 283 0 0 1 1 0 1
#> 284 1 1 1 1 0 1
#> 285 0 1 1 1 0 1
#> 286 0 1 0 1 1 1
#> 287 1 1 1 0 0 1
#> 288 1 1 0 0 0 1
#> 289 0 0 1 0 0 1
#> 290 1 1 1 0 0 1
#> 291 1 1 1 1 1 1
#> 292 0 1 1 0 0 1
#> 293 1 1 1 1 1 1
#> 294 1 1 1 1 1 1
#> 295 0 1 1 0 0 1
#> 296 0 1 1 0 0 1
#> 297 1 1 1 1 1 1
#> 298 1 1 1 1 0 1
#> 299 0 0 1 1 0 1
#> 300 1 1 1 1 0 1
# generating dichotomous data set with 300 observations (250 reference group, 50 focal)
genNLR(N = 300, ratio = 5, a = a, b = b, c = c, d = d)
#> Item1 Item2 Item3 Item4 Item5 group
#> 1 0 0 1 1 1 0
#> 2 0 0 0 0 0 0
#> 3 0 1 0 1 1 0
#> 4 0 1 0 1 1 0
#> 5 0 1 1 1 0 0
#> 6 1 1 0 1 1 0
#> 7 0 1 0 0 1 0
#> 8 0 0 0 0 1 0
#> 9 1 1 0 1 1 0
#> 10 0 1 1 1 1 0
#> 11 0 0 1 1 1 0
#> 12 1 0 0 1 1 0
#> 13 0 0 0 1 1 0
#> 14 1 1 0 1 1 0
#> 15 0 1 0 0 0 0
#> 16 0 1 0 1 1 0
#> 17 0 0 0 1 1 0
#> 18 0 0 0 0 1 0
#> 19 1 1 0 1 1 0
#> 20 0 0 0 0 1 0
#> 21 0 0 0 1 1 0
#> 22 1 0 0 1 1 0
#> 23 1 0 1 0 0 0
#> 24 0 0 0 1 0 0
#> 25 0 0 1 0 1 0
#> 26 1 0 0 0 1 0
#> 27 0 1 0 1 1 0
#> 28 0 1 0 1 1 0
#> 29 0 0 1 0 0 0
#> 30 0 1 1 1 1 0
#> 31 1 1 1 1 1 0
#> 32 0 1 1 1 1 0
#> 33 1 1 1 1 1 0
#> 34 1 1 1 1 1 0
#> 35 0 1 1 1 1 0
#> 36 0 1 1 0 1 0
#> 37 0 0 0 1 1 0
#> 38 0 0 0 1 1 0
#> 39 0 0 0 0 1 0
#> 40 0 1 0 1 1 0
#> 41 0 1 0 0 1 0
#> 42 0 1 0 1 0 0
#> 43 1 0 0 0 0 0
#> 44 0 1 1 1 0 0
#> 45 0 0 0 1 1 0
#> 46 0 0 0 1 1 0
#> 47 0 0 1 1 0 0
#> 48 0 0 0 0 1 0
#> 49 1 0 0 1 1 0
#> 50 1 0 1 0 1 0
#> 51 1 0 0 1 1 0
#> 52 0 0 1 0 0 0
#> 53 0 0 0 1 1 0
#> 54 0 0 0 0 0 0
#> 55 0 0 0 0 1 0
#> 56 0 1 0 0 1 0
#> 57 0 1 1 1 1 0
#> 58 0 0 1 1 1 0
#> 59 0 1 1 1 1 0
#> 60 0 0 1 0 1 0
#> 61 1 0 1 1 1 0
#> 62 1 0 0 1 1 0
#> 63 1 1 0 0 0 0
#> 64 1 1 1 1 1 0
#> 65 0 1 1 0 1 0
#> 66 0 0 0 0 1 0
#> 67 0 1 1 0 1 0
#> 68 1 1 1 1 1 0
#> 69 0 0 0 0 1 0
#> 70 0 1 0 0 1 0
#> 71 0 0 0 1 1 0
#> 72 1 0 0 0 1 0
#> 73 1 0 0 1 1 0
#> 74 0 1 1 1 1 0
#> 75 0 1 1 0 0 0
#> 76 0 1 1 1 1 0
#> 77 0 0 1 0 1 0
#> 78 0 1 1 0 1 0
#> 79 0 1 0 1 1 0
#> 80 0 0 1 1 1 0
#> 81 1 1 1 1 1 0
#> 82 0 0 0 0 1 0
#> 83 0 1 0 1 1 0
#> 84 0 1 0 0 1 0
#> 85 0 1 1 1 1 0
#> 86 1 1 1 1 0 0
#> 87 0 1 1 1 1 0
#> 88 0 1 0 0 1 0
#> 89 0 0 0 0 0 0
#> 90 1 1 0 1 1 0
#> 91 0 0 1 1 1 0
#> 92 0 1 0 1 1 0
#> 93 1 1 0 1 0 0
#> 94 0 1 0 1 1 0
#> 95 1 1 0 1 0 0
#> 96 0 1 1 0 0 0
#> 97 1 1 0 1 1 0
#> 98 0 0 0 0 0 0
#> 99 0 1 1 1 1 0
#> 100 1 1 1 1 1 0
#> 101 0 0 0 1 1 0
#> 102 0 1 1 0 1 0
#> 103 0 0 0 0 0 0
#> 104 0 0 0 0 1 0
#> 105 1 1 0 0 1 0
#> 106 0 1 0 0 1 0
#> 107 1 1 0 1 1 0
#> 108 0 0 0 1 1 0
#> 109 0 1 0 0 1 0
#> 110 1 0 0 1 0 0
#> 111 0 0 1 0 0 0
#> 112 0 0 0 0 1 0
#> 113 0 1 0 1 1 0
#> 114 1 1 1 1 0 0
#> 115 0 1 0 1 1 0
#> 116 1 1 1 1 0 0
#> 117 0 1 0 0 1 0
#> 118 0 1 0 1 1 0
#> 119 1 1 0 1 1 0
#> 120 0 0 0 1 1 0
#> 121 1 0 0 1 0 0
#> 122 1 1 1 1 0 0
#> 123 0 1 1 1 1 0
#> 124 0 1 0 0 0 0
#> 125 0 0 1 0 1 0
#> 126 0 0 0 0 1 0
#> 127 0 1 0 1 0 0
#> 128 0 1 0 1 1 0
#> 129 0 0 1 1 1 0
#> 130 0 0 0 0 0 0
#> 131 1 1 1 0 0 0
#> 132 0 1 1 0 1 0
#> 133 1 1 0 0 1 0
#> 134 1 0 0 1 1 0
#> 135 0 1 1 1 1 0
#> 136 1 1 0 1 1 0
#> 137 0 0 1 0 1 0
#> 138 1 1 1 1 1 0
#> 139 0 0 0 0 1 0
#> 140 0 1 1 1 0 0
#> 141 1 1 1 0 1 0
#> 142 0 0 0 0 1 0
#> 143 0 0 0 0 1 0
#> 144 0 1 0 0 1 0
#> 145 0 1 0 1 1 0
#> 146 0 1 1 0 0 0
#> 147 1 1 1 0 1 0
#> 148 0 0 0 0 1 0
#> 149 0 0 0 0 0 0
#> 150 0 1 0 0 0 0
#> 151 1 1 0 1 1 0
#> 152 0 0 0 1 1 0
#> 153 1 1 1 1 1 0
#> 154 0 0 0 0 0 0
#> 155 0 1 1 1 1 0
#> 156 0 1 0 0 1 0
#> 157 1 0 0 1 0 0
#> 158 0 1 0 1 1 0
#> 159 1 1 0 1 0 0
#> 160 0 0 0 0 0 0
#> 161 0 1 0 0 0 0
#> 162 1 0 0 1 1 0
#> 163 0 0 0 1 0 0
#> 164 0 0 0 0 1 0
#> 165 0 0 0 1 0 0
#> 166 0 1 1 1 1 0
#> 167 0 0 0 0 1 0
#> 168 0 1 1 1 1 0
#> 169 1 1 0 1 1 0
#> 170 0 1 0 1 0 0
#> 171 0 0 0 1 1 0
#> 172 1 1 1 1 1 0
#> 173 1 1 1 1 1 0
#> 174 1 0 0 0 0 0
#> 175 1 1 1 1 1 0
#> 176 1 1 1 1 0 0
#> 177 0 1 1 0 1 0
#> 178 0 0 0 0 1 0
#> 179 1 0 0 1 1 0
#> 180 0 1 1 1 0 0
#> 181 1 0 0 0 1 0
#> 182 1 0 0 1 1 0
#> 183 1 1 0 0 1 0
#> 184 0 1 0 1 0 0
#> 185 0 1 0 0 1 0
#> 186 0 1 0 1 1 0
#> 187 1 1 1 0 1 0
#> 188 0 1 1 1 0 0
#> 189 0 0 1 0 1 0
#> 190 1 0 0 0 0 0
#> 191 0 1 0 1 1 0
#> 192 1 1 0 1 1 0
#> 193 1 1 1 1 1 0
#> 194 0 0 0 0 1 0
#> 195 0 1 0 1 1 0
#> 196 1 1 0 0 0 0
#> 197 1 1 1 1 1 0
#> 198 0 1 0 1 1 0
#> 199 1 1 0 1 1 0
#> 200 1 0 0 0 1 0
#> 201 1 0 0 1 1 0
#> 202 0 0 0 0 0 0
#> 203 1 1 0 1 1 0
#> 204 1 1 0 1 1 0
#> 205 0 1 0 0 1 0
#> 206 1 0 0 0 1 0
#> 207 0 1 1 1 1 0
#> 208 0 1 0 0 1 0
#> 209 1 1 0 1 1 0
#> 210 1 1 1 0 1 0
#> 211 0 1 0 1 1 0
#> 212 0 1 1 1 0 0
#> 213 1 1 0 0 1 0
#> 214 1 0 0 0 1 0
#> 215 0 0 0 0 1 0
#> 216 0 0 0 1 1 0
#> 217 0 1 1 0 1 0
#> 218 1 1 0 0 1 0
#> 219 0 0 1 1 1 0
#> 220 0 0 0 1 1 0
#> 221 0 0 0 0 1 0
#> 222 1 1 0 0 1 0
#> 223 0 0 0 1 0 0
#> 224 0 1 1 1 1 0
#> 225 0 1 0 0 1 0
#> 226 1 0 0 1 1 0
#> 227 0 0 0 0 1 0
#> 228 0 0 0 1 1 0
#> 229 0 1 0 0 1 0
#> 230 0 1 0 1 1 0
#> 231 0 0 0 0 0 0
#> 232 0 1 1 0 1 0
#> 233 0 1 1 1 1 0
#> 234 1 0 0 0 0 0
#> 235 0 1 1 1 1 0
#> 236 0 1 0 1 1 0
#> 237 1 1 0 1 1 0
#> 238 1 0 0 1 1 0
#> 239 1 1 1 0 1 0
#> 240 0 0 0 0 1 0
#> 241 0 0 0 0 1 0
#> 242 0 1 1 1 1 0
#> 243 0 0 1 1 1 0
#> 244 0 0 0 0 1 0
#> 245 0 0 0 0 0 0
#> 246 0 0 0 0 1 0
#> 247 1 1 1 1 1 0
#> 248 0 1 0 0 1 0
#> 249 0 1 1 1 1 0
#> 250 0 0 1 0 1 0
#> 251 0 0 1 0 1 1
#> 252 0 1 1 0 0 1
#> 253 1 1 1 1 0 1
#> 254 1 1 1 1 1 1
#> 255 1 1 1 1 0 1
#> 256 0 1 1 0 0 1
#> 257 1 1 1 0 0 1
#> 258 0 1 1 0 0 1
#> 259 0 1 1 0 0 1
#> 260 0 0 1 1 1 1
#> 261 0 1 1 0 0 1
#> 262 1 0 1 0 0 1
#> 263 0 0 1 0 0 1
#> 264 0 0 1 1 0 1
#> 265 1 1 1 1 0 1
#> 266 0 1 0 1 0 1
#> 267 0 1 1 1 0 1
#> 268 0 1 1 0 0 1
#> 269 1 1 0 0 0 1
#> 270 1 1 1 1 0 1
#> 271 0 1 0 1 0 1
#> 272 0 0 1 1 0 1
#> 273 1 1 1 0 1 1
#> 274 0 1 0 0 0 1
#> 275 0 1 1 1 0 1
#> 276 0 0 1 1 0 1
#> 277 1 1 1 1 0 1
#> 278 0 1 1 1 0 1
#> 279 0 1 1 1 1 1
#> 280 0 0 1 0 0 1
#> 281 0 1 1 0 0 1
#> 282 1 0 1 1 0 1
#> 283 1 1 1 0 0 1
#> 284 0 1 1 1 0 1
#> 285 0 1 1 1 0 1
#> 286 0 0 1 0 0 1
#> 287 1 0 1 1 0 1
#> 288 1 0 0 0 0 1
#> 289 0 1 1 1 0 1
#> 290 1 1 0 1 0 1
#> 291 1 0 0 1 0 1
#> 292 0 0 1 0 0 1
#> 293 0 0 1 1 0 1
#> 294 1 1 1 1 0 1
#> 295 1 0 0 1 0 1
#> 296 0 1 0 0 0 1
#> 297 0 0 0 0 0 1
#> 298 1 0 1 1 1 1
#> 299 1 1 1 1 0 1
#> 300 0 1 1 0 0 1
# generating parameters for nominal data with DDF, 5 items,
# each item 3 choices
a <- matrix(runif(20, 0.8, 2), ncol = 4)
b <- matrix(runif(20, -2, 2), ncol = 4)
# generating nominal data set with 300 observations (150 each group)
genNLR(N = 300, itemtype = "nominal", a = a, b = b)
#> Item1 Item2 Item3 Item4 Item5 group
#> 1 A B A A B 0
#> 2 A C B C B 0
#> 3 A B A B B 0
#> 4 A B A A B 0
#> 5 B A B B B 0
#> 6 A B A A B 0
#> 7 B A A B A 0
#> 8 B A A C B 0
#> 9 A A A B A 0
#> 10 A A A B B 0
#> 11 B C A B B 0
#> 12 A B A A B 0
#> 13 B B A A C 0
#> 14 A A A A A 0
#> 15 C C C A C 0
#> 16 A A A A A 0
#> 17 A B A B B 0
#> 18 A A A B B 0
#> 19 C B A B B 0
#> 20 B A A B B 0
#> 21 B B A B B 0
#> 22 A A A A B 0
#> 23 A B A A C 0
#> 24 A A A A B 0
#> 25 A A A B A 0
#> 26 B A A A A 0
#> 27 A A A A A 0
#> 28 A A A A A 0
#> 29 A A A A A 0
#> 30 A A A A A 0
#> 31 A A A B A 0
#> 32 B A A B A 0
#> 33 B B A A B 0
#> 34 C B A A B 0
#> 35 A B A B B 0
#> 36 B B A C B 0
#> 37 A A A A A 0
#> 38 C A B B C 0
#> 39 C B A C A 0
#> 40 B A A A A 0
#> 41 A A A C B 0
#> 42 A A B B A 0
#> 43 B C A B B 0
#> 44 A A A A B 0
#> 45 A B A B B 0
#> 46 A C B C C 0
#> 47 B B A C A 0
#> 48 B B B B C 0
#> 49 A A B A B 0
#> 50 B A B B B 0
#> 51 B B A A A 0
#> 52 B A B A C 0
#> 53 B A A A A 0
#> 54 B B A B A 0
#> 55 C C C B C 0
#> 56 A B A A A 0
#> 57 A A A C A 0
#> 58 A B A B A 0
#> 59 C C C C C 0
#> 60 B A A B B 0
#> 61 A A B A B 0
#> 62 A B A C B 0
#> 63 A A A A A 0
#> 64 A A A A A 0
#> 65 A B A B C 0
#> 66 A A A A B 0
#> 67 B A A A A 0
#> 68 A A A A A 0
#> 69 A C A B A 0
#> 70 B B B C C 0
#> 71 C B B A C 0
#> 72 A A A C B 0
#> 73 A A B A A 0
#> 74 A B A A A 0
#> 75 A C B B C 0
#> 76 A B A A B 0
#> 77 A A A B A 0
#> 78 C C B C A 0
#> 79 B B A A A 0
#> 80 C B B B C 0
#> 81 A C B C B 0
#> 82 B A A A A 0
#> 83 B A A B B 0
#> 84 B B A B A 0
#> 85 A C A A B 0
#> 86 A A B A B 0
#> 87 A A A A B 0
#> 88 C C A C B 0
#> 89 A B C C C 0
#> 90 B C B C B 0
#> 91 C C C C B 0
#> 92 C C C C C 0
#> 93 A C B C B 0
#> 94 B B A B C 0
#> 95 B C A C B 0
#> 96 A A A B B 0
#> 97 A A A B A 0
#> 98 A A A A A 0
#> 99 A A A B B 0
#> 100 A A A A B 0
#> 101 B C B C B 0
#> 102 B B B A B 0
#> 103 C B A B B 0
#> 104 B A A B A 0
#> 105 A A A A A 0
#> 106 A A A A C 0
#> 107 A B B A B 0
#> 108 C C C C C 0
#> 109 A A A B B 0
#> 110 B B A B A 0
#> 111 B C A A B 0
#> 112 A A A A A 0
#> 113 B B A B A 0
#> 114 B C B A B 0
#> 115 B C A B C 0
#> 116 B B B A C 0
#> 117 B A B A A 0
#> 118 C A A B B 0
#> 119 A B A B C 0
#> 120 A A A A A 0
#> 121 B A A B A 0
#> 122 B A A B C 0
#> 123 B B B B B 0
#> 124 B A A A C 0
#> 125 A B A B A 0
#> 126 A A A B A 0
#> 127 C C C C C 0
#> 128 C C B C C 0
#> 129 A A A A B 0
#> 130 A A A A A 0
#> 131 B B A C C 0
#> 132 A C A C B 0
#> 133 C C B C C 0
#> 134 A A A A A 0
#> 135 C C B B B 0
#> 136 A A A A B 0
#> 137 A A A A A 0
#> 138 B C A B C 0
#> 139 A A A B B 0
#> 140 A A A A A 0
#> 141 A A A C B 0
#> 142 C C A A B 0
#> 143 A B A C A 0
#> 144 A A A B A 0
#> 145 A A A A A 0
#> 146 C B B B C 0
#> 147 A B A A B 0
#> 148 B C A B B 0
#> 149 A A B A A 0
#> 150 A B A B B 0
#> 151 B A A B A 1
#> 152 A A A A A 1
#> 153 A A A A A 1
#> 154 B B A A A 1
#> 155 B A A A A 1
#> 156 B A A A A 1
#> 157 B B B A A 1
#> 158 A B A A A 1
#> 159 B A A A A 1
#> 160 A A A A B 1
#> 161 A A A B A 1
#> 162 A B A B A 1
#> 163 A A A A A 1
#> 164 A B A A A 1
#> 165 B B C B A 1
#> 166 B C A A A 1
#> 167 B C B A B 1
#> 168 B B A A A 1
#> 169 C B B A A 1
#> 170 A C B A A 1
#> 171 C C B A B 1
#> 172 B A A A A 1
#> 173 A A A B A 1
#> 174 A C A B A 1
#> 175 A B A A A 1
#> 176 A A A A A 1
#> 177 B C B C B 1
#> 178 A B A A A 1
#> 179 B A A A A 1
#> 180 A A A B A 1
#> 181 A A A A A 1
#> 182 A B B A A 1
#> 183 A A A A A 1
#> 184 B A A A A 1
#> 185 C C B C A 1
#> 186 A B A B B 1
#> 187 B A A A A 1
#> 188 A B A A A 1
#> 189 A A A A A 1
#> 190 A A A A A 1
#> 191 A B A A A 1
#> 192 B B B C A 1
#> 193 A B A B A 1
#> 194 A C A B A 1
#> 195 B A A A A 1
#> 196 A B A A A 1
#> 197 B A A A A 1
#> 198 A B C A A 1
#> 199 A A A B A 1
#> 200 A B A A A 1
#> 201 A B B A B 1
#> 202 A B B A A 1
#> 203 A A A A A 1
#> 204 B A A A A 1
#> 205 B C A B A 1
#> 206 A B A A A 1
#> 207 A C B A A 1
#> 208 A B A A A 1
#> 209 A B A A A 1
#> 210 C B A C A 1
#> 211 B C A A B 1
#> 212 A A A A A 1
#> 213 A B A B A 1
#> 214 B B A B A 1
#> 215 A A A A A 1
#> 216 A B A A A 1
#> 217 B B B B A 1
#> 218 C C B A A 1
#> 219 B B B C A 1
#> 220 A B A B A 1
#> 221 A A A A A 1
#> 222 A B A B A 1
#> 223 B B A A A 1
#> 224 C B B A A 1
#> 225 A B B A A 1
#> 226 B B A A A 1
#> 227 B C B B A 1
#> 228 A A A A A 1
#> 229 A A A A A 1
#> 230 A A A B A 1
#> 231 A A A A A 1
#> 232 B B C B B 1
#> 233 A B A A A 1
#> 234 A A A A A 1
#> 235 A A A A B 1
#> 236 B A A A A 1
#> 237 C B B B A 1
#> 238 A B A A A 1
#> 239 C C C C B 1
#> 240 B A A B A 1
#> 241 A A A A A 1
#> 242 A B A A A 1
#> 243 B C B C B 1
#> 244 A A A A A 1
#> 245 A A B B A 1
#> 246 B B A B A 1
#> 247 C C C C B 1
#> 248 A B A B A 1
#> 249 A C A B A 1
#> 250 A B A A A 1
#> 251 A A B A A 1
#> 252 B C A B A 1
#> 253 B A A A A 1
#> 254 A B A A A 1
#> 255 B B A A A 1
#> 256 B A A A A 1
#> 257 B B A B A 1
#> 258 A A A A A 1
#> 259 A B B A B 1
#> 260 A C B A A 1
#> 261 A A A A A 1
#> 262 A A A A A 1
#> 263 A A A A A 1
#> 264 A B A A A 1
#> 265 A A A A A 1
#> 266 B C B A B 1
#> 267 A A A A A 1
#> 268 A A A A A 1
#> 269 B B A B A 1
#> 270 A A A A A 1
#> 271 A A B A A 1
#> 272 A A A A A 1
#> 273 B B B A A 1
#> 274 A A A A A 1
#> 275 C C B C A 1
#> 276 A A A A A 1
#> 277 A B B B B 1
#> 278 A C A A A 1
#> 279 A B A A A 1
#> 280 B C B B A 1
#> 281 B B B A A 1
#> 282 A A B A A 1
#> 283 B A A A A 1
#> 284 A A A A A 1
#> 285 C C B B B 1
#> 286 A A A A A 1
#> 287 A A A A A 1
#> 288 A C A C A 1
#> 289 A A A A A 1
#> 290 A A A A A 1
#> 291 B C B C A 1
#> 292 B B B B A 1
#> 293 B C C C B 1
#> 294 A A A B A 1
#> 295 B C A C A 1
#> 296 A B A A A 1
#> 297 A B A A A 1
#> 298 A B A A A 1
#> 299 A A B A A 1
#> 300 A A A B A 1
# generating nominal data set with 300 observations (250 reference group, 50 focal)
genNLR(N = 300, itemtype = "nominal", ratio = 5, a = a, b = b)
#> Item1 Item2 Item3 Item4 Item5 group
#> 1 B A A B B 0
#> 2 A A A A C 0
#> 3 A B B B A 0
#> 4 C B A B A 0
#> 5 B B A A B 0
#> 6 B A A B A 0
#> 7 C B A C B 0
#> 8 B B A C B 0
#> 9 A C A A B 0
#> 10 A C C B B 0
#> 11 B A A A A 0
#> 12 A B A B B 0
#> 13 A A A C A 0
#> 14 A A A A B 0
#> 15 A A A A B 0
#> 16 A A A A A 0
#> 17 C B A C A 0
#> 18 B A B A B 0
#> 19 B C A B C 0
#> 20 A A A B B 0
#> 21 B A A B C 0
#> 22 A A A A A 0
#> 23 C A A C B 0
#> 24 A C C C B 0
#> 25 A A A A B 0
#> 26 C B A B B 0
#> 27 A A A B A 0
#> 28 A B A B A 0
#> 29 A A A B A 0
#> 30 C C C C B 0
#> 31 C C A B C 0
#> 32 A A A B A 0
#> 33 B B A B A 0
#> 34 A B C C B 0
#> 35 A A A A C 0
#> 36 A B B B A 0
#> 37 A A A A A 0
#> 38 A B B A C 0
#> 39 B B A B B 0
#> 40 C B A A A 0
#> 41 A A A A B 0
#> 42 A B B C C 0
#> 43 B A A B A 0
#> 44 A A A B A 0
#> 45 B B A B B 0
#> 46 A A A A B 0
#> 47 A B B B A 0
#> 48 A B A B C 0
#> 49 A A A A B 0
#> 50 A A A A A 0
#> 51 A A A B A 0
#> 52 A A A A B 0
#> 53 B B A C B 0
#> 54 C C C B C 0
#> 55 A A A A A 0
#> 56 B B B B C 0
#> 57 C C B A A 0
#> 58 A A A A B 0
#> 59 A A B A B 0
#> 60 A A A A A 0
#> 61 A A A A B 0
#> 62 A A C A B 0
#> 63 B A A A B 0
#> 64 A B A A B 0
#> 65 A A A A C 0
#> 66 A A A A A 0
#> 67 A A B A B 0
#> 68 A A A B B 0
#> 69 A C B B C 0
#> 70 A A A A B 0
#> 71 C C C C C 0
#> 72 A A A C B 0
#> 73 B A A A C 0
#> 74 A C A B B 0
#> 75 A A A B B 0
#> 76 A A A A A 0
#> 77 B B B A B 0
#> 78 B A B C C 0
#> 79 B B B C B 0
#> 80 A A A A C 0
#> 81 A A A A A 0
#> 82 B B A C A 0
#> 83 B A A A B 0
#> 84 C C A B C 0
#> 85 C B A B B 0
#> 86 A A A A A 0
#> 87 A A A A A 0
#> 88 C B B C B 0
#> 89 B C B C C 0
#> 90 A B A C A 0
#> 91 A A A A A 0
#> 92 C C B B C 0
#> 93 C C B C B 0
#> 94 B C A B B 0
#> 95 B A A A A 0
#> 96 A B A B B 0
#> 97 A A A B A 0
#> 98 B B B C B 0
#> 99 B C A C B 0
#> 100 A A A A A 0
#> 101 A A B C C 0
#> 102 B B A B B 0
#> 103 C B C B B 0
#> 104 B B A B C 0
#> 105 A C B B A 0
#> 106 C C B C C 0
#> 107 B C B B B 0
#> 108 B B A B C 0
#> 109 B A A A A 0
#> 110 A A A A B 0
#> 111 A B B B B 0
#> 112 A A B B A 0
#> 113 A B A A A 0
#> 114 A A A A A 0
#> 115 B B B A C 0
#> 116 A A A A B 0
#> 117 B A A C B 0
#> 118 B B A B C 0
#> 119 B B C C C 0
#> 120 C A A B C 0
#> 121 C B B A A 0
#> 122 B B B C C 0
#> 123 B C A B C 0
#> 124 B A A A C 0
#> 125 A A A A B 0
#> 126 C C A B B 0
#> 127 B A A B A 0
#> 128 A B B B C 0
#> 129 A A A A A 0
#> 130 A A A B B 0
#> 131 A A A A B 0
#> 132 B B B C B 0
#> 133 A B A B A 0
#> 134 A A A B A 0
#> 135 B B A C A 0
#> 136 A A A A A 0
#> 137 B B A A A 0
#> 138 A A A A A 0
#> 139 B B A B C 0
#> 140 C C C A B 0
#> 141 C C C C C 0
#> 142 B C B A B 0
#> 143 A A A A A 0
#> 144 A A A A A 0
#> 145 A A B B A 0
#> 146 A A A B B 0
#> 147 B B B B B 0
#> 148 A C A B B 0
#> 149 B C B A B 0
#> 150 B C B B B 0
#> 151 B C C B B 0
#> 152 B C A A A 0
#> 153 B A B B A 0
#> 154 A A A A B 0
#> 155 A A A A A 0
#> 156 C C C B C 0
#> 157 B B A A B 0
#> 158 B B A C A 0
#> 159 A C B B B 0
#> 160 C B B B C 0
#> 161 B A A B B 0
#> 162 C C B C C 0
#> 163 A A A B C 0
#> 164 B B B C B 0
#> 165 A A A A A 0
#> 166 B C A B C 0
#> 167 B A A B B 0
#> 168 B C B C C 0
#> 169 B B A A A 0
#> 170 A A A A B 0
#> 171 A B B B B 0
#> 172 A A A A A 0
#> 173 B C A B C 0
#> 174 A A B B A 0
#> 175 C A A B C 0
#> 176 B B B B C 0
#> 177 A B A B A 0
#> 178 B B A A C 0
#> 179 A B A B B 0
#> 180 A B C A B 0
#> 181 B A A B B 0
#> 182 A A A A A 0
#> 183 A A A B A 0
#> 184 B A A B B 0
#> 185 C A A A B 0
#> 186 A A A A A 0
#> 187 A B A A A 0
#> 188 A A B A B 0
#> 189 A A A B B 0
#> 190 A B A B B 0
#> 191 A A A A A 0
#> 192 A A A A A 0
#> 193 B A B B A 0
#> 194 A B A A B 0
#> 195 C B B B B 0
#> 196 A B A C B 0
#> 197 B A C A B 0
#> 198 A C C C B 0
#> 199 A A A A A 0
#> 200 B B B C B 0
#> 201 A A A A A 0
#> 202 B B A C C 0
#> 203 B B B B B 0
#> 204 A B A A A 0
#> 205 A A A C B 0
#> 206 B C A B C 0
#> 207 A A A A B 0
#> 208 C B B C C 0
#> 209 C C A B C 0
#> 210 B C A C B 0
#> 211 B A B B B 0
#> 212 A A A A C 0
#> 213 B B A B C 0
#> 214 A B A B B 0
#> 215 B A A B A 0
#> 216 A A A A C 0
#> 217 B B B B B 0
#> 218 C B C B C 0
#> 219 B C A B B 0
#> 220 C C B C C 0
#> 221 A A A B A 0
#> 222 A A A A A 0
#> 223 C C A B C 0
#> 224 B C A B C 0
#> 225 B C A B C 0
#> 226 A A A A B 0
#> 227 B A A B A 0
#> 228 A B A A B 0
#> 229 A A A A A 0
#> 230 B A B B C 0
#> 231 A A A A B 0
#> 232 B C A C C 0
#> 233 B A B C B 0
#> 234 A C B A B 0
#> 235 A B B B A 0
#> 236 C C B B C 0
#> 237 A A A A A 0
#> 238 C C A B C 0
#> 239 B A B A B 0
#> 240 A A A A B 0
#> 241 A A A B B 0
#> 242 A A A B A 0
#> 243 A B B B B 0
#> 244 B A B C C 0
#> 245 C C B C C 0
#> 246 B C B C B 0
#> 247 B C C C B 0
#> 248 C B B B B 0
#> 249 A A A A A 0
#> 250 A A B B A 0
#> 251 A B A A B 1
#> 252 A A A A A 1
#> 253 B B A B A 1
#> 254 A B A A A 1
#> 255 B B B A A 1
#> 256 A A A B A 1
#> 257 A C B A A 1
#> 258 A A A A A 1
#> 259 A A A A A 1
#> 260 B B A A A 1
#> 261 A A A A A 1
#> 262 A C B A B 1
#> 263 B A A A A 1
#> 264 A C C B C 1
#> 265 A B A A A 1
#> 266 A A A A A 1
#> 267 A A A A A 1
#> 268 A A A B A 1
#> 269 A A A A A 1
#> 270 C C B C B 1
#> 271 A A A A A 1
#> 272 A B A B B 1
#> 273 A A A A A 1
#> 274 B B A A A 1
#> 275 A A A A A 1
#> 276 B C A A A 1
#> 277 A A A A A 1
#> 278 B A A A A 1
#> 279 A A A A A 1
#> 280 B C A C A 1
#> 281 A B A A A 1
#> 282 B C A B A 1
#> 283 A A A A A 1
#> 284 B B A B A 1
#> 285 A B A A A 1
#> 286 A A A A A 1
#> 287 A A B A B 1
#> 288 A B A A A 1
#> 289 A B A B A 1
#> 290 A A A A A 1
#> 291 B B A B A 1
#> 292 B C B C A 1
#> 293 A B A C A 1
#> 294 B B B C A 1
#> 295 A A A A A 1
#> 296 A A B B A 1
#> 297 A A A A A 1
#> 298 A A A A A 1
#> 299 A A A A A 1
#> 300 A A A B A 1
# generating parameters for nominal data with DDF, 5 items,
# items 1 and 2 have 2 choices, items 3, 4 and 5 have 3 choices
a <- matrix(runif(20, 0.8, 2), ncol = 4)
a[1:2, c(2, 4)] <- NA
b <- matrix(runif(20, -2, 2), ncol = 4)
b[1:2, c(2, 4)] <- NA
# generating nominal data set with 300 observations (150 each group)
genNLR(N = 300, itemtype = "nominal", a = a, b = b)
#> Item1 Item2 Item3 Item4 Item5 group
#> 1 A A A A A 0
#> 2 B B A A C 0
#> 3 B B C B B 0
#> 4 A B A A B 0
#> 5 A B B A C 0
#> 6 A B A B B 0
#> 7 A B A B C 0
#> 8 A A A B C 0
#> 9 A B A A B 0
#> 10 A B B B C 0
#> 11 B B A B C 0
#> 12 A B A A A 0
#> 13 A A A A C 0
#> 14 B B B B C 0
#> 15 B B C C C 0
#> 16 B B B C C 0
#> 17 A B B A B 0
#> 18 A B A A B 0
#> 19 A A B A A 0
#> 20 A A B B B 0
#> 21 A B A B B 0
#> 22 B B A B C 0
#> 23 A B B A C 0
#> 24 A B C B C 0
#> 25 B B B B C 0
#> 26 A A A B B 0
#> 27 B B C B C 0
#> 28 B B B B C 0
#> 29 A A A B B 0
#> 30 B B B B A 0
#> 31 B B A A B 0
#> 32 B B C B C 0
#> 33 A B A A A 0
#> 34 B B A B C 0
#> 35 A B A B A 0
#> 36 B B A B B 0
#> 37 A B A B C 0
#> 38 B B A B C 0
#> 39 A B A A A 0
#> 40 A B B B B 0
#> 41 A A A A B 0
#> 42 B B B A C 0
#> 43 B A B C B 0
#> 44 B B A A B 0
#> 45 A A B A B 0
#> 46 A B A A B 0
#> 47 A B A C C 0
#> 48 A A A B B 0
#> 49 B B A A C 0
#> 50 A B A A C 0
#> 51 B B C A B 0
#> 52 A B B A C 0
#> 53 B B A A B 0
#> 54 B B C C B 0
#> 55 B B A C C 0
#> 56 A A B B C 0
#> 57 B B B B C 0
#> 58 A B B C C 0
#> 59 A A A A A 0
#> 60 B B B A C 0
#> 61 B B B B B 0
#> 62 B B A C C 0
#> 63 B A A B B 0
#> 64 B A A B A 0
#> 65 A B A B B 0
#> 66 B B B C C 0
#> 67 A A A A B 0
#> 68 A B C A B 0
#> 69 B B A A B 0
#> 70 B B C A C 0
#> 71 A B B B B 0
#> 72 A B B B C 0
#> 73 A B A A A 0
#> 74 A B A A C 0
#> 75 A B A B B 0
#> 76 B B C C C 0
#> 77 A B A A B 0
#> 78 A B B B B 0
#> 79 B B C B C 0
#> 80 B B A A C 0
#> 81 B B C A C 0
#> 82 B B A A B 0
#> 83 B B A A B 0
#> 84 A A A A A 0
#> 85 A A A A A 0
#> 86 A B A B C 0
#> 87 B A A A B 0
#> 88 A B A B C 0
#> 89 A A A A A 0
#> 90 B B B B C 0
#> 91 B B A B C 0
#> 92 A B A A B 0
#> 93 B A A A A 0
#> 94 B B B A C 0
#> 95 B B A C B 0
#> 96 A A A A B 0
#> 97 B A B A B 0
#> 98 B B A B C 0
#> 99 A B A A A 0
#> 100 A A A B B 0
#> 101 B B B C C 0
#> 102 B B A A C 0
#> 103 A B A A A 0
#> 104 B B B C C 0
#> 105 B B A A A 0
#> 106 B A A C B 0
#> 107 A B B B C 0
#> 108 A A A A B 0
#> 109 A A A A A 0
#> 110 B A A B B 0
#> 111 B B B B C 0
#> 112 A B A A C 0
#> 113 A A A A A 0
#> 114 B B A B C 0
#> 115 B B B A C 0
#> 116 B B A A B 0
#> 117 B B C C C 0
#> 118 A B A A B 0
#> 119 A B B A C 0
#> 120 A B A A C 0
#> 121 A A A A A 0
#> 122 A B A A B 0
#> 123 A B A A B 0
#> 124 A B B B C 0
#> 125 B A A A B 0
#> 126 B B A A C 0
#> 127 A B A A B 0
#> 128 A B A A B 0
#> 129 B B A C C 0
#> 130 A A A A A 0
#> 131 A A A A B 0
#> 132 B B B A B 0
#> 133 A A A A A 0
#> 134 A A A A A 0
#> 135 B B A B B 0
#> 136 B B A A C 0
#> 137 A B A A A 0
#> 138 A B A B C 0
#> 139 A B A A C 0
#> 140 A B A A B 0
#> 141 B B B B B 0
#> 142 B B A B B 0
#> 143 A B A B B 0
#> 144 A B A C A 0
#> 145 A B A A C 0
#> 146 B B B B C 0
#> 147 A B B A B 0
#> 148 B A A A C 0
#> 149 B B A A B 0
#> 150 B B A B C 0
#> 151 B B B A C 1
#> 152 B B C B C 1
#> 153 B B B A C 1
#> 154 B B A B A 1
#> 155 B B C B C 1
#> 156 B B A B B 1
#> 157 B B A C C 1
#> 158 B B B B C 1
#> 159 B B A A C 1
#> 160 B B A B C 1
#> 161 B B A B C 1
#> 162 B A A A A 1
#> 163 A B A A A 1
#> 164 A A A A B 1
#> 165 A A A A B 1
#> 166 B B A A A 1
#> 167 B B B B B 1
#> 168 B B B A C 1
#> 169 B B A B C 1
#> 170 B A A A C 1
#> 171 B B C A C 1
#> 172 B A C C C 1
#> 173 B B A B B 1
#> 174 B B A B C 1
#> 175 B B A A C 1
#> 176 B B A C C 1
#> 177 B A A A A 1
#> 178 B B A B B 1
#> 179 B B B A C 1
#> 180 B B C C C 1
#> 181 B B C C C 1
#> 182 B B A B C 1
#> 183 B B B A C 1
#> 184 B A B A A 1
#> 185 B A A A C 1
#> 186 B B A A B 1
#> 187 B A A A C 1
#> 188 B B B C C 1
#> 189 B B B C B 1
#> 190 B B A B C 1
#> 191 B B C B C 1
#> 192 B B C B C 1
#> 193 B B B C C 1
#> 194 B A A A B 1
#> 195 B B A B C 1
#> 196 B A B B C 1
#> 197 B A A B A 1
#> 198 B B A A C 1
#> 199 B B A B C 1
#> 200 A B A A C 1
#> 201 B A A C C 1
#> 202 B B A B C 1
#> 203 B B C B C 1
#> 204 B B A C C 1
#> 205 B A A A C 1
#> 206 B B A B A 1
#> 207 B B A B C 1
#> 208 A B A A B 1
#> 209 B B A A B 1
#> 210 B A A A B 1
#> 211 B B B A C 1
#> 212 B B A A C 1
#> 213 B B A A C 1
#> 214 B A B B C 1
#> 215 B B A A B 1
#> 216 B B B B C 1
#> 217 B A C B C 1
#> 218 B B A A B 1
#> 219 B B B B C 1
#> 220 B B B C B 1
#> 221 A B B A C 1
#> 222 B B B B C 1
#> 223 B B A A B 1
#> 224 B B B A C 1
#> 225 B B A A C 1
#> 226 B B B B C 1
#> 227 B A A A B 1
#> 228 B B B A C 1
#> 229 B A A A A 1
#> 230 B B A A C 1
#> 231 B B B B C 1
#> 232 A B A A B 1
#> 233 B B A B B 1
#> 234 B A A C B 1
#> 235 B B A A C 1
#> 236 B A A A B 1
#> 237 B A A A C 1
#> 238 B B A A C 1
#> 239 A A A A C 1
#> 240 B A A A A 1
#> 241 A B A A B 1
#> 242 B A A A C 1
#> 243 B B B B C 1
#> 244 B A A A B 1
#> 245 B B B A C 1
#> 246 B B A A A 1
#> 247 B B C C C 1
#> 248 B B A C C 1
#> 249 B A A B A 1
#> 250 B B C C C 1
#> 251 A B A A B 1
#> 252 B B B A C 1
#> 253 A B A A C 1
#> 254 B B C C C 1
#> 255 B B A A A 1
#> 256 B B B B B 1
#> 257 B B A C C 1
#> 258 B B A B B 1
#> 259 B B A A C 1
#> 260 B B C C B 1
#> 261 B B C C C 1
#> 262 B B B A C 1
#> 263 B B A A B 1
#> 264 B A A B A 1
#> 265 B A B A C 1
#> 266 B B A C B 1
#> 267 B A A A B 1
#> 268 B B B A C 1
#> 269 B B A A C 1
#> 270 B B B A C 1
#> 271 B A A A C 1
#> 272 A B A A B 1
#> 273 B B A B C 1
#> 274 B A A B A 1
#> 275 B B B B C 1
#> 276 B A B A B 1
#> 277 B B A A C 1
#> 278 B A A A A 1
#> 279 B A A A A 1
#> 280 B B B C C 1
#> 281 A A A B C 1
#> 282 B B B B C 1
#> 283 B B B B B 1
#> 284 A B A A A 1
#> 285 A B A A C 1
#> 286 A A A A A 1
#> 287 B A A A A 1
#> 288 B B A A C 1
#> 289 B B A A B 1
#> 290 A B A A B 1
#> 291 B B C B C 1
#> 292 B B C A C 1
#> 293 B B A B C 1
#> 294 B B A A B 1
#> 295 B B C C C 1
#> 296 B B A A C 1
#> 297 B B B C C 1
#> 298 B B A B C 1
#> 299 B B C C C 1
#> 300 B B A B B 1
# generating nominal data set with 300 observations (250 reference group, 50 focal)
genNLR(N = 300, itemtype = "nominal", ratio = 5, a = a, b = b)
#> Item1 Item2 Item3 Item4 Item5 group
#> 1 A B A A B 0
#> 2 A B A B B 0
#> 3 B B A A C 0
#> 4 B B C B B 0
#> 5 B A C B C 0
#> 6 A A A B B 0
#> 7 A B A A C 0
#> 8 B B A C B 0
#> 9 A B A B C 0
#> 10 B B B B C 0
#> 11 A B A C C 0
#> 12 A B A A C 0
#> 13 B B C C C 0
#> 14 B B A A C 0
#> 15 A B B B C 0
#> 16 B B A B C 0
#> 17 A B A A B 0
#> 18 A A A A A 0
#> 19 A B A A B 0
#> 20 B B B B C 0
#> 21 A B A A C 0
#> 22 A B C C C 0
#> 23 A B C B B 0
#> 24 B B C C C 0
#> 25 A B B A C 0
#> 26 B B B B B 0
#> 27 B B C B C 0
#> 28 A B A A A 0
#> 29 A B A B C 0
#> 30 B B A C B 0
#> 31 B B A A C 0
#> 32 B B A A A 0
#> 33 A B A A B 0
#> 34 A B A A B 0
#> 35 B B B B B 0
#> 36 B B A B C 0
#> 37 A B B B C 0
#> 38 B B A B B 0
#> 39 B B A A B 0
#> 40 B B B B C 0
#> 41 B B B B B 0
#> 42 A B A B B 0
#> 43 B B B C C 0
#> 44 A A A A B 0
#> 45 B B A A C 0
#> 46 B B B C C 0
#> 47 B B A A C 0
#> 48 A A A A A 0
#> 49 B B B C C 0
#> 50 B B B C C 0
#> 51 B B A A B 0
#> 52 A B A A A 0
#> 53 B B A A C 0
#> 54 B A A A A 0
#> 55 B A A B A 0
#> 56 A B A B C 0
#> 57 A A A B A 0
#> 58 A B A A C 0
#> 59 A B B A B 0
#> 60 A A A A A 0
#> 61 A A A A B 0
#> 62 B B A B C 0
#> 63 B B C B C 0
#> 64 A B A A C 0
#> 65 A B A A A 0
#> 66 A B A A B 0
#> 67 A B A A A 0
#> 68 A B A B C 0
#> 69 B B A A B 0
#> 70 A B A A C 0
#> 71 A B A A B 0
#> 72 A B A B C 0
#> 73 A B A A C 0
#> 74 A B A A C 0
#> 75 A B A A C 0
#> 76 B B A A B 0
#> 77 A B A B B 0
#> 78 A B B B C 0
#> 79 B B A A C 0
#> 80 A A A A B 0
#> 81 B B A A C 0
#> 82 A A A A A 0
#> 83 B B B B B 0
#> 84 B B C B C 0
#> 85 B B C B B 0
#> 86 B B A A B 0
#> 87 A B B A C 0
#> 88 B B C A C 0
#> 89 B B C C C 0
#> 90 A A C B B 0
#> 91 A B A A A 0
#> 92 A A A A B 0
#> 93 A B A B B 0
#> 94 B B A A C 0
#> 95 B B C A B 0
#> 96 A A A A A 0
#> 97 A A A A B 0
#> 98 B B A A A 0
#> 99 A B A A A 0
#> 100 A A B A B 0
#> 101 A A A A C 0
#> 102 A B A A B 0
#> 103 A B B A C 0
#> 104 B B A A A 0
#> 105 A B A A A 0
#> 106 A B A C C 0
#> 107 A B A B B 0
#> 108 B B C B C 0
#> 109 B B A A C 0
#> 110 A A A B B 0
#> 111 A B A A B 0
#> 112 A B A A B 0
#> 113 B B A C C 0
#> 114 B A A A C 0
#> 115 A B A A B 0
#> 116 B B A B C 0
#> 117 B B A B C 0
#> 118 A B A A A 0
#> 119 A B A A C 0
#> 120 B B A B C 0
#> 121 B B A B B 0
#> 122 B B B A C 0
#> 123 A A A A A 0
#> 124 A A B A C 0
#> 125 B B B B B 0
#> 126 A B A A B 0
#> 127 A B B A C 0
#> 128 A B A A C 0
#> 129 A A A B C 0
#> 130 B B B B B 0
#> 131 A B A A C 0
#> 132 A A A A C 0
#> 133 B B C C C 0
#> 134 A B A B B 0
#> 135 B B B B C 0
#> 136 A B B B C 0
#> 137 A A A B C 0
#> 138 A A B A A 0
#> 139 B B B A B 0
#> 140 B A B A C 0
#> 141 A B A B C 0
#> 142 A A B B C 0
#> 143 A B A A A 0
#> 144 A A A A B 0
#> 145 B B B B C 0
#> 146 A B B A B 0
#> 147 A A A A B 0
#> 148 A B B A B 0
#> 149 A A A A B 0
#> 150 A B A B A 0
#> 151 B A A A C 0
#> 152 A A A B B 0
#> 153 B B B B C 0
#> 154 B A A A A 0
#> 155 B B C A C 0
#> 156 B B A A B 0
#> 157 A A C A C 0
#> 158 A B A A C 0
#> 159 A A A A A 0
#> 160 B B B B C 0
#> 161 B B B B C 0
#> 162 A B A B B 0
#> 163 A B A B C 0
#> 164 A A A A A 0
#> 165 A B B A C 0
#> 166 B B A A B 0
#> 167 A B A B B 0
#> 168 A B A B A 0
#> 169 A A C B C 0
#> 170 A B A B B 0
#> 171 A B B A C 0
#> 172 B B C B C 0
#> 173 B B C A B 0
#> 174 B B B A C 0
#> 175 A B A A A 0
#> 176 B B B B C 0
#> 177 A B A A B 0
#> 178 A B A A A 0
#> 179 B B B B C 0
#> 180 A B A A C 0
#> 181 A B A B B 0
#> 182 B B A B C 0
#> 183 A B B A A 0
#> 184 A B C A C 0
#> 185 A B A B C 0
#> 186 B B C C C 0
#> 187 B B A A C 0
#> 188 B B B C C 0
#> 189 B B B A C 0
#> 190 B B B A C 0
#> 191 B B A A A 0
#> 192 A B A A A 0
#> 193 B A A A C 0
#> 194 A B B C C 0
#> 195 A B A A B 0
#> 196 A A A A B 0
#> 197 A B C B C 0
#> 198 B B A B C 0
#> 199 B B A A A 0
#> 200 A B A A C 0
#> 201 A B A B C 0
#> 202 A B C A C 0
#> 203 A A A B B 0
#> 204 A B B B B 0
#> 205 B B A A B 0
#> 206 B B A B C 0
#> 207 A B A B C 0
#> 208 A A B C C 0
#> 209 B B B A C 0
#> 210 B B A A B 0
#> 211 A B A B C 0
#> 212 A B A A A 0
#> 213 A B A A C 0
#> 214 B B C B C 0
#> 215 B B B B C 0
#> 216 A B A A B 0
#> 217 B B B B C 0
#> 218 B A A A C 0
#> 219 B B B B C 0
#> 220 A B A A C 0
#> 221 A B A A A 0
#> 222 A B A C B 0
#> 223 A B A A C 0
#> 224 A A A A C 0
#> 225 A B A A A 0
#> 226 A B A A A 0
#> 227 B B A A C 0
#> 228 A B A B B 0
#> 229 A B A B C 0
#> 230 A B A A C 0
#> 231 A B A B B 0
#> 232 A B B B C 0
#> 233 B B A B C 0
#> 234 A B A B B 0
#> 235 B B A C C 0
#> 236 A B A A B 0
#> 237 A B A A C 0
#> 238 A B B B C 0
#> 239 B B C B B 0
#> 240 A A A A B 0
#> 241 B B A B C 0
#> 242 A B A A A 0
#> 243 A B A C B 0
#> 244 A B A B A 0
#> 245 B B A B C 0
#> 246 A B A A C 0
#> 247 B B B B C 0
#> 248 A A A A C 0
#> 249 A B A B C 0
#> 250 A B A A C 0
#> 251 B B A B C 1
#> 252 B B A A C 1
#> 253 B B C B C 1
#> 254 B B A A C 1
#> 255 B A A B B 1
#> 256 B B B A B 1
#> 257 B B A C C 1
#> 258 A B A C B 1
#> 259 B B A B C 1
#> 260 B B A B C 1
#> 261 B A A B C 1
#> 262 B A B A C 1
#> 263 B B A B C 1
#> 264 B B B C C 1
#> 265 B B A A C 1
#> 266 B B A C C 1
#> 267 B B A C C 1
#> 268 B B B B C 1
#> 269 B B B A C 1
#> 270 B A A A C 1
#> 271 B B A A B 1
#> 272 B A A A C 1
#> 273 B B B C C 1
#> 274 B B B B C 1
#> 275 B B A C C 1
#> 276 B A B A B 1
#> 277 B B A B B 1
#> 278 B A A B C 1
#> 279 B B A A C 1
#> 280 B B A A C 1
#> 281 B B A B C 1
#> 282 B B A B C 1
#> 283 B A A A A 1
#> 284 B B A A C 1
#> 285 B B A B C 1
#> 286 B B C B C 1
#> 287 B A A B B 1
#> 288 B B C B C 1
#> 289 B B C A C 1
#> 290 B B A A A 1
#> 291 A B B B C 1
#> 292 B B A B B 1
#> 293 B B A A B 1
#> 294 A A A A B 1
#> 295 B B A B C 1
#> 296 A B A A B 1
#> 297 B B A B A 1
#> 298 B B A A B 1
#> 299 B A A B C 1
#> 300 A B A A C 1