├── .Rhistory ├── README.md ├── baldwin2014-multivariate.R ├── berry2016-practical.R ├── bollen2004-autoregressive.R ├── curran1998-alcohol_trajectories.R ├── curran2014-separation.R ├── data ├── currandemo.dat └── masc_math.dat ├── little2006-scaling.R ├── millsap2004-FIorderedCategorical.R └── raykov2018-dichotomousMI.R /.Rhistory: -------------------------------------------------------------------------------- 1 | q15.2 | nu9*t1 2 | q1.3 | 0*t1 3 | q4.3 | nu2*t1 4 | q10.3 | nu3*t1 5 | q11.3 | nu4*t1 6 | q19.3 | nu5*t1 7 | q23.3 | nu6*t1 8 | q27.3 | nu7*t1 9 | q28.3 | nu8*t1 10 | q15.3 | nu9*t1 11 | # variances 12 | q1.1 ~~ q1.2 + q1.3 13 | q1.2 ~~ q1.3 14 | q4.1 ~~ q4.2 + q4.3 15 | q4.2 ~~ q4.3 16 | q10.1 ~~ q10.2 + q10.3 17 | q10.2 ~~ q10.3 18 | q19.1 ~~ q19.2 + q19.3 19 | q19.2 ~~ q19.3 20 | q23.1 ~~ q23.2 + q23.3 21 | q23.2 ~~ q23.3 22 | q27.1 ~~ q27.2 + q27.3 23 | q27.2 ~~ q27.3 24 | q28.1 ~~ q28.2 + q28.3 25 | q28.2 ~~ q28.3 26 | q15.1 ~~ q15.2 + q15.3 27 | q15.2 ~~ q15.3 28 | " 29 | fit.strong.eqmeans <- sem(strong.eqmeans.mod, data = pdr.wide, parameterization = "theta", estimator = "wlsmv") 30 | fit.strong.eqmeans <- sem(strong.eqmeans.mod, data = pdr.wide, parameterization = "theta", estimator = "wlsmv") 31 | strong.eqmeans.mod <- " 32 | # define the factor models 33 | fac2.1 =~ 1 * q1.1 + lam2 * q4.1 + lam3 * q10.1 + lam4 * q11.1 + lam5 * q19.1 + lam6 * q23.1 + lam7 * q27.1 + lam8 * q28.1 + lam9 * q15.1 34 | fac2.2 =~ 1 * q1.2 + lam2 * q4.2 + lam3 * q10.2 + lam4 * q11.2 + lam5 * q19.2 + lam6 * q23.2 + lam7 * q27.2 + lam8 * q28.2 + lam9 * q15.2 35 | fac2.3 =~ 1 * q1.3 + lam2 * q4.3 + lam3 * q10.3 + lam4 * q11.3 + lam5 * q19.3 + lam6 * q23.3 + lam7 * q27.3 + lam8 * q28.3 + lam9 * q15.3 36 | # latent common factor variances and covariances 37 | fac2.1 ~~ var1*fac2.1 + fac2.2 + fac2.3 38 | fac2.2 ~~ var1*fac2.2 + fac2.3 39 | fac2.3 ~~ var1*fac2.3 40 | # latent common factor means 41 | fac2.1 ~ int1*1 42 | fac2.2 ~ int1*1 43 | fac2.3 ~ int1*1 44 | # thresholds 45 | q1.1 | 0*t1 46 | q4.1 | nu2*t1 47 | q10.1 | nu3*t1 48 | q11.1 | nu4*t1 49 | q19.1 | nu5*t1 50 | q23.1 | nu6*t1 51 | q27.1 | nu7*t1 52 | q28.1 | nu8*t1 53 | q15.1 | nu9*t1 54 | q1.2 | 0*t1 55 | q4.2 | nu2*t1 56 | q10.2 | nu3*t1 57 | q11.2 | nu4*t1 58 | q19.2 | nu5*t1 59 | q23.2 | nu6*t1 60 | q27.2 | nu7*t1 61 | q28.2 | nu8*t1 62 | q15.2 | nu9*t1 63 | q1.3 | 0*t1 64 | q4.3 | nu2*t1 65 | q10.3 | nu3*t1 66 | q11.3 | nu4*t1 67 | q19.3 | nu5*t1 68 | q23.3 | nu6*t1 69 | q27.3 | nu7*t1 70 | q28.3 | nu8*t1 71 | q15.3 | nu9*t1 72 | # variances 73 | q1.1 ~~ q1.2 + q1.3 74 | q1.2 ~~ q1.3 75 | q4.1 ~~ q4.2 + q4.3 76 | q4.2 ~~ q4.3 77 | q10.1 ~~ q10.2 + q10.3 78 | q10.2 ~~ q10.3 79 | q19.1 ~~ q19.2 + q19.3 80 | q19.2 ~~ q19.3 81 | q23.1 ~~ q23.2 + q23.3 82 | q23.2 ~~ q23.3 83 | q27.1 ~~ q27.2 + q27.3 84 | q27.2 ~~ q27.3 85 | q28.1 ~~ q28.2 + q28.3 86 | q28.2 ~~ q28.3 87 | q15.1 ~~ q15.2 + q15.3 88 | q15.2 ~~ q15.3 89 | " 90 | fit.strong.eqmeans <- sem(strong.eqmeans.mod, data = pdr.wide, parameterization = "theta", estimator = "wlsmv") 91 | anova(fit.strong, fit.strong.eqmeans) 92 | strong.eqmeans.mod <- " 93 | # define the factor models 94 | fac2.1 =~ 1 * q1.1 + lam2 * q4.1 + lam3 * q10.1 + lam4 * q11.1 + lam5 * q19.1 + lam6 * q23.1 + lam7 * q27.1 + lam8 * q28.1 + lam9 * q15.1 95 | fac2.2 =~ 1 * q1.2 + lam2 * q4.2 + lam3 * q10.2 + lam4 * q11.2 + lam5 * q19.2 + lam6 * q23.2 + lam7 * q27.2 + lam8 * q28.2 + lam9 * q15.2 96 | fac2.3 =~ 1 * q1.3 + lam2 * q4.3 + lam3 * q10.3 + lam4 * q11.3 + lam5 * q19.3 + lam6 * q23.3 + lam7 * q27.3 + lam8 * q28.3 + lam9 * q15.3 97 | # latent common factor variances and covariances 98 | fac2.1 ~~ var1*fac2.1 + fac2.2 + fac2.3 99 | fac2.2 ~~ var1*fac2.2 + fac2.3 100 | fac2.3 ~~ var1*fac2.3 101 | # latent common factor means 102 | fac2.1 ~ 1 103 | fac2.2 ~ int1*1 104 | fac2.3 ~ int1*1 105 | # thresholds 106 | q1.1 | 0*t1 107 | q4.1 | nu2*t1 108 | q10.1 | nu3*t1 109 | q11.1 | nu4*t1 110 | q19.1 | nu5*t1 111 | q23.1 | nu6*t1 112 | q27.1 | nu7*t1 113 | q28.1 | nu8*t1 114 | q15.1 | nu9*t1 115 | q1.2 | 0*t1 116 | q4.2 | nu2*t1 117 | q10.2 | nu3*t1 118 | q11.2 | nu4*t1 119 | q19.2 | nu5*t1 120 | q23.2 | nu6*t1 121 | q27.2 | nu7*t1 122 | q28.2 | nu8*t1 123 | q15.2 | nu9*t1 124 | q1.3 | 0*t1 125 | q4.3 | nu2*t1 126 | q10.3 | nu3*t1 127 | q11.3 | nu4*t1 128 | q19.3 | nu5*t1 129 | q23.3 | nu6*t1 130 | q27.3 | nu7*t1 131 | q28.3 | nu8*t1 132 | q15.3 | nu9*t1 133 | # variances 134 | q1.1 ~~ q1.2 + q1.3 135 | q1.2 ~~ q1.3 136 | q4.1 ~~ q4.2 + q4.3 137 | q4.2 ~~ q4.3 138 | q10.1 ~~ q10.2 + q10.3 139 | q10.2 ~~ q10.3 140 | q19.1 ~~ q19.2 + q19.3 141 | q19.2 ~~ q19.3 142 | q23.1 ~~ q23.2 + q23.3 143 | q23.2 ~~ q23.3 144 | q27.1 ~~ q27.2 + q27.3 145 | q27.2 ~~ q27.3 146 | q28.1 ~~ q28.2 + q28.3 147 | q28.2 ~~ q28.3 148 | q15.1 ~~ q15.2 + q15.3 149 | q15.2 ~~ q15.3 150 | " 151 | fit.strong.eqmeans <- sem(strong.eqmeans.mod, data = pdr.wide, parameterization = "theta", estimator = "wlsmv") 152 | anova(fit.strong, fit.strong.eqmeans) 153 | strong.eqmeans.mod <- " 154 | # define the factor models 155 | fac2.1 =~ 1 * q1.1 + lam2 * q4.1 + lam3 * q10.1 + lam4 * q11.1 + lam5 * q19.1 + lam6 * q23.1 + lam7 * q27.1 + lam8 * q28.1 + lam9 * q15.1 156 | fac2.2 =~ 1 * q1.2 + lam2 * q4.2 + lam3 * q10.2 + lam4 * q11.2 + lam5 * q19.2 + lam6 * q23.2 + lam7 * q27.2 + lam8 * q28.2 + lam9 * q15.2 157 | fac2.3 =~ 1 * q1.3 + lam2 * q4.3 + lam3 * q10.3 + lam4 * q11.3 + lam5 * q19.3 + lam6 * q23.3 + lam7 * q27.3 + lam8 * q28.3 + lam9 * q15.3 158 | # latent common factor variances and covariances 159 | fac2.1 ~~ var1*fac2.1 + fac2.2 + fac2.3 160 | fac2.2 ~~ var1*fac2.2 + fac2.3 161 | fac2.3 ~~ var1*fac2.3 162 | # latent common factor means 163 | fac2.1 ~ int1*1 164 | fac2.2 ~ int1*1 165 | fac2.3 ~ 1 166 | # thresholds 167 | q1.1 | 0*t1 168 | q4.1 | nu2*t1 169 | q10.1 | nu3*t1 170 | q11.1 | nu4*t1 171 | q19.1 | nu5*t1 172 | q23.1 | nu6*t1 173 | q27.1 | nu7*t1 174 | q28.1 | nu8*t1 175 | q15.1 | nu9*t1 176 | q1.2 | 0*t1 177 | q4.2 | nu2*t1 178 | q10.2 | nu3*t1 179 | q11.2 | nu4*t1 180 | q19.2 | nu5*t1 181 | q23.2 | nu6*t1 182 | q27.2 | nu7*t1 183 | q28.2 | nu8*t1 184 | q15.2 | nu9*t1 185 | q1.3 | 0*t1 186 | q4.3 | nu2*t1 187 | q10.3 | nu3*t1 188 | q11.3 | nu4*t1 189 | q19.3 | nu5*t1 190 | q23.3 | nu6*t1 191 | q27.3 | nu7*t1 192 | q28.3 | nu8*t1 193 | q15.3 | nu9*t1 194 | # variances 195 | q1.1 ~~ q1.2 + q1.3 196 | q1.2 ~~ q1.3 197 | q4.1 ~~ q4.2 + q4.3 198 | q4.2 ~~ q4.3 199 | q10.1 ~~ q10.2 + q10.3 200 | q10.2 ~~ q10.3 201 | q19.1 ~~ q19.2 + q19.3 202 | q19.2 ~~ q19.3 203 | q23.1 ~~ q23.2 + q23.3 204 | q23.2 ~~ q23.3 205 | q27.1 ~~ q27.2 + q27.3 206 | q27.2 ~~ q27.3 207 | q28.1 ~~ q28.2 + q28.3 208 | q28.2 ~~ q28.3 209 | q15.1 ~~ q15.2 + q15.3 210 | q15.2 ~~ q15.3 211 | " 212 | fit.strong.eqmeans <- sem(strong.eqmeans.mod, data = pdr.wide, parameterization = "theta", estimator = "wlsmv") 213 | anova(fit.strong, fit.strong.eqmeans) 214 | strong.eqmeans.mod <- " 215 | # define the factor models 216 | fac2.1 =~ 1 * q1.1 + lam2 * q4.1 + lam3 * q10.1 + lam4 * q11.1 + lam5 * q19.1 + lam6 * q23.1 + lam7 * q27.1 + lam8 * q28.1 + lam9 * q15.1 217 | fac2.2 =~ 1 * q1.2 + lam2 * q4.2 + lam3 * q10.2 + lam4 * q11.2 + lam5 * q19.2 + lam6 * q23.2 + lam7 * q27.2 + lam8 * q28.2 + lam9 * q15.2 218 | fac2.3 =~ 1 * q1.3 + lam2 * q4.3 + lam3 * q10.3 + lam4 * q11.3 + lam5 * q19.3 + lam6 * q23.3 + lam7 * q27.3 + lam8 * q28.3 + lam9 * q15.3 219 | # latent common factor variances and covariances 220 | fac2.1 ~~ var1*fac2.1 + fac2.2 + fac2.3 221 | fac2.2 ~~ var1*fac2.2 + fac2.3 222 | fac2.3 ~~ var1*fac2.3 223 | # latent common factor means 224 | fac2.1 ~ int1*1 225 | fac2.2 ~ 1 226 | fac2.3 ~ int1*1 227 | # thresholds 228 | q1.1 | 0*t1 229 | q4.1 | nu2*t1 230 | q10.1 | nu3*t1 231 | q11.1 | nu4*t1 232 | q19.1 | nu5*t1 233 | q23.1 | nu6*t1 234 | q27.1 | nu7*t1 235 | q28.1 | nu8*t1 236 | q15.1 | nu9*t1 237 | q1.2 | 0*t1 238 | q4.2 | nu2*t1 239 | q10.2 | nu3*t1 240 | q11.2 | nu4*t1 241 | q19.2 | nu5*t1 242 | q23.2 | nu6*t1 243 | q27.2 | nu7*t1 244 | q28.2 | nu8*t1 245 | q15.2 | nu9*t1 246 | q1.3 | 0*t1 247 | q4.3 | nu2*t1 248 | q10.3 | nu3*t1 249 | q11.3 | nu4*t1 250 | q19.3 | nu5*t1 251 | q23.3 | nu6*t1 252 | q27.3 | nu7*t1 253 | q28.3 | nu8*t1 254 | q15.3 | nu9*t1 255 | # variances 256 | q1.1 ~~ q1.2 + q1.3 257 | q1.2 ~~ q1.3 258 | q4.1 ~~ q4.2 + q4.3 259 | q4.2 ~~ q4.3 260 | q10.1 ~~ q10.2 + q10.3 261 | q10.2 ~~ q10.3 262 | q19.1 ~~ q19.2 + q19.3 263 | q19.2 ~~ q19.3 264 | q23.1 ~~ q23.2 + q23.3 265 | q23.2 ~~ q23.3 266 | q27.1 ~~ q27.2 + q27.3 267 | q27.2 ~~ q27.3 268 | q28.1 ~~ q28.2 + q28.3 269 | q28.2 ~~ q28.3 270 | q15.1 ~~ q15.2 + q15.3 271 | q15.2 ~~ q15.3 272 | " 273 | fit.strong.eqmeans <- sem(strong.eqmeans.mod, data = pdr.wide, parameterization = "theta", estimator = "wlsmv") 274 | anova(fit.strong, fit.strong.eqmeans) 275 | configural.mod <- " 276 | group: 1 277 | fac2.1 =~ 1 * q1.1 + lam2 * q4.1 + lam3 * q10.1 + lam4 * q11.1 + lam5 * q19.1 + lam6 * q23.1 + lam7 * q27.1 + lam8 * q28.1 + lam9 * q15.1 278 | fac2.2 =~ 1 * q1.2 + lam2 * q4.2 + lam3 * q10.2 + lam4 * q11.2 + lam5 * q19.2 + lam6 * q23.2 + lam7 * q27.2 + lam8 * q28.2 + lam9 * q15.2 279 | fac2.3 =~ 1 * q1.3 + lam2 * q4.3 + lam3 * q10.3 + lam4 * q11.3 + lam5 * q19.3 + lam6 * q23.3 + lam7 * q27.3 + lam8 * q28.3 + lam9 * q15.3 280 | # latent common factor variances and covariances 281 | fac2.1 ~~ fac2.1 + fac2.2 + fac2.3 282 | fac2.2 ~~ fac2.2 + fac2.3 283 | fac2.3 ~~ fac2.3 284 | # latent common factor means 285 | fac2.1 ~ 1 286 | fac2.2 ~ 1 287 | fac2.3 ~ 1 288 | # thresholds 289 | q1.1 | 0*t1 290 | q4.1 | nu2*t1 291 | q10.1 | nu3*t1 292 | q11.1 | nu4*t1 293 | q19.1 | nu5*t1 294 | q23.1 | nu6*t1 295 | q27.1 | nu7*t1 296 | q28.1 | nu8*t1 297 | q15.1 | nu9*t1 298 | q1.2 | 0*t1 299 | q4.2 | nu2*t1 300 | q10.2 | nu3*t1 301 | q11.2 | nu4*t1 302 | q19.2 | nu5*t1 303 | q23.2 | nu6*t1 304 | q27.2 | nu7*t1 305 | q28.2 | nu8*t1 306 | q15.2 | nu9*t1 307 | q1.3 | 0*t1 308 | q4.3 | nu2*t1 309 | q10.3 | nu3*t1 310 | q11.3 | nu4*t1 311 | q19.3 | nu5*t1 312 | q23.3 | nu6*t1 313 | q27.3 | nu7*t1 314 | q28.3 | nu8*t1 315 | q15.3 | nu9*t1 316 | # variances 317 | q1.1 ~~ q1.2 + q1.3 318 | q1.2 ~~ q1.3 319 | q4.1 ~~ q4.2 + q4.3 320 | q4.2 ~~ q4.3 321 | q10.1 ~~ q10.2 + q10.3 322 | q10.2 ~~ q10.3 323 | q19.1 ~~ q19.2 + q19.3 324 | q19.2 ~~ q19.3 325 | q23.1 ~~ q23.2 + q23.3 326 | q23.2 ~~ q23.3 327 | q27.1 ~~ q27.2 + q27.3 328 | q27.2 ~~ q27.3 329 | q28.1 ~~ q28.2 + q28.3 330 | q28.2 ~~ q28.3 331 | q15.1 ~~ q15.2 + q15.3 332 | q15.2 ~~ q15.3 333 | group: 2 334 | fac2.1 =~ 1 * q1.1 + lam2.1 * q4.1 + lam3.1 * q10.1 + lam4.1 * q11.1 + lam5.1 * q19.1 + lam6.1 * q23.1 + lam7.1 * q27.1 + lam8.1 * q28.1 + lam9.1 * q15.1 335 | fac2.2 =~ 1 * q1.2 + lam2.1 * q4.2 + lam3.1 * q10.2 + lam4.1 * q11.2 + lam5.1 * q19.2 + lam6.1 * q23.2 + lam7.1 * q27.2 + lam8.1 * q28.2 + lam9.1 * q15.2 336 | fac2.3 =~ 1 * q1.3 + lam2.1 * q4.3 + lam3.1 * q10.3 + lam4.1 * q11.3 + lam5.1 * q19.3 + lam6.1 * q23.3 + lam7.1 * q27.3 + lam8.1 * q28.3 + lam9.1 * q15.3 337 | # latent common factor variances and covariances 338 | fac2.1 ~~ fac2.1 + fac2.2 + fac2.3 339 | fac2.2 ~~ fac2.2 + fac2.3 340 | fac2.3 ~~ fac2.3 341 | # latent common factor means 342 | fac2.1 ~ 1 343 | fac2.2 ~ 1 344 | fac2.3 ~ 1 345 | # thresholds 346 | q1.1 | 0*t1 347 | q4.1 | nu2.1*t1 348 | q10.1 | nu3.1*t1 349 | q11.1 | nu4.1*t1 350 | q19.1 | nu5.1*t1 351 | q23.1 | nu6.1*t1 352 | q27.1 | nu7.1*t1 353 | q28.1 | nu8.1*t1 354 | q15.1 | nu9.1*t1 355 | q1.2 | 0*t1 356 | q4.2 | nu2.1*t1 357 | q10.2 | nu3.1*t1 358 | q11.2 | nu4.1*t1 359 | q19.2 | nu5.1*t1 360 | q23.2 | nu6.1*t1 361 | q27.2 | nu7.1*t1 362 | q28.2 | nu8.1*t1 363 | q15.2 | nu9.1*t1 364 | q1.3 | 0*t1 365 | q4.3 | nu2.1*t1 366 | q10.3 | nu3.1*t1 367 | q11.3 | nu4.1*t1 368 | q19.3 | nu5.1*t1 369 | q23.3 | nu6.1*t1 370 | q27.3 | nu7.1*t1 371 | q28.3 | nu8.1*t1 372 | q15.3 | nu9.1*t1 373 | # variances 374 | q1.1 ~~ q1.2 + q1.3 375 | q1.2 ~~ q1.3 376 | q4.1 ~~ q4.2 + q4.3 377 | q4.2 ~~ q4.3 378 | q10.1 ~~ q10.2 + q10.3 379 | q10.2 ~~ q10.3 380 | q19.1 ~~ q19.2 + q19.3 381 | q19.2 ~~ q19.3 382 | q23.1 ~~ q23.2 + q23.3 383 | q23.2 ~~ q23.3 384 | q27.1 ~~ q27.2 + q27.3 385 | q27.2 ~~ q27.3 386 | q28.1 ~~ q28.2 + q28.3 387 | q28.2 ~~ q28.3 388 | q15.1 ~~ q15.2 + q15.3 389 | q15.2 ~~ q15.3 390 | " 391 | fit.configural <- sem(configural.mod, data = pdr.wide, group = "Sex", parameterization = "theta", estimator = "wlsmv") 392 | strong.mod <- " 393 | group: 1 394 | fac2.1 =~ 1 * q1.1 + lam2 * q4.1 + lam3 * q10.1 + lam4 * q11.1 + lam5 * q19.1 + lam6 * q23.1 + lam7 * q27.1 + lam8 * q28.1 + lam9 * q15.1 395 | fac2.2 =~ 1 * q1.2 + lam2 * q4.2 + lam3 * q10.2 + lam4 * q11.2 + lam5 * q19.2 + lam6 * q23.2 + lam7 * q27.2 + lam8 * q28.2 + lam9 * q15.2 396 | fac2.3 =~ 1 * q1.3 + lam2 * q4.3 + lam3 * q10.3 + lam4 * q11.3 + lam5 * q19.3 + lam6 * q23.3 + lam7 * q27.3 + lam8 * q28.3 + lam9 * q15.3 397 | # latent common factor variances and covariances 398 | fac2.1 ~~ 1*fac2.1 + fac2.2 + fac2.3 399 | fac2.2 ~~ 1*fac2.2 + fac2.3 400 | fac2.3 ~~ 1*fac2.3 401 | # latent common factor means 402 | fac2.1 ~ 0 403 | fac2.2 ~ 0 404 | fac2.3 ~ 0 405 | # thresholds 406 | q1.1 | 0*t1 407 | q4.1 | nu2*t1 408 | q10.1 | nu3*t1 409 | q11.1 | nu4*t1 410 | q19.1 | nu5*t1 411 | q23.1 | nu6*t1 412 | q27.1 | nu7*t1 413 | q28.1 | nu8*t1 414 | q15.1 | nu9*t1 415 | q1.2 | 0*t1 416 | q4.2 | nu2*t1 417 | q10.2 | nu3*t1 418 | q11.2 | nu4*t1 419 | q19.2 | nu5*t1 420 | q23.2 | nu6*t1 421 | q27.2 | nu7*t1 422 | q28.2 | nu8*t1 423 | q15.2 | nu9*t1 424 | q1.3 | 0*t1 425 | q4.3 | nu2*t1 426 | q10.3 | nu3*t1 427 | q11.3 | nu4*t1 428 | q19.3 | nu5*t1 429 | q23.3 | nu6*t1 430 | q27.3 | nu7*t1 431 | q28.3 | nu8*t1 432 | q15.3 | nu9*t1 433 | # variances 434 | q1.1 ~~ q1.2 + q1.3 435 | q1.2 ~~ q1.3 436 | q4.1 ~~ q4.2 + q4.3 437 | q4.2 ~~ q4.3 438 | q10.1 ~~ q10.2 + q10.3 439 | q10.2 ~~ q10.3 440 | q19.1 ~~ q19.2 + q19.3 441 | q19.2 ~~ q19.3 442 | q23.1 ~~ q23.2 + q23.3 443 | q23.2 ~~ q23.3 444 | q27.1 ~~ q27.2 + q27.3 445 | q27.2 ~~ q27.3 446 | q28.1 ~~ q28.2 + q28.3 447 | q28.2 ~~ q28.3 448 | q15.1 ~~ q15.2 + q15.3 449 | q15.2 ~~ q15.3 450 | group: 2 451 | fac2.1 =~ 1 * q1.1 + lam2 * q4.1 + lam3 * q10.1 + lam4 * q11.1 + lam5 * q19.1 + lam6 * q23.1 + lam7 * q27.1 + lam8 * q28.1 + lam9 * q15.1 452 | fac2.2 =~ 1 * q1.2 + lam2 * q4.2 + lam3 * q10.2 + lam4 * q11.2 + lam5 * q19.2 + lam6 * q23.2 + lam7 * q27.2 + lam8 * q28.2 + lam9 * q15.2 453 | fac2.3 =~ 1 * q1.3 + lam2 * q4.3 + lam3 * q10.3 + lam4 * q11.3 + lam5 * q19.3 + lam6 * q23.3 + lam7 * q27.3 + lam8 * q28.3 + lam9 * q15.3 454 | # latent common factor variances and covariances 455 | fac2.1 ~~ fac2.1 + fac2.2 + fac2.3 456 | fac2.2 ~~ fac2.2 + fac2.3 457 | fac2.3 ~~ fac2.3 458 | # latent common factor means 459 | fac2.1 ~ 1 460 | fac2.2 ~ 1 461 | fac2.3 ~ 1 462 | # thresholds 463 | q1.1 | 0*t1 464 | q4.1 | nu2*t1 465 | q10.1 | nu3*t1 466 | q11.1 | nu4*t1 467 | q19.1 | nu5*t1 468 | q23.1 | nu6*t1 469 | q27.1 | nu7*t1 470 | q28.1 | nu8*t1 471 | q15.1 | nu9*t1 472 | q1.2 | 0*t1 473 | q4.2 | nu2*t1 474 | q10.2 | nu3*t1 475 | q11.2 | nu4*t1 476 | q19.2 | nu5*t1 477 | q23.2 | nu6*t1 478 | q27.2 | nu7*t1 479 | q28.2 | nu8*t1 480 | q15.2 | nu9*t1 481 | q1.3 | 0*t1 482 | q4.3 | nu2*t1 483 | q10.3 | nu3*t1 484 | q11.3 | nu4*t1 485 | q19.3 | nu5*t1 486 | q23.3 | nu6*t1 487 | q27.3 | nu7*t1 488 | q28.3 | nu8*t1 489 | q15.3 | nu9*t1 490 | # variances 491 | q1.1 ~~ q1.2 + q1.3 492 | q1.2 ~~ q1.3 493 | q4.1 ~~ q4.2 + q4.3 494 | q4.2 ~~ q4.3 495 | q10.1 ~~ q10.2 + q10.3 496 | q10.2 ~~ q10.3 497 | q19.1 ~~ q19.2 + q19.3 498 | q19.2 ~~ q19.3 499 | q23.1 ~~ q23.2 + q23.3 500 | q23.2 ~~ q23.3 501 | q27.1 ~~ q27.2 + q27.3 502 | q27.2 ~~ q27.3 503 | q28.1 ~~ q28.2 + q28.3 504 | q28.2 ~~ q28.3 505 | q15.1 ~~ q15.2 + q15.3 506 | q15.2 ~~ q15.3 507 | " 508 | fit.strong <- sem(strong.mod, data = pdr.wide, group = "Sex", parameterization = "theta", estimator = "wlsmv", group.equal = c("thresholds", "loadings")) 509 | anova(fit.configural, fit.strong) 510 | summary(fit.strong, fit.measures = TRUE) 511 | summary(fit.configural, fit.measures = TRUE) 512 | summary(fit.strong, fit.measures = TRUE) 513 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Reproducible analyses with `lavaan` 2 | 3 | ## Purpose: 4 | This repositories hosts `R` scripts to replicate the findings of published articles where the authors share their data (either raw or covariance matrices). 5 | 6 | ## Scripts for the following articles: 7 | - Baldwin, Imel, Braithwaite, & Atkins. (2014). _Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models_. Journal of Consulting and Clinical Psychology. See `baldwin2014-multivariate.R` 8 | - Berry & Willoughby. (2016). _On the Practical Interpretability of Cross-Lagged Panel Models: Rethinking a Developmental Workhorse_. Child Development. See `berry2016-practical.R` 9 | - Bollen & Curran. (2004). _Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions_. Sociological Methods & Research. See `bollen2004-autoregressive.R` 10 | - Curran, Howard, Bainter, Lane, & McGinley.(2014). _The Separation of Between-Person and Within-Person Component of Individual Change Over Time: A Latent Curve Model With Structured Residuals_. Journal of Consulting and Clinical Psychology. See `curran2014-separation.R` 11 | - Curren, Muthen, & Harford. (1998). _The Influence of changes in marital status on development trajectories of alcohol use in young adults_. Journal of Studies on Alcohol. See `curran1998-alcohol_trajectories.R` 12 | - Little, Slegers, & Card. (2006). _A Non-arbitrary Method of Identifying and Scaling Latent Variables in SEM and MACS Models_. Structural Equation Modeling. See `little2006-scaling.R` 13 | - Millsap & Yun-Tein. (2004). _Assessing factorial invariance in ordered-categorical measures_. Multivariate Behavioral Research. See `millsap2004-FIorderedCategorical.R`. 14 | - Raykov, Dimitrov, Marcoulides, Li, & Menold. (2018). _Examining measurement invariance and differential item functioning with discrete latent construct indicators: A note on a multiple testing procedure_. Educational and Psychological Measurement. See `raykov2081-dichotomousMI.R`. 15 | -------------------------------------------------------------------------------- /baldwin2014-multivariate.R: -------------------------------------------------------------------------------- 1 | # ------------------------ 2 | # Analyzing Multiple Outcomes in Clinical Research 3 | # Using Multivariate Multilevel Models 4 | # 5 | # Baldwin, Imel, Braithwaite, Atkins 6 | # 7 | # Journal of Consulting and Clinicial Psychology 2014 8 | # http://dx.doi.org/10.1037/a0035628 9 | # ------------------------ 10 | library(lavaan) 11 | 12 | # Read in data in wide format 13 | baldwin_data <- read.csv("http://supp.apa.org/psycarticles/supplemental/a0035628/appendix_example1_wide.SUPP.FINAL.csv") 14 | # y1: depression 15 | # y2: quality of life 16 | 17 | # need to make even wider for lavaan`` 18 | baldwin_wide <- reshape(baldwin_data, 19 | direction = "wide", 20 | v.names = c("y1", "y2"), 21 | timevar = "time", 22 | idvar = "pid") 23 | colnames(baldwin_wide) <- c("pid", "tx", paste0(rep(c("dep", "qol"), 3), rep(0:2, each = 2))) 24 | 25 | # Table 2: Univariate models ---- 26 | # Depression 27 | dep.mod <- ' 28 | dep.i =~ 1*dep0 + 1*dep1 + 1*dep2 29 | dep.s =~ 0*dep0 + 1*dep1 + 2*dep2 30 | dep.i ~~ dep.i 31 | dep.s ~~ dep.s 32 | dep.i ~~ dep.s 33 | dep.i ~ 1 + tx 34 | dep.s ~ 1 + beta13*tx 35 | 36 | # set residuals to be equal with a measure 37 | dep0 ~~ e1*dep0 38 | dep1 ~~ e1*dep1 39 | dep2 ~~ e1*dep2 40 | ' 41 | dep.fit <- lavaan(dep.mod, baldwin_wide) 42 | summary(dep.fit) 43 | 44 | # Quality of Life 45 | qol.mod <- ' 46 | qol.i =~ 1*qol0 + 1*qol1 + 1*qol2 47 | qol.s =~ 0*qol0 + 1*qol1 + 2*qol2 48 | qol.i ~~ qol.i 49 | qol.s ~~ qol.s 50 | qol.i ~~ qol.s 51 | qol.i ~ 1 + tx 52 | qol.s ~ 1 + beta23*tx 53 | 54 | # set residuals to be equal with a measure 55 | qol0 ~~ e2*qol0 56 | qol1 ~~ e2*qol1 57 | qol2 ~~ e2*qol2 58 | ' 59 | qol.fit <- lavaan(qol.mod, baldwin_wide) 60 | summary(qol.fit) 61 | 62 | # Table 2: Multivariate independent outcomes ---- 63 | ind.mod <- ' 64 | # depression 65 | dep.i =~ 1*dep0 + 1*dep1 + 1*dep2 66 | dep.s =~ 0*dep0 + 1*dep1 + 2*dep2 67 | dep.i ~~ dep.i 68 | dep.s ~~ dep.s 69 | dep.i ~~ dep.s 70 | dep.i ~ 1 + tx 71 | dep.s ~ 1 + tx 72 | 73 | # quality of life 74 | qol.i =~ 1*qol0 + 1*qol1 + 1*qol2 75 | qol.s =~ 0*qol0 + 1*qol1 + 2*qol2 76 | qol.i ~~ qol.i 77 | qol.s ~~ qol.s 78 | qol.i ~~ qol.s 79 | qol.i ~ 1 + tx 80 | qol.s ~ 1 + tx 81 | 82 | # set residuals to be equal with a measure 83 | dep0 ~~ e1*dep0 84 | dep1 ~~ e1*dep1 85 | dep2 ~~ e1*dep2 86 | 87 | qol0 ~~ e2*qol0 88 | qol1 ~~ e2*qol1 89 | qol2 ~~ e2*qol2 90 | ' 91 | ind.fit <- lavaan(ind.mod, baldwin_wide) 92 | summary(ind.fit) 93 | 94 | # Test of different treatement effects based on 95 | # dep.fit and qol.fit 96 | se.z <- sqrt(0.098^2 + 0.108^2) 97 | beta.diff <- (-0.412 - -0.089) 98 | z <- beta.diff/se.z 99 | pnorm(z,lower.tail = T)*2 100 | 101 | # Table 2: Multivariate related outcomes, main model ---- 102 | rel.mod <- ' 103 | # depression 104 | dep.i =~ 1*dep0 + 1*dep1 + 1*dep2 105 | dep.s =~ 0*dep0 + 1*dep1 + 2*dep2 106 | dep.i ~~ u1*dep.i 107 | dep.s ~~ v1*dep.s 108 | dep.i ~~ u1v1*dep.s 109 | dep.i ~ 1 + beta11*tx 110 | dep.s ~ 1 + beta13*tx 111 | 112 | # quality of life 113 | qol.i =~ 1*qol0 + 1*qol1 + 1*qol2 114 | qol.s =~ 0*qol0 + 1*qol1 + 2*qol2 115 | qol.i ~~ u2*qol.i 116 | qol.s ~~ v2*qol.s 117 | qol.i ~~ u2v2*qol.s 118 | qol.i ~ 1 + beta21*tx 119 | qol.s ~ 1 + beta23*tx 120 | 121 | # covariances across outcome 122 | dep.i ~~ u1u2*qol.i 123 | dep.i ~~ u1v2*qol.s 124 | dep.s ~~ v1u2*qol.i 125 | dep.s ~~ v1v2*qol.s 126 | 127 | # set residuals to be equal with a measure 128 | dep0 ~~ e1*dep0 129 | dep1 ~~ e1*dep1 130 | dep2 ~~ e1*dep2 131 | 132 | qol0 ~~ e2*qol0 133 | qol1 ~~ e2*qol1 134 | qol2 ~~ e2*qol2 135 | 136 | # correlated residuals across outcome within timer 137 | dep0 ~~ e3*qol0 138 | dep1 ~~ e3*qol1 139 | dep2 ~~ e3*qol2 140 | 141 | # Test of different trt effects of beta13 and beta23 142 | diff.beta := beta13 - beta23 143 | 144 | # Calculation of various correlations 145 | r.int := u1u2 / (sqrt(u1)*sqrt(u2)) 146 | r.slope := v1v2 / (sqrt(v1)*sqrt(v2)) 147 | r.qoli.deps := v1u2 / (sqrt(u2)*sqrt(v1)) 148 | r.depi.qols := u1v2 / (sqrt(u1)*sqrt(v2)) 149 | ' 150 | rel.fit <- lavaan(rel.mod, baldwin_wide) 151 | summary(rel.fit, fit.measures = T) 152 | anova(ind.fit, rel.fit) 153 | 154 | ## Alternative LRT test fixing trt effects to be identical 155 | ## - fit just to perform LRT 156 | lrt.mod <- ' 157 | # depression 158 | dep.i =~ 1*dep0 + 1*dep1 + 1*dep2 159 | dep.s =~ 0*dep0 + 1*dep1 + 2*dep2 160 | dep.i ~~ u1*dep.i 161 | dep.s ~~ v1*dep.s 162 | dep.i ~~ u1v1*dep.s 163 | dep.i ~ 1 + beta11*tx 164 | dep.s ~ 1 + beta3*tx 165 | 166 | # quality of life 167 | qol.i =~ 1*qol0 + 1*qol1 + 1*qol2 168 | qol.s =~ 0*qol0 + 1*qol1 + 2*qol2 169 | qol.i ~~ u2*qol.i 170 | qol.s ~~ v2*qol.s 171 | qol.i ~~ u2v2*qol.s 172 | qol.i ~ 1 + beta21*tx 173 | qol.s ~ 1 + beta3*tx 174 | 175 | # covariances across outcome 176 | dep.i ~~ u1u2*qol.i 177 | dep.i ~~ u1v2*qol.s 178 | dep.s ~~ v1u2*qol.i 179 | dep.s ~~ v1v2*qol.s 180 | 181 | # set residuals to be equal with a measure 182 | dep0 ~~ e1*dep0 183 | dep1 ~~ e1*dep1 184 | dep2 ~~ e1*dep2 185 | 186 | qol0 ~~ e2*qol0 187 | qol1 ~~ e2*qol1 188 | qol2 ~~ e2*qol2 189 | 190 | # correlated residuals across outcome within timer 191 | dep0 ~~ e3*qol0 192 | dep1 ~~ e3*qol1 193 | dep2 ~~ e3*qol2 194 | ' 195 | lrt.fit <- lavaan(lrt.mod, baldwin_wide) 196 | anova(lrt.fit, rel.fit) -------------------------------------------------------------------------------- /berry2016-practical.R: -------------------------------------------------------------------------------- 1 | # ------------------------ 2 | # On the Practical Interpretability of Cross-Lagged Panel Models: 3 | # Rethinking a Developmental Workhorse 4 | # 5 | # Berry & Willoughby 6 | # 7 | # Child Development 2016 8 | # doi.org/10.1111/cdev.12660 9 | # ------------------------ 10 | library(lavaan) 11 | MEANS <- c(1.73, 1.66, 1.61, 1.36, 0.28, 0.28, 0.28, 0.33) 12 | SDS <- c(0.62, 0.61, 0.59, 0.55, 0.18, 0.17, 0.18, 0.23) 13 | LOWER <- ' 14 | 1.00 15 | 0.58 1.00 16 | 0.61 0.68 1.00 17 | 0.39 0.45 0.51 1.00 18 | 0.30 0.16 0.23 0.09 1.00 19 | 0.30 0.20 0.28 0.12 0.56 1.00 20 | 0.28 0.24 0.28 0.10 0.56 0.55 1.00 21 | 0.15 0.18 0.17 0.08 0.32 0.37 0.36 1.00' 22 | lansford.cor <- getCov(LOWER, names = c(paste0("spank", c(10:12, 15)), paste0("agg", c(10:12, 15)))) 23 | lansford.cov <- (SDS %*% t(SDS)) * lansford.cor 24 | 25 | # Example 1 26 | altsr.mod <- ' 27 | # random intercepts 28 | agg.i =~ 1*agg10 + 1*agg11 + 1*agg12 + 1*agg15 29 | spank.i =~ 1*spank10 + 1*spank11 + 1*spank12 + 1*spank15 30 | agg.i ~~ agg.i 31 | spank.i ~~ spank.i 32 | agg.i ~~ p10*spank.i 33 | agg.i ~ 1 34 | spank.i ~ 1 35 | 36 | # random slopes 37 | agg.s =~ 0*agg10 + 1*agg11 + 2*agg12 + 3*agg15 38 | spank.s =~ 0*spank10 + 1*spank11 + 2*spank12 + 3*spank15 39 | 40 | # constrain no variation in random slopes (Figure 4) 41 | agg.s ~~ 0*agg.s 42 | spank.s ~~ 0*spank.s 43 | agg.s ~~ 0*agg.i 44 | agg.s ~~ 0*spank.i 45 | spank.s ~~ 0*agg.i 46 | spank.s ~~ 0*spank.i 47 | agg.s ~ 1 48 | spank.s ~ 1 49 | 50 | # create structured residuals 51 | e.agg10 =~ 1*agg10 52 | e.agg11 =~ 1*agg11 53 | e.agg12 =~ 1*agg12 54 | e.agg15 =~ 1*agg15 55 | 56 | agg10 ~~ 0*agg10 57 | agg11 ~~ 0*agg11 58 | agg12 ~~ 0*agg12 59 | agg15 ~~ 0*agg15 60 | 61 | e.agg10 ~~ e.agg10 62 | e.agg11 ~~ p1012*e.agg11 63 | e.agg12 ~~ p1012*e.agg12 64 | e.agg15 ~~ p1012*e.agg15 65 | 66 | e.spank10 =~ 1*spank10 67 | e.spank11 =~ 1*spank11 68 | e.spank12 =~ 1*spank12 69 | e.spank15 =~ 1*spank15 70 | 71 | spank10 ~~ 0*spank10 72 | spank11 ~~ 0*spank11 73 | spank12 ~~ 0*spank12 74 | spank15 ~~ 0*spank15 75 | 76 | e.spank10 ~~ e.spank10 77 | e.spank11 ~~ p1011*e.spank11 78 | e.spank12 ~~ p1011*e.spank12 79 | e.spank15 ~~ p1011*e.spank15 80 | 81 | e.spank15 ~ p3*e.spank12 + p2*e.agg12 82 | e.spank12 ~ p3*e.spank11 + p2*e.agg11 83 | e.spank11 ~ p3*e.spank10 + p2*e.agg10 84 | 85 | e.agg15 ~ p1*e.spank12 + p4*e.agg12 86 | e.agg12 ~ p1*e.spank11 + p4*e.agg11 87 | e.agg11 ~ p1*e.spank10 + p4*e.agg10 88 | 89 | e.spank10 ~~ e.agg10 90 | e.spank11 ~~ p1000*e.agg11 91 | e.spank12 ~~ p1000*e.agg12 92 | e.spank15 ~~ p1000*e.agg15 93 | ' 94 | fit.altsr <- lavaan(altsr.mod, sample.mean = MEANS, sample.nobs = 290, sample.cov = lansford.cov) 95 | summary(fit.altsr, fit.measures = T, standardized = T) 96 | -------------------------------------------------------------------------------- /bollen2004-autoregressive.R: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------ 2 | # Autoregressive Latent Trajectory (ALT) Models A Synthesis of Two Traditions 3 | # 4 | # Bollen & Curran 5 | # 6 | # Sociological Methods & Research 2004 7 | # doi.org/10.1177/0049124103260222 8 | # ------------------------------------------------ 9 | library(lavaan) 10 | n <- 500 11 | lower <- ' 12 | .619 13 | .453 .595 14 | .438 .438 .587 15 | .422 .430 .438 .595 16 | .406 .422 .438 .453 .619 17 | ' 18 | S <- getCov(lower, names = paste0("t", 1:5)) 19 | 20 | ## Latent trajectory model, i.e., latent growth curve w/ random intercept & random slope ---- 21 | lt.model <- ' 22 | i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4 + 1*t5 23 | s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4 + 4*t5 24 | 25 | # estimate the means 26 | i ~ 1 27 | s ~ 1 28 | 29 | # estimate the variances/covariances 30 | i ~~ i 31 | s ~~ s 32 | i ~~ s 33 | 34 | # estimate the residual variances 35 | t1 ~~ t1 36 | t2 ~~ t2 37 | t3 ~~ t3 38 | t4 ~~ t4 39 | t5 ~~ t5 40 | ' 41 | lt.fit <- lavaan(lt.model, sample.cov = S, sample.nobs = n) 42 | summary(lt.fit, fit.measures = T) 43 | round(lt.fit@implied$cov[[1]], 3) 44 | 45 | ## Autoregressive model ---- 46 | ar.model <- ' 47 | t2 ~ t1 48 | t3 ~ t2 49 | t4 ~ t3 50 | t5 ~ t4 51 | t2 ~~ t2 52 | t3 ~~ t3 53 | t4 ~~ t4 54 | t5 ~~ t5 55 | ' 56 | ar.fit <- lavaan(ar.model, sample.cov = S, sample.nobs = n) 57 | summary(ar.fit, fit.measures = T) 58 | round(ar.fit@implied$cov[[1]], 3) 59 | 60 | ## ALT model --- 61 | # Predetermined, i.e., T1 exogenous ---- 62 | alt.preT1.model <- ' 63 | i =~ 1*t2 + 1*t3 + 1*t4 + 1*t5 64 | s =~ 1*t2 + 2*t3 + 3*t4 + 4*t5 65 | 66 | # estimate the means 67 | i ~ 1 68 | s ~ 1 69 | t1 ~ 1 70 | 71 | # estimate the variances/covariances 72 | i ~~ i 73 | s ~~ s 74 | i ~~ s 75 | t1 ~~ t1 76 | t1 ~~ i 77 | t1 ~~ s 78 | 79 | # estimate the residual variances 80 | t2 ~~ e2*t2 81 | t3 ~~ e3*t3 82 | t4 ~~ e4*t4 83 | t5 ~~ e5*t5 84 | 85 | # autoregressive components 86 | t2 ~ p2*t1 87 | t3 ~ p3*t2 88 | t4 ~ p4*t3 89 | t5 ~ p5*t4 90 | ' 91 | alt.preT1.fit <- lavaan(alt.preT1.model, sample.cov = S, sample.nobs = n) 92 | 93 | # Replicates Table 2 94 | summary(alt.preT1.fit, fit.measures = T) 95 | round(inspect(alt.preT1.fit, "rsquare"), 2) 96 | 97 | # Now, fit the latent trajectory model as a reduced ALT model 98 | # again with T1 predetermined 99 | alt.lt.model <- ' 100 | i =~ 1*t2 + 1*t3 + 1*t4 + 1*t5 101 | s =~ 1*t2 + 2*t3 + 3*t4 + 4*t5 102 | 103 | # estimate the means 104 | i ~ 1 105 | s ~ 1 106 | t1 ~ 1 107 | 108 | # estimate the variances/covariances 109 | i ~~ i 110 | s ~~ s 111 | i ~~ s 112 | t1 ~~ t1 113 | t1 ~~ i 114 | t1 ~~ s 115 | 116 | # estimate the residual variances 117 | t2 ~~ e2*t2 118 | t3 ~~ e3*t3 119 | t4 ~~ e4*t4 120 | t5 ~~ e5*t5 121 | 122 | # autoregressive components 123 | t2 ~ 0*t1 124 | t3 ~ 0*t2 125 | t4 ~ 0*t3 126 | t5 ~ 0*t4 127 | ' 128 | alt.lt.fit <- lavaan(alt.lt.model, sample.cov = S, sample.nobs = n) 129 | 130 | # LRT, dfs are slightly different but statistics are identical 131 | # to that present on p. 35 132 | anova(alt.preT1.fit, alt.lt.fit) 133 | 134 | # ALT model with T1 as endogenous ---- 135 | alt.model <- ' 136 | i =~ 1*t1 + 1*t2 + 1*t3 + 1*t4 + 1*t5 137 | s =~ 0*t1 + 1*t2 + 2*t3 + 3*t4 + 4*t5 138 | 139 | # estimate the means 140 | i ~ 1 141 | s ~ 1 142 | 143 | # estimate the variances/covariances 144 | i ~~ i 145 | s ~~ s 146 | i ~~ s 147 | 148 | # estimate the residual variances 149 | t1 ~~ t1 150 | t2 ~~ t2 151 | t3 ~~ t3 152 | t4 ~~ t4 153 | t5 ~~ t5 154 | 155 | # autoregressive components 156 | t2 ~ t1 157 | t3 ~ t2 158 | t4 ~ t3 159 | t5 ~ t4 160 | ' 161 | alt.fit <- lavaan(alt.model, sample.cov = S, sample.nobs = n) 162 | summary(alt.fit, fit.measures = T) 163 | round(alt.model@implied$cov[[1]], 3) 164 | 165 | ## ALT with structured residuals example 166 | # From Berry & Willoughby (2016) 167 | MEANS <- c(1.73, 1.66, 1.61, 1.36, 0.28, 0.28, 0.28, 0.33) 168 | SDS <- c(0.62, 0.61, 0.59, 0.55, 0.18, 0.17, 0.18, 0.23) 169 | LOWER <- ' 170 | 1.00 171 | 0.58 1.00 172 | 0.61 0.68 1.00 173 | 0.39 0.45 0.51 1.00 174 | 0.30 0.16 0.23 0.09 1.00 175 | 0.30 0.20 0.28 0.12 0.56 1.00 176 | 0.28 0.24 0.28 0.10 0.56 0.55 1.00 177 | 0.15 0.18 0.17 0.08 0.32 0.37 0.36 1.00' 178 | lansford.cor <- getCov(LOWER, names = c(paste0("spank", c(10:12, 15)), paste0("agg", c(10:12, 15)))) 179 | lansford.cov <- (SDS %*% t(SDS)) * lansford.cor 180 | 181 | ## ALT-SR model 182 | altsr.mod <- ' 183 | # random intercepts 184 | agg.i =~ 1*agg10 + 1*agg11 + 1*agg12 + 1*agg15 185 | spank.i =~ 1*spank10 + 1*spank11 + 1*spank12 + 1*spank15 186 | agg.i ~~ agg.i 187 | spank.i ~~ spank.i 188 | agg.i ~~ p10*spank.i 189 | agg.i ~ 1 190 | spank.i ~ 1 191 | 192 | # random slopes 193 | agg.s =~ 0*agg10 + 1*agg11 + 2*agg12 + 3*agg15 194 | spank.s =~ 0*spank10 + 1*spank11 + 2*spank12 + 3*spank15 195 | 196 | # constrain no variation in random slopes (Figure 4) 197 | agg.s ~~ 0*agg.s 198 | spank.s ~~ 0*spank.s 199 | agg.s ~~ 0*agg.i 200 | agg.s ~~ 0*spank.i 201 | spank.s ~~ 0*agg.i 202 | spank.s ~~ 0*spank.i 203 | 204 | agg.s ~ p16*1 205 | spank.s ~ p11*1 206 | 207 | # create structured residuals 208 | e.agg10 =~ 1*agg10 209 | e.agg11 =~ 1*agg11 210 | e.agg12 =~ 1*agg12 211 | e.agg15 =~ 1*agg15 212 | 213 | agg10 ~~ 0*agg10 214 | agg11 ~~ 0*agg11 215 | agg12 ~~ 0*agg12 216 | agg15 ~~ 0*agg15 217 | 218 | e.agg10 ~~ e.agg10 219 | e.agg11 ~~ p1012*e.agg11 220 | e.agg12 ~~ p1012*e.agg12 221 | e.agg15 ~~ p1012*e.agg15 222 | 223 | e.spank10 =~ 1*spank10 224 | e.spank11 =~ 1*spank11 225 | e.spank12 =~ 1*spank12 226 | e.spank15 =~ 1*spank15 227 | 228 | spank10 ~~ 0*spank10 229 | spank11 ~~ 0*spank11 230 | spank12 ~~ 0*spank12 231 | spank15 ~~ 0*spank15 232 | 233 | e.spank10 ~~ e.spank10 234 | e.spank11 ~~ p1011*e.spank11 235 | e.spank12 ~~ p1011*e.spank12 236 | e.spank15 ~~ p1011*e.spank15 237 | 238 | e.spank15 ~ p4*e.spank12 + p2*e.agg12 239 | e.spank12 ~ p4*e.spank11 + p2*e.agg11 240 | e.spank11 ~ p4*e.spank10 + p2*e.agg10 241 | 242 | e.agg15 ~ p1*e.spank12 + p3*e.agg12 243 | e.agg12 ~ p1*e.spank11 + p3*e.agg11 244 | e.agg11 ~ p1*e.spank10 + p3*e.agg10 245 | 246 | e.spank10 ~~ e.agg10 247 | e.spank11 ~~ p1000*e.agg11 248 | e.spank12 ~~ p1000*e.agg12 249 | e.spank15 ~~ p1000*e.agg15 250 | ' 251 | fit.altsr <- lavaan(altsr.mod, sample.mean = MEANS, sample.nobs = 290, sample.cov = lansford.cov) 252 | summary(fit.altsr, fit.measures = T, standardized = T) 253 | -------------------------------------------------------------------------------- /curran1998-alcohol_trajectories.R: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------ 2 | # The Influence of changes in marital status on 3 | # development trajectories of alcohol use 4 | # in young adults. 5 | # 6 | # Curran, Muthen, & Harford 7 | # 8 | # Journal of Studies on Alcohol 1998 9 | # ------------------------------------------------ 10 | 11 | library("lavaan") 12 | MEANS <- c(0.92, 0.76, 0.73, 0.62, 0.08, 0.08, 0.08, 0.07, 22.69, 0.54, 0.30, 0.14, 12.72) 13 | SDS <- c(1.68, 1.35, 1.26, 1.08, 0.27, 0.27, 0.27, 0.26, 1.38, 0.49, 0.46, 0.35, 2.02) 14 | LOWER <- ' 15 | 1.00 16 | .494 1.00 17 | .440 .519 1.00 18 | .382 .471 .510 1.00 19 | -.074 -.068 -.062 -.035 1.00 20 | -.023 -.048 -.057 -.055 -.086 1.00 21 | .009 -.003 -.036 -.039 -.085 -.083 1.00 22 | .043 .025 .011 -.022 -.081 -.080 -.079 1.00 23 | .032 .020 -.005 .010 .050 .028 .007 .028 1.00 24 | .231, .238 .252 .264 -.048 -.005 -.028 -.003 .011 1.00 25 | -.142 -.146 -.120 -.118 -.085 -.080 -.067 -.071 -.014 -.013 1.00 26 | -.025 -.014 -.028 -.001 .012 .019 -.021 -.013 -.023 .040 -.265 1.00 27 | .012 -.005 -.021 -.018 .006 .006 .009 .049 .222 -.110 -.149 -.125 1.00' 28 | alcuse.cor <- getCov(LOWER, names = c(paste0("alc", 1:4), paste0("mar", 1:4), "age", "gender", "black", "hispanic", "education")) 29 | alcuse.cov <- (SDS %*% t(SDS)) * alcuse.cor 30 | 31 | # Sample sizes 32 | total.n <- 4052 33 | female.n <- 1869 34 | male.n <- 2183 35 | black.n <- 1232 36 | hispanic.n <- 562 37 | white.n <- 2258 38 | blackFemale.n <- 581 39 | whiteFemale.n <- 1057 40 | blackMale.n <- 651 41 | whiteMale.n <- 1201 42 | 43 | model.1 <- ' 44 | # ---------------- 45 | # latent factors 46 | # ---------------- 47 | # alcohol 48 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 49 | alc.s =~ 0*alc1 + 1*alc2 + p3*alc3 + p4*alc4 50 | 51 | # p3 is freely estimated to be 1.54 52 | # and p4 is freely estimated to be 2.46 53 | 54 | alc.i ~~ alc.s 55 | alc.i ~~ alc.i 56 | alc.s ~~ alc.s 57 | 58 | alc.i ~ 1 59 | alc.s ~ 1 60 | 61 | alc1 ~~ alc1 62 | alc2 ~~ alc2 63 | alc3 ~~ alc3 64 | alc4 ~~ alc4 65 | ' 66 | fit.1 <- lavaan(model.1, sample.mean = MEANS, sample.nobs = total.n, sample.cov = alcuse.cov, start = "simple") # won't converge 67 | summary(fit.1, fit.measures = T) 68 | 69 | model.2 <- ' 70 | # ---------------- 71 | # latent factors 72 | # ---------------- 73 | # alcohol 74 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 75 | alc.s =~ 0*alc1 + 1*alc2 + p3*alc3 + p4*alc4 76 | 77 | alc.i ~~ alc.s 78 | alc.i ~~ alc.i 79 | alc.s ~~ alc.s 80 | 81 | alc.i ~ 1 + age + gender + education + black + hispanic 82 | alc.s ~ 1 + age + gender + education + black + hispanic 83 | 84 | alc1 ~~ alc1 85 | alc2 ~~ alc2 86 | alc3 ~~ alc3 87 | alc4 ~~ alc4 88 | 89 | lmar1 =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 90 | lmar2 =~ 1*alc2 + 1*alc3 + 1*alc4 91 | lmar3 =~ 1*alc3 + 1*alc4 92 | lmar4 =~ 1*alc4 93 | 94 | lmar1 ~ mar1 95 | lmar2 ~ mar2 96 | lmar3 ~ mar3 97 | lmar4 ~ mar4 98 | ' 99 | 100 | fit.2 <- lavaan(model.2, sample.mean = MEANS, sample.nobs = total.n, sample.cov = alcuse.cov) 101 | summary(fit.2, standardized = T, fit.measures = T) 102 | 103 | model.2b <- ' 104 | # ---------------- 105 | # latent factors 106 | # ---------------- 107 | # alcohol 108 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 109 | alc.s =~ 0*alc1 + 1*alc2 + p3*alc3 + p4*alc4 110 | 111 | alc.i ~~ alc.s 112 | alc.i ~~ alc.i 113 | alc.s ~~ alc.s 114 | 115 | alc.i ~ 1 + age + gender + education + black + hispanic 116 | alc.s ~ 1 + age + gender + education + black + hispanic 117 | 118 | alc1 ~~ alc1 119 | alc2 ~~ alc2 120 | alc3 ~~ alc3 121 | alc4 ~~ alc4 122 | 123 | lmar1 =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 124 | lmar2 =~ 1*alc2 + 1*alc3 + 1*alc4 125 | lmar3 =~ 1*alc3 + 1*alc4 126 | lmar4 =~ 1*alc4 127 | 128 | lmar1 ~ mar1 129 | lmar2 ~ mar2 130 | lmar3 ~ mar3 131 | lmar4 ~ mar4 132 | 133 | # authors says no effect, these are significant here 134 | alc1 ~ lmar2 135 | alc2 ~ lmar3 136 | alc3 ~ lmar4 137 | ' 138 | 139 | fit.2b <- lavaan(model.2b, sample.mean = MEANS, sample.nobs = total.n, sample.cov = alcuse.cov) 140 | summary(fit.2b, standardized = T, fit.measures = T) 141 | # actually i find there is an effect ... 142 | 143 | # Can't replicate his multi-group model because the data are not on the web. 144 | 145 | 146 | -------------------------------------------------------------------------------- /curran2014-separation.R: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------ 2 | # The Separation of Between-Person and Within-Person Components 3 | # of Individual Change Over Time: A Latent Curve Model 4 | # With Structured Residuals 5 | # 6 | # Curran, Howard, Bainter, Lane, McGinley 7 | # 8 | # Journal of Consulting and Clinical Psychology 2014 9 | # doi.org/10.1037/a0035297 10 | # ------------------------------------------------ 11 | 12 | # read in the data 13 | lcmsr.sim <- read.table("data/currandemo.dat", col.names = c("id", "gen", "trt", paste0("alc", 1:5), paste0("dep", 1:5))) 14 | 15 | # load necessary libraries 16 | # install.packages("lavaan") # install lavaan if you haven't. 17 | library(lavaan) 18 | 19 | # ------------------------------- 20 | # univariate unconditional model for alcohol 21 | # ------------------------------- 22 | 23 | # model 1 24 | alc.mod1 <- ' 25 | # ALCOHOL # 26 | # random intercept 27 | alc =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 28 | alc ~ 1 29 | alc ~~ alc 30 | 31 | # create structured residuals 32 | alc1 ~~ 0*alc1 33 | alc2 ~~ 0*alc2 34 | alc3 ~~ 0*alc3 35 | alc4 ~~ 0*alc4 36 | alc5 ~~ 0*alc5 37 | 38 | salc1 =~ 1*alc1 39 | salc2 =~ 1*alc2 40 | salc3 =~ 1*alc3 41 | salc4 =~ 1*alc4 42 | salc5 =~ 1*alc5 43 | 44 | salc1 ~ 0 45 | salc2 ~ 0 46 | salc3 ~ 0 47 | salc4 ~ 0 48 | salc5 ~ 0 49 | 50 | salc1 ~~ salc1 51 | salc2 ~~ salc2 52 | salc3 ~~ salc3 53 | salc4 ~~ salc4 54 | salc5 ~~ salc5 55 | ' 56 | 57 | alc.fit1 <- lavaan(alc.mod1, lcmsr.sim) 58 | # summary(alc.fit1, fit.measures = T) 59 | 60 | # model 2 61 | alc.mod2 <- ' 62 | # ALCOHOL # 63 | # random intercept 64 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 65 | alc.i ~ 1 66 | alc.i ~~ alc.i 67 | 68 | # random slope 69 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 70 | alc.s ~ 1 71 | alc.s ~~ alc.s 72 | alc.i ~~ alc.s 73 | 74 | # create structured residuals 75 | alc1 ~~ 0*alc1 76 | alc2 ~~ 0*alc2 77 | alc3 ~~ 0*alc3 78 | alc4 ~~ 0*alc4 79 | alc5 ~~ 0*alc5 80 | 81 | salc1 =~ 1*alc1 82 | salc2 =~ 1*alc2 83 | salc3 =~ 1*alc3 84 | salc4 =~ 1*alc4 85 | salc5 =~ 1*alc5 86 | 87 | salc1 ~ 0 88 | salc2 ~ 0 89 | salc3 ~ 0 90 | salc4 ~ 0 91 | salc5 ~ 0 92 | 93 | salc1 ~~ salc1 94 | salc2 ~~ salc2 95 | salc3 ~~ salc3 96 | salc4 ~~ salc4 97 | salc5 ~~ salc5 98 | ' 99 | 100 | alc.fit2 <- lavaan(alc.mod2, lcmsr.sim) 101 | # summary(alc.fit2, fit.measures = T) 102 | # print(anova(alc.fit1, alc.fit2)) 103 | 104 | alc.mod3 <- ' 105 | # ALCOHOL # 106 | # random intercept 107 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 108 | alc.i ~ 1 109 | alc.i ~~ alc.i 110 | 111 | # random slope 112 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 113 | alc.s ~ 1 114 | alc.s ~~ alc.s 115 | alc.i ~~ alc.s 116 | 117 | # create structured residuals 118 | alc1 ~~ 0*alc1 119 | alc2 ~~ 0*alc2 120 | alc3 ~~ 0*alc3 121 | alc4 ~~ 0*alc4 122 | alc5 ~~ 0*alc5 123 | 124 | salc1 =~ 1*alc1 125 | salc2 =~ 1*alc2 126 | salc3 =~ 1*alc3 127 | salc4 =~ 1*alc4 128 | salc5 =~ 1*alc5 129 | 130 | salc1 ~ 0 131 | salc2 ~ 0 132 | salc3 ~ 0 133 | salc4 ~ 0 134 | salc5 ~ 0 135 | 136 | salc1 ~~ salc1 137 | salc2 ~~ salc2 138 | salc3 ~~ salc3 139 | salc4 ~~ salc4 140 | salc5 ~~ salc5 141 | 142 | # add auto-regressive paths 143 | salc2 ~ pyy*salc1 144 | salc3 ~ pyy*salc2 145 | salc4 ~ pyy*salc3 146 | salc5 ~ pyy*salc4 147 | ' 148 | 149 | alc.fit3 <- lavaan(alc.mod3, lcmsr.sim) 150 | # summary(alc.fit3, fit.measures = T) 151 | # print(anova(alc.fit3, alc.fit2)) 152 | 153 | # ------------------------------- 154 | # univariate unconditional model for depression 155 | # ------------------------------- 156 | 157 | # model 1 158 | dep.mod1 <- ' 159 | # DEPRESSION # 160 | # random intercept 161 | dep =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 162 | dep ~ 1 163 | dep ~~ dep 164 | 165 | # create structured residuals 166 | dep1 ~~ 0*dep1 167 | dep2 ~~ 0*dep2 168 | dep3 ~~ 0*dep3 169 | dep4 ~~ 0*dep4 170 | dep5 ~~ 0*dep5 171 | 172 | sdep1 =~ 1*dep1 173 | sdep2 =~ 1*dep2 174 | sdep3 =~ 1*dep3 175 | sdep4 =~ 1*dep4 176 | sdep5 =~ 1*dep5 177 | 178 | sdep1 ~ 0 179 | sdep2 ~ 0 180 | sdep3 ~ 0 181 | sdep4 ~ 0 182 | sdep5 ~ 0 183 | 184 | sdep1 ~~ sdep1 185 | sdep2 ~~ sdep2 186 | sdep3 ~~ sdep3 187 | sdep4 ~~ sdep4 188 | sdep5 ~~ sdep5 189 | ' 190 | 191 | dep.fit1 <- lavaan(dep.mod1, lcmsr.sim) 192 | # summary(dep.fit1, fit.measures = T) 193 | 194 | # model 2 195 | dep.mod2 <- ' 196 | # DEPRESSION # 197 | # random intercept 198 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 199 | dep.i ~ 1 200 | dep.i ~~ dep.i 201 | 202 | # random slope 203 | dep.s =~ 0*dep1 + 1*dep2 + 2*dep3 + 3*dep4 + 4*dep5 204 | dep.s ~ 1 205 | dep.s ~~ dep.s 206 | dep.s ~~ dep.i 207 | 208 | # create structured residuals 209 | dep1 ~~ 0*dep1 210 | dep2 ~~ 0*dep2 211 | dep3 ~~ 0*dep3 212 | dep4 ~~ 0*dep4 213 | dep5 ~~ 0*dep5 214 | 215 | sdep1 =~ 1*dep1 216 | sdep2 =~ 1*dep2 217 | sdep3 =~ 1*dep3 218 | sdep4 =~ 1*dep4 219 | sdep5 =~ 1*dep5 220 | 221 | sdep1 ~ 0 222 | sdep2 ~ 0 223 | sdep3 ~ 0 224 | sdep4 ~ 0 225 | sdep5 ~ 0 226 | 227 | sdep1 ~~ sdep1 228 | sdep2 ~~ sdep2 229 | sdep3 ~~ sdep3 230 | sdep4 ~~ sdep4 231 | sdep5 ~~ sdep5 232 | ' 233 | 234 | dep.fit2 <- lavaan(dep.mod2, lcmsr.sim) 235 | # summary(dep.fit2, fit.measures = T) 236 | # print(anova(dep.fit1, dep.fit2)) 237 | 238 | # model 3 239 | dep.mod3 <- ' 240 | # DEPRESSION # 241 | # random intercept 242 | dep =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 243 | dep ~ 1 244 | dep ~~ dep 245 | 246 | # create structured residuals 247 | dep1 ~~ 0*dep1 248 | dep2 ~~ 0*dep2 249 | dep3 ~~ 0*dep3 250 | dep4 ~~ 0*dep4 251 | dep5 ~~ 0*dep5 252 | 253 | sdep1 =~ 1*dep1 254 | sdep2 =~ 1*dep2 255 | sdep3 =~ 1*dep3 256 | sdep4 =~ 1*dep4 257 | sdep5 =~ 1*dep5 258 | 259 | sdep1 ~ 0 260 | sdep2 ~ 0 261 | sdep3 ~ 0 262 | sdep4 ~ 0 263 | sdep5 ~ 0 264 | 265 | sdep1 ~~ sdep1 266 | sdep2 ~~ sdep2 267 | sdep3 ~~ sdep3 268 | sdep4 ~~ sdep4 269 | sdep5 ~~ sdep5 270 | 271 | # add auto-regressive paths 272 | sdep2 ~ pzz*sdep1 273 | sdep3 ~ pzz*sdep2 274 | sdep4 ~ pzz*sdep3 275 | sdep5 ~ pzz*sdep4 276 | ' 277 | 278 | dep.fit3 <- lavaan(dep.mod3, lcmsr.sim) 279 | # summary(dep.fit3, fit.measures = T) 280 | print(anova(dep.fit1, dep.fit3)) 281 | 282 | # ------------------------------- 283 | # bivariate unconditional model for alcohol & depression 284 | # ------------------------------- 285 | 286 | # model 1 287 | ad.mod1 <- ' 288 | # --------------------------- 289 | # latent factors 290 | # --------------------------- 291 | # ALCOHOL 292 | # random intercept 293 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 294 | alc.i ~ 1 295 | alc.i ~~ alc.i 296 | 297 | # random slope 298 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 299 | alc.s ~ 1 300 | alc.s ~~ alc.s 301 | alc.i ~~ alc.s 302 | 303 | # DEPRESSION 304 | # random intercept 305 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 306 | dep.i ~ 1 307 | dep.i ~~ dep.i 308 | dep.i ~~ alc.i 309 | dep.i ~~ alc.s 310 | 311 | # --------------------------- 312 | # create structured residuals 313 | # --------------------------- 314 | # ALCOHOL 315 | alc1 ~~ 0*alc1 316 | alc2 ~~ 0*alc2 317 | alc3 ~~ 0*alc3 318 | alc4 ~~ 0*alc4 319 | alc5 ~~ 0*alc5 320 | 321 | salc1 =~ 1*alc1 322 | salc2 =~ 1*alc2 323 | salc3 =~ 1*alc3 324 | salc4 =~ 1*alc4 325 | salc5 =~ 1*alc5 326 | 327 | salc1 ~ 0 328 | salc2 ~ 0 329 | salc3 ~ 0 330 | salc4 ~ 0 331 | salc5 ~ 0 332 | 333 | salc1 ~~ salc1 334 | salc2 ~~ salc2 335 | salc3 ~~ salc3 336 | salc4 ~~ salc4 337 | salc5 ~~ salc5 338 | 339 | # DEPRESSION 340 | dep1 ~~ 0*dep1 341 | dep2 ~~ 0*dep2 342 | dep3 ~~ 0*dep3 343 | dep4 ~~ 0*dep4 344 | dep5 ~~ 0*dep5 345 | 346 | sdep1 =~ 1*dep1 347 | sdep2 =~ 1*dep2 348 | sdep3 =~ 1*dep3 349 | sdep4 =~ 1*dep4 350 | sdep5 =~ 1*dep5 351 | 352 | sdep1 ~ 0 353 | sdep2 ~ 0 354 | sdep3 ~ 0 355 | sdep4 ~ 0 356 | sdep5 ~ 0 357 | 358 | sdep1 ~~ sdep1 359 | sdep2 ~~ sdep2 360 | sdep3 ~~ sdep3 361 | sdep4 ~~ sdep4 362 | sdep5 ~~ sdep5 363 | 364 | salc1 ~~ sdep1 365 | salc2 ~~ vzy*sdep2 366 | salc3 ~~ vzy*sdep3 367 | salc4 ~~ vzy*sdep4 368 | salc5 ~~ vzy*sdep5 369 | 370 | # --------------------------- 371 | # residual regressions 372 | # --------------------------- 373 | # ALCOHOL 374 | salc2 ~ pyy*salc1 375 | salc3 ~ pyy*salc2 376 | salc4 ~ pyy*salc3 377 | salc5 ~ pyy*salc4 378 | 379 | # DEPRESSION 380 | sdep2 ~ pzz*sdep1 381 | sdep3 ~ pzz*sdep2 382 | sdep4 ~ pzz*sdep3 383 | sdep5 ~ pzz*sdep4 384 | ' 385 | ad.fit1 <- lavaan(ad.mod1, lcmsr.sim) 386 | summary(ad.fit1, fit.measures = T) 387 | 388 | # model 2 389 | ad.mod2 <- ' 390 | # --------------------------- 391 | # latent factors 392 | # --------------------------- 393 | # ALCOHOL 394 | # random intercept 395 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 396 | alc.i ~ 1 397 | alc.i ~~ alc.i 398 | 399 | # random slope 400 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 401 | alc.s ~ 1 402 | alc.s ~~ alc.s 403 | alc.i ~~ alc.s 404 | 405 | # DEPRESSION 406 | # random intercept 407 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 408 | dep.i ~ 1 409 | dep.i ~~ dep.i 410 | dep.i ~~ alc.i 411 | dep.i ~~ alc.s 412 | 413 | # --------------------------- 414 | # create structured residuals 415 | # --------------------------- 416 | # ALCOHOL 417 | alc1 ~~ 0*alc1 418 | alc2 ~~ 0*alc2 419 | alc3 ~~ 0*alc3 420 | alc4 ~~ 0*alc4 421 | alc5 ~~ 0*alc5 422 | 423 | salc1 =~ 1*alc1 424 | salc2 =~ 1*alc2 425 | salc3 =~ 1*alc3 426 | salc4 =~ 1*alc4 427 | salc5 =~ 1*alc5 428 | 429 | salc1 ~ 0 430 | salc2 ~ 0 431 | salc3 ~ 0 432 | salc4 ~ 0 433 | salc5 ~ 0 434 | 435 | salc1 ~~ salc1 436 | salc2 ~~ salc2 437 | salc3 ~~ salc3 438 | salc4 ~~ salc4 439 | salc5 ~~ salc5 440 | 441 | # DEPRESSION 442 | dep1 ~~ 0*dep1 443 | dep2 ~~ 0*dep2 444 | dep3 ~~ 0*dep3 445 | dep4 ~~ 0*dep4 446 | dep5 ~~ 0*dep5 447 | 448 | sdep1 =~ 1*dep1 449 | sdep2 =~ 1*dep2 450 | sdep3 =~ 1*dep3 451 | sdep4 =~ 1*dep4 452 | sdep5 =~ 1*dep5 453 | 454 | sdep1 ~ 0 455 | sdep2 ~ 0 456 | sdep3 ~ 0 457 | sdep4 ~ 0 458 | sdep5 ~ 0 459 | 460 | sdep1 ~~ sdep1 461 | sdep2 ~~ sdep2 462 | sdep3 ~~ sdep3 463 | sdep4 ~~ sdep4 464 | sdep5 ~~ sdep5 465 | 466 | salc1 ~~ sdep1 467 | salc2 ~~ p1*sdep2 468 | salc3 ~~ p1*sdep3 469 | salc4 ~~ p1*sdep4 470 | salc5 ~~ p1*sdep5 471 | 472 | # --------------------------- 473 | # residual regressions 474 | # --------------------------- 475 | # ALCOHOL 476 | salc2 ~ p2*salc1 + sdep1 477 | salc3 ~ p2*salc2 + sdep2 478 | salc4 ~ p2*salc3 + sdep3 479 | salc5 ~ p2*salc4 + sdep4 480 | 481 | # DEPRESSION 482 | sdep2 ~ p3*sdep1 483 | sdep3 ~ p3*sdep2 484 | sdep4 ~ p3*sdep3 485 | sdep5 ~ p3*sdep4 486 | ' 487 | ad.fit2 <- lavaan(ad.mod2, lcmsr.sim) 488 | # summary(ad.fit2, fit.measures = T) 489 | # print(anova(ad.fit1, ad.fit2)) 490 | 491 | # model 3 492 | ad.mod3 <- ' 493 | # --------------------------- 494 | # latent factors 495 | # --------------------------- 496 | # ALCOHOL 497 | # random intercept 498 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 499 | alc.i ~ 1 500 | alc.i ~~ alc.i 501 | 502 | # random slope 503 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 504 | alc.s ~ 1 505 | alc.s ~~ alc.s 506 | alc.i ~~ alc.s 507 | 508 | # DEPRESSION 509 | # random intercept 510 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 511 | dep.i ~ 1 512 | dep.i ~~ dep.i 513 | dep.i ~~ alc.i 514 | dep.i ~~ alc.s 515 | 516 | # --------------------------- 517 | # create structured residuals 518 | # --------------------------- 519 | # ALCOHOL 520 | alc1 ~~ 0*alc1 521 | alc2 ~~ 0*alc2 522 | alc3 ~~ 0*alc3 523 | alc4 ~~ 0*alc4 524 | alc5 ~~ 0*alc5 525 | 526 | salc1 =~ 1*alc1 527 | salc2 =~ 1*alc2 528 | salc3 =~ 1*alc3 529 | salc4 =~ 1*alc4 530 | salc5 =~ 1*alc5 531 | 532 | salc1 ~ 0 533 | salc2 ~ 0 534 | salc3 ~ 0 535 | salc4 ~ 0 536 | salc5 ~ 0 537 | 538 | salc1 ~~ salc1 539 | salc2 ~~ salc2 540 | salc3 ~~ salc3 541 | salc4 ~~ salc4 542 | salc5 ~~ salc5 543 | 544 | # DEPRESSION 545 | dep1 ~~ 0*dep1 546 | dep2 ~~ 0*dep2 547 | dep3 ~~ 0*dep3 548 | dep4 ~~ 0*dep4 549 | dep5 ~~ 0*dep5 550 | 551 | sdep1 =~ 1*dep1 552 | sdep2 =~ 1*dep2 553 | sdep3 =~ 1*dep3 554 | sdep4 =~ 1*dep4 555 | sdep5 =~ 1*dep5 556 | 557 | sdep1 ~ 0 558 | sdep2 ~ 0 559 | sdep3 ~ 0 560 | sdep4 ~ 0 561 | sdep5 ~ 0 562 | 563 | sdep1 ~~ sdep1 564 | sdep2 ~~ sdep2 565 | sdep3 ~~ sdep3 566 | sdep4 ~~ sdep4 567 | sdep5 ~~ sdep5 568 | 569 | salc1 ~~ sdep1 570 | salc2 ~~ p1*sdep2 571 | salc3 ~~ p1*sdep3 572 | salc4 ~~ p1*sdep4 573 | salc5 ~~ p1*sdep5 574 | 575 | # --------------------------- 576 | # residual regressions 577 | # --------------------------- 578 | # ALCOHOL 579 | salc2 ~ p2*salc1 + p4*sdep1 580 | salc3 ~ p2*salc2 + p4*sdep2 581 | salc4 ~ p2*salc3 + p4*sdep3 582 | salc5 ~ p2*salc4 + p4*sdep4 583 | 584 | # DEPRESSION 585 | sdep2 ~ p3*sdep1 586 | sdep3 ~ p3*sdep2 587 | sdep4 ~ p3*sdep3 588 | sdep5 ~ p3*sdep4 589 | ' 590 | ad.fit3 <- lavaan(ad.mod3, lcmsr.sim) 591 | # summary(ad.fit3, fit.measures = T) 592 | # print(anova(ad.fit2, ad.fit3)) 593 | 594 | # model 4 595 | ad.mod4 <- ' 596 | # --------------------------- 597 | # latent factors 598 | # --------------------------- 599 | # ALCOHOL 600 | # random intercept 601 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 602 | alc.i ~ 1 603 | alc.i ~~ alc.i 604 | 605 | # random slope 606 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 607 | alc.s ~ 1 608 | alc.s ~~ alc.s 609 | alc.i ~~ alc.s 610 | 611 | # DEPRESSION 612 | # random intercept 613 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 614 | dep.i ~ 1 615 | dep.i ~~ dep.i 616 | dep.i ~~ alc.i 617 | dep.i ~~ alc.s 618 | 619 | # --------------------------- 620 | # create structured residuals 621 | # --------------------------- 622 | # ALCOHOL 623 | alc1 ~~ 0*alc1 624 | alc2 ~~ 0*alc2 625 | alc3 ~~ 0*alc3 626 | alc4 ~~ 0*alc4 627 | alc5 ~~ 0*alc5 628 | 629 | salc1 =~ 1*alc1 630 | salc2 =~ 1*alc2 631 | salc3 =~ 1*alc3 632 | salc4 =~ 1*alc4 633 | salc5 =~ 1*alc5 634 | 635 | salc1 ~ 0 636 | salc2 ~ 0 637 | salc3 ~ 0 638 | salc4 ~ 0 639 | salc5 ~ 0 640 | 641 | salc1 ~~ salc1 642 | salc2 ~~ salc2 643 | salc3 ~~ salc3 644 | salc4 ~~ salc4 645 | salc5 ~~ salc5 646 | 647 | # DEPRESSION 648 | dep1 ~~ 0*dep1 649 | dep2 ~~ 0*dep2 650 | dep3 ~~ 0*dep3 651 | dep4 ~~ 0*dep4 652 | dep5 ~~ 0*dep5 653 | 654 | sdep1 =~ 1*dep1 655 | sdep2 =~ 1*dep2 656 | sdep3 =~ 1*dep3 657 | sdep4 =~ 1*dep4 658 | sdep5 =~ 1*dep5 659 | 660 | sdep1 ~ 0 661 | sdep2 ~ 0 662 | sdep3 ~ 0 663 | sdep4 ~ 0 664 | sdep5 ~ 0 665 | 666 | sdep1 ~~ sdep1 667 | sdep2 ~~ sdep2 668 | sdep3 ~~ sdep3 669 | sdep4 ~~ sdep4 670 | sdep5 ~~ sdep5 671 | 672 | salc1 ~~ sdep1 673 | salc2 ~~ p1*sdep2 674 | salc3 ~~ p1*sdep3 675 | salc4 ~~ p1*sdep4 676 | salc5 ~~ p1*sdep5 677 | 678 | # --------------------------- 679 | # residual regressions 680 | # --------------------------- 681 | # ALCOHOL 682 | salc2 ~ p2*salc1 683 | salc3 ~ p2*salc2 684 | salc4 ~ p2*salc3 685 | salc5 ~ p2*salc4 686 | 687 | # DEPRESSION 688 | sdep2 ~ p3*sdep1 + salc1 689 | sdep3 ~ p3*sdep2 + salc2 690 | sdep4 ~ p3*sdep3 + salc3 691 | sdep5 ~ p3*sdep4 + salc4 692 | ' 693 | ad.fit4 <- lavaan(ad.mod4, lcmsr.sim) 694 | # summary(ad.fit4, fit.measures = T) 695 | # print(anova(ad.fit1, ad.fit4)) 696 | 697 | # model 5 698 | ad.mod5 <- ' 699 | # --------------------------- 700 | # latent factors 701 | # --------------------------- 702 | # ALCOHOL 703 | # random intercept 704 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 705 | alc.i ~ 1 706 | alc.i ~~ alc.i 707 | 708 | # random slope 709 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 710 | alc.s ~ 1 711 | alc.s ~~ alc.s 712 | alc.i ~~ alc.s 713 | 714 | # DEPRESSION 715 | # random intercept 716 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 717 | dep.i ~ 1 718 | dep.i ~~ dep.i 719 | dep.i ~~ alc.i 720 | dep.i ~~ alc.s 721 | 722 | # --------------------------- 723 | # create structured residuals 724 | # --------------------------- 725 | # ALCOHOL 726 | alc1 ~~ 0*alc1 727 | alc2 ~~ 0*alc2 728 | alc3 ~~ 0*alc3 729 | alc4 ~~ 0*alc4 730 | alc5 ~~ 0*alc5 731 | 732 | salc1 =~ 1*alc1 733 | salc2 =~ 1*alc2 734 | salc3 =~ 1*alc3 735 | salc4 =~ 1*alc4 736 | salc5 =~ 1*alc5 737 | 738 | salc1 ~ 0 739 | salc2 ~ 0 740 | salc3 ~ 0 741 | salc4 ~ 0 742 | salc5 ~ 0 743 | 744 | salc1 ~~ salc1 745 | salc2 ~~ salc2 746 | salc3 ~~ salc3 747 | salc4 ~~ salc4 748 | salc5 ~~ salc5 749 | 750 | # DEPRESSION 751 | dep1 ~~ 0*dep1 752 | dep2 ~~ 0*dep2 753 | dep3 ~~ 0*dep3 754 | dep4 ~~ 0*dep4 755 | dep5 ~~ 0*dep5 756 | 757 | sdep1 =~ 1*dep1 758 | sdep2 =~ 1*dep2 759 | sdep3 =~ 1*dep3 760 | sdep4 =~ 1*dep4 761 | sdep5 =~ 1*dep5 762 | 763 | sdep1 ~ 0 764 | sdep2 ~ 0 765 | sdep3 ~ 0 766 | sdep4 ~ 0 767 | sdep5 ~ 0 768 | 769 | sdep1 ~~ sdep1 770 | sdep2 ~~ sdep2 771 | sdep3 ~~ sdep3 772 | sdep4 ~~ sdep4 773 | sdep5 ~~ sdep5 774 | 775 | salc1 ~~ sdep1 776 | salc2 ~~ p1*sdep2 777 | salc3 ~~ p1*sdep3 778 | salc4 ~~ p1*sdep4 779 | salc5 ~~ p1*sdep5 780 | 781 | # --------------------------- 782 | # residual regressions 783 | # --------------------------- 784 | # ALCOHOL 785 | salc2 ~ p2*salc1 786 | salc3 ~ p2*salc2 787 | salc4 ~ p2*salc3 788 | salc5 ~ p2*salc4 789 | 790 | # DEPRESSION 791 | sdep2 ~ p3*sdep1 + p4*salc1 792 | sdep3 ~ p3*sdep2 + p4*salc2 793 | sdep4 ~ p3*sdep3 + p4*salc3 794 | sdep5 ~ p3*sdep4 + p4*salc4 795 | ' 796 | ad.fit5 <- lavaan(ad.mod5, lcmsr.sim) 797 | # summary(ad.fit5, fit.measures = T) 798 | # print(anova(ad.fit4, ad.fit5)) 799 | 800 | # model 6 801 | ad.mod6 <- ' 802 | # --------------------------- 803 | # latent factors 804 | # --------------------------- 805 | # ALCOHOL 806 | # random intercept 807 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 808 | alc.i ~ 1 809 | alc.i ~~ alc.i 810 | 811 | # random slope 812 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 813 | alc.s ~ 1 814 | alc.s ~~ alc.s 815 | alc.i ~~ alc.s 816 | 817 | # DEPRESSION 818 | # random intercept 819 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 820 | dep.i ~ 1 821 | dep.i ~~ dep.i 822 | dep.i ~~ alc.i 823 | dep.i ~~ alc.s 824 | 825 | # --------------------------- 826 | # create structured residuals 827 | # --------------------------- 828 | # ALCOHOL 829 | alc1 ~~ 0*alc1 830 | alc2 ~~ 0*alc2 831 | alc3 ~~ 0*alc3 832 | alc4 ~~ 0*alc4 833 | alc5 ~~ 0*alc5 834 | 835 | salc1 =~ 1*alc1 836 | salc2 =~ 1*alc2 837 | salc3 =~ 1*alc3 838 | salc4 =~ 1*alc4 839 | salc5 =~ 1*alc5 840 | 841 | salc1 ~ 0 842 | salc2 ~ 0 843 | salc3 ~ 0 844 | salc4 ~ 0 845 | salc5 ~ 0 846 | 847 | salc1 ~~ salc1 848 | salc2 ~~ salc2 849 | salc3 ~~ salc3 850 | salc4 ~~ salc4 851 | salc5 ~~ salc5 852 | 853 | # DEPRESSION 854 | dep1 ~~ 0*dep1 855 | dep2 ~~ 0*dep2 856 | dep3 ~~ 0*dep3 857 | dep4 ~~ 0*dep4 858 | dep5 ~~ 0*dep5 859 | 860 | sdep1 =~ 1*dep1 861 | sdep2 =~ 1*dep2 862 | sdep3 =~ 1*dep3 863 | sdep4 =~ 1*dep4 864 | sdep5 =~ 1*dep5 865 | 866 | sdep1 ~ 0 867 | sdep2 ~ 0 868 | sdep3 ~ 0 869 | sdep4 ~ 0 870 | sdep5 ~ 0 871 | 872 | sdep1 ~~ sdep1 873 | sdep2 ~~ sdep2 874 | sdep3 ~~ sdep3 875 | sdep4 ~~ sdep4 876 | sdep5 ~~ sdep5 877 | 878 | salc1 ~~ sdep1 879 | salc2 ~~ p1*sdep2 880 | salc3 ~~ p1*sdep3 881 | salc4 ~~ p1*sdep4 882 | salc5 ~~ p1*sdep5 883 | 884 | # --------------------------- 885 | # residual regressions 886 | # --------------------------- 887 | # ALCOHOL 888 | salc2 ~ p2*salc1 889 | salc3 ~ p2*salc2 890 | salc4 ~ p2*salc3 891 | salc5 ~ p2*salc4 892 | 893 | # DEPRESSION 894 | sdep2 ~ p3*sdep1 + p4*salc1 895 | sdep3 ~ p3*sdep2 + p5*salc2 896 | sdep4 ~ p3*sdep3 + p6*salc3 897 | sdep5 ~ p3*sdep4 + p7*salc4 898 | 899 | kappa := p5 - p4 900 | p5 == p4 + 1*kappa 901 | p6 == p4 + 2*kappa 902 | p7 == p4 + 3*kappa 903 | ' 904 | ad.fit6 <- lavaan(ad.mod6, lcmsr.sim) 905 | # summary(ad.fit6, fit.measures = T) 906 | # print(anova(ad.fit4, ad.fit6)) 907 | 908 | # model 7 909 | ad.mod7 <- ' 910 | # --------------------------- 911 | # latent factors 912 | # --------------------------- 913 | # ALCOHOL 914 | # random intercept 915 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 916 | alc.i ~ 1 917 | alc.i ~~ alc.i 918 | 919 | # random slope 920 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 921 | alc.s ~ 1 922 | alc.s ~~ alc.s 923 | alc.i ~~ alc.s 924 | 925 | # DEPRESSION 926 | # random intercept 927 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 928 | dep.i ~ 1 929 | dep.i ~~ dep.i 930 | dep.i ~~ alc.i 931 | dep.i ~~ alc.s 932 | 933 | # --------------------------- 934 | # create structured residuals 935 | # --------------------------- 936 | # ALCOHOL 937 | alc1 ~~ 0*alc1 938 | alc2 ~~ 0*alc2 939 | alc3 ~~ 0*alc3 940 | alc4 ~~ 0*alc4 941 | alc5 ~~ 0*alc5 942 | 943 | salc1 =~ 1*alc1 944 | salc2 =~ 1*alc2 945 | salc3 =~ 1*alc3 946 | salc4 =~ 1*alc4 947 | salc5 =~ 1*alc5 948 | 949 | salc1 ~ 0 950 | salc2 ~ 0 951 | salc3 ~ 0 952 | salc4 ~ 0 953 | salc5 ~ 0 954 | 955 | salc1 ~~ salc1 956 | salc2 ~~ salc2 957 | salc3 ~~ salc3 958 | salc4 ~~ salc4 959 | salc5 ~~ salc5 960 | 961 | # DEPRESSION 962 | dep1 ~~ 0*dep1 963 | dep2 ~~ 0*dep2 964 | dep3 ~~ 0*dep3 965 | dep4 ~~ 0*dep4 966 | dep5 ~~ 0*dep5 967 | 968 | sdep1 =~ 1*dep1 969 | sdep2 =~ 1*dep2 970 | sdep3 =~ 1*dep3 971 | sdep4 =~ 1*dep4 972 | sdep5 =~ 1*dep5 973 | 974 | sdep1 ~ 0 975 | sdep2 ~ 0 976 | sdep3 ~ 0 977 | sdep4 ~ 0 978 | sdep5 ~ 0 979 | 980 | sdep1 ~~ sdep1 981 | sdep2 ~~ sdep2 982 | sdep3 ~~ sdep3 983 | sdep4 ~~ sdep4 984 | sdep5 ~~ sdep5 985 | 986 | salc1 ~~ sdep1 987 | salc2 ~~ p1*sdep2 988 | salc3 ~~ p1*sdep3 989 | salc4 ~~ p1*sdep4 990 | salc5 ~~ p1*sdep5 991 | 992 | # --------------------------- 993 | # residual regressions 994 | # --------------------------- 995 | # ALCOHOL 996 | salc2 ~ p2*salc1 + p4*sdep1 997 | salc3 ~ p2*salc2 + p4*sdep2 998 | salc4 ~ p2*salc3 + p4*sdep3 999 | salc5 ~ p2*salc4 + p4*sdep4 1000 | 1001 | # DEPRESSION 1002 | sdep2 ~ p3*sdep1 + p5*salc1 1003 | sdep3 ~ p3*sdep2 + p6*salc2 1004 | sdep4 ~ p3*sdep3 + p7*salc3 1005 | sdep5 ~ p3*sdep4 + p8*salc4 1006 | 1007 | kappa := p6 - p5 1008 | p6 == p5 + 1*kappa 1009 | p7 == p5 + 2*kappa 1010 | p8 == p5 + 3*kappa 1011 | ' 1012 | ad.fit7 <- lavaan(ad.mod7, lcmsr.sim) 1013 | # summary(ad.fit7, fit.measures = T) 1014 | 1015 | # ------------------------------- 1016 | # bivariate model for alcohol & depression 1017 | # conditional on gender & treatment 1018 | # ------------------------------- 1019 | 1020 | # model 8 1021 | ad.mod8 <- ' 1022 | # --------------------------- 1023 | # latent factors 1024 | # --------------------------- 1025 | # ALCOHOL 1026 | # random intercept 1027 | alc.i =~ 1*alc1 + 1*alc2 + 1*alc3 + 1*alc4 + 1*alc5 1028 | alc.i ~ 1 + gen + trt 1029 | alc.i ~~ alc.i 1030 | 1031 | # random slope 1032 | alc.s =~ 0*alc1 + 1*alc2 + 2*alc3 + 3*alc4 + 4*alc5 1033 | alc.s ~ 1 + gen + trt 1034 | alc.s ~~ alc.s 1035 | alc.i ~~ alc.s 1036 | 1037 | # DEPRESSION 1038 | # random intercept 1039 | dep.i =~ 1*dep1 + 1*dep2 + 1*dep3 + 1*dep4 + 1*dep5 1040 | dep.i ~ 1 + gen + trt 1041 | 1042 | dep.i ~~ dep.i 1043 | dep.i ~~ alc.i 1044 | dep.i ~~ alc.s 1045 | 1046 | # --------------------------- 1047 | # create structured residuals 1048 | # --------------------------- 1049 | # ALCOHOL 1050 | alc1 ~~ 0*alc1 1051 | alc2 ~~ 0*alc2 1052 | alc3 ~~ 0*alc3 1053 | alc4 ~~ 0*alc4 1054 | alc5 ~~ 0*alc5 1055 | 1056 | salc1 =~ 1*alc1 1057 | salc2 =~ 1*alc2 1058 | salc3 =~ 1*alc3 1059 | salc4 =~ 1*alc4 1060 | salc5 =~ 1*alc5 1061 | 1062 | salc1 ~ 0 1063 | salc2 ~ 0 1064 | salc3 ~ 0 1065 | salc4 ~ 0 1066 | salc5 ~ 0 1067 | 1068 | salc1 ~~ salc1 1069 | salc2 ~~ salc2 1070 | salc3 ~~ salc3 1071 | salc4 ~~ salc4 1072 | salc5 ~~ salc5 1073 | 1074 | # DEPRESSION 1075 | dep1 ~~ 0*dep1 1076 | dep2 ~~ 0*dep2 1077 | dep3 ~~ 0*dep3 1078 | dep4 ~~ 0*dep4 1079 | dep5 ~~ 0*dep5 1080 | 1081 | sdep1 =~ 1*dep1 1082 | sdep2 =~ 1*dep2 1083 | sdep3 =~ 1*dep3 1084 | sdep4 =~ 1*dep4 1085 | sdep5 =~ 1*dep5 1086 | 1087 | sdep1 ~ 0 1088 | sdep2 ~ 0 1089 | sdep3 ~ 0 1090 | sdep4 ~ 0 1091 | sdep5 ~ 0 1092 | 1093 | sdep1 ~~ sdep1 1094 | sdep2 ~~ sdep2 1095 | sdep3 ~~ sdep3 1096 | sdep4 ~~ sdep4 1097 | sdep5 ~~ sdep5 1098 | 1099 | salc1 ~~ sdep1 1100 | salc2 ~~ p1*sdep2 1101 | salc3 ~~ p1*sdep3 1102 | salc4 ~~ p1*sdep4 1103 | salc5 ~~ p1*sdep5 1104 | 1105 | # --------------------------- 1106 | # residual regressions 1107 | # --------------------------- 1108 | # ALCOHOL 1109 | salc2 ~ p2*salc1 + p4*sdep1 1110 | salc3 ~ p2*salc2 + p4*sdep2 1111 | salc4 ~ p2*salc3 + p4*sdep3 1112 | salc5 ~ p2*salc4 + p4*sdep4 1113 | 1114 | # DEPRESSION 1115 | sdep2 ~ p3*sdep1 + p5*salc1 1116 | sdep3 ~ p3*sdep2 + p6*salc2 1117 | sdep4 ~ p3*sdep3 + p7*salc3 1118 | sdep5 ~ p3*sdep4 + p8*salc4 1119 | 1120 | kappa := p6 - p5 1121 | p6 == p5 + 1*kappa 1122 | p7 == p5 + 2*kappa 1123 | p8 == p5 + 3*kappa 1124 | ' 1125 | ad.fit8 <- lavaan(ad.mod8, lcmsr.sim) 1126 | summary(ad.fit8, fit.measures = T) 1127 | -------------------------------------------------------------------------------- /data/currandemo.dat: -------------------------------------------------------------------------------- 1 | 1 1 1 5.965188 6.565689 3.409886 5.062632 10.934174 2.850457 -2.586410 -1.620009 -1.127919 -4.600885 2 | 2 1 0 7.131402 3.586757 9.612473 7.743128 9.916999 1.850571 -0.745795 -1.002225 1.877833 0.543067 3 | 3 1 0 4.761294 1.927701 6.867105 1.378688 0.956535 1.530763 2.102556 1.466487 2.521019 -0.291426 4 | 4 0 0 4.871162 6.085778 2.603995 2.412024 1.906498 1.979955 2.263897 2.342727 0.735305 5.019734 5 | 5 1 0 3.169513 5.534468 2.495484 4.302668 7.730947 2.123857 0.769192 2.054296 3.216513 1.265279 6 | 6 1 0 0.927734 1.842237 4.054808 6.428935 3.352636 1.049315 0.920176 1.224657 -0.049343 1.732567 7 | 7 1 1 2.533457 0.317134 5.288620 1.889978 6.955049 0.230084 -1.735781 -1.716251 1.165439 -1.696050 8 | 8 1 0 5.116521 3.635587 5.395471 12.222831 8.967415 -0.443492 3.621190 2.265593 -0.359288 4.772487 9 | 9 0 1 0.529484 1.754256 -3.707724 1.087559 1.576240 0.637920 -1.509684 0.186466 -1.181396 3.914198 10 | 10 0 0 8.785190 8.804097 4.756224 8.559701 -0.188024 1.550209 2.215392 3.187221 0.251529 4.198992 11 | 11 0 1 2.575162 3.866306 2.186392 -0.894969 -5.172764 4.780726 4.233073 2.570589 2.791243 0.599487 12 | 12 0 1 5.865996 5.364320 3.227130 3.440099 -0.726701 2.539937 3.134493 1.838475 1.148415 3.380452 13 | 13 1 1 0.338165 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1361 1 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1 1 0 0 1 0 0 0 0 1 1 0 1 1 1 1363 | 1362 1 0 1 1 0 1 0 0 1 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 0 1 1364 | 1363 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 0 1 0 0 1 1 1 0 1 1 1365 | 1364 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1366 | 1365 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 1367 | 1366 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1368 | 1367 0 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1369 | 1368 0 1 1 1 1 1 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1370 | 1369 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 1 1371 | 1370 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 0 1 0 0 1 1 1 1 1 1 1372 | 1371 1 0 1 1 0 1 1 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 0 0 0 1 1373 | 1372 1 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 0 1 1374 | 1373 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1 0 1 0 1 0 1 1 1375 | 1374 0 1 1 1 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1 1 1 0 1 1 1 1 1 1 1 0 0 0 0 1 1 1 0 1 0 1 1389 | 1388 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1390 | 1389 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 1 1 1 1 0 1 1 1391 | 1390 0 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1392 | 1391 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1393 | 1392 0 1 0 0 1 1 1 1 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1394 | 1393 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 1 1 1 1 1395 | 1394 0 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 1396 | 1395 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1397 | 1396 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1398 | 1397 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1399 | 1398 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 0 0 1 0 1 1 1 0 0 0 1 1400 | 1399 1 1 1 1 1 0 0 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1401 | 1400 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 0 1 1 1 1402 | 1401 0 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0 0 1 1 1403 | 1402 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 1 1 1404 | 1403 1 0 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 0 1 0 1 1 0 0 0 1 1 1405 | 1404 0 0 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 0 0 1 1 1 0 1 0 1 1406 | 1405 0 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 1 0 1 1407 | 1406 0 0 1 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 1 1 0 1 1 1 1 0 1 1408 | 1407 1 0 0 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 1 0 1409 | 1408 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1410 | 1409 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1411 | 1410 0 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1412 | 1411 1 1 0 0 1 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 0 0 1413 | 1412 0 0 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1414 | 1413 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1415 | 1414 1 1 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1416 | 1415 0 0 0 1 1 0 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 1 1 1417 | 1416 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 1 1 0 1418 | 1417 0 1 1 0 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 0 0 1 0 1419 | 1418 0 1 0 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 0 1 0 0 0 1 1 0 0 0 0 1 1420 | 1419 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1421 | 1420 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1422 | 1421 0 0 1 0 1 1 0 1 1 1 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 1423 | 1422 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1424 | 1423 0 1 1 1 1 0 1 1 1 0 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1425 | 1424 1 0 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 0 1 0 0 1 0 1 0 0 1 1 1 1 1 1 1426 | 1425 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1427 | 1426 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1428 | 1427 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1429 | 1428 1 0 0 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 0 1430 | 1429 0 1 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 1431 | 1430 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 0 0 1 0 0 0 0 1 1432 | 1431 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 0 1 1 0 1 0 1 0 0 0 1 1 0 1 0 1433 | 1432 0 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1 1 0 1 1 1 1434 | 1433 0 1 0 1 1 1 0 1 1 1 0 0 1 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 1 1 0 1 1435 | 1434 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 1436 | 1435 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 0 1 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 1437 | 1436 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 0 1 0 0 1 0 0 1 1 1 1438 | 1437 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 0 1 1439 | 1438 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1440 | 1439 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 1 1 1441 | 1440 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1442 | 1441 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1443 | 1442 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 1444 | 1443 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0 1 1 1 1 1 1 1445 | 1444 1 0 0 0 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1446 | 1445 0 0 0 1 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 0 0 1 0 0 1 1 1 1 0 0 0 1 1447 | 1446 0 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 0 1 1 1 1 1 1 1448 | 1447 0 1 0 1 1 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 1 1449 | 1448 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 0 0 1 1 0 1 1 1 1 1 1 1450 | 1449 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1451 | 1450 0 0 0 1 1 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1452 | 1451 0 1 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0 1453 | 1452 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1454 | 1453 0 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 0 1 1 0 1 1455 | 1454 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 1 0 1 0 0 1 1456 | 1455 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 1457 | 1456 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1458 | 1457 1 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 0 0 1 0 1 0 1 1 1459 | 1458 1 0 0 0 1 1 1 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1460 | 1459 0 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 0 1 0 0 1 1 1 0 1 1 1461 | 1460 0 0 0 1 1 1 0 0 1 1 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1462 | 1461 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0 1 0 0 0 1 1 1463 | 1462 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1464 | 1463 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 1 1 0 1 1 1465 | 1464 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 1466 | 1465 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 1 1 1 1467 | 1466 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1468 | 1467 1 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1469 | 1468 0 1 1 1 1 1 1 1 1 0 1 1 1 0 0 1 1 1 0 0 0 0 0 0 0 1 1 0 1 1 0 0 1470 | 1469 0 0 0 1 1 0 0 1 1 1 0 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1471 | 1470 0 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 1 1 1472 | 1471 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 0 1 1 0 1 1 1473 | 1472 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1474 | 1473 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1475 | 1474 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 1476 | 1475 0 1 1 0 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 1477 | 1476 1 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1478 | 1477 1 1 1 1 1 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1479 | 1478 1 0 0 0 1 1 0 1 1 1 0 1 1 0 1 1 0 1 0 0 1 1 0 0 0 1 1 1 0 0 1 1 1480 | 1479 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 0 1 1 0 0 1 1 0 0 1 1481 | 1480 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 1 0 0 1 0 1 1 1 1 1482 | 1481 0 1 1 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 1 0 1 1 0 0 1 0 0 1 0 1 1483 | 1482 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0 1 1 1 1 1 1 1484 | 1483 1 1 1 1 1 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 1 1 1485 | 1484 1 0 0 1 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1486 | 1485 0 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 1 1487 | 1486 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1488 | 1487 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 1 0 0 1 1 0 1 0 1 1489 | 1488 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 0 0 1 1 1490 | 1489 0 1 0 1 1 1 1 1 0 0 0 1 1 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 1 1491 | 1490 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1492 | 1491 0 1 1 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 1 1 1 0 1 1 0 1 0 0 0 0 1 1493 | 1492 0 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 1 1494 | 1493 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1495 | 1494 1 1 1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1496 | 1495 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 0 1 0 1 1 1 1 1 0 1 1497 | 1496 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1498 | 1497 1 1 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 1499 | 1498 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 1500 | 1499 0 1 1 1 1 1 0 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 1501 | 1500 0 1 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1502 | -------------------------------------------------------------------------------- /little2006-scaling.R: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------ 2 | # A Non-arbitrary method of identifying and scaling 3 | # latent variables in SEM and MACS models. 4 | # 5 | # Little, Slegers, & Card 6 | # 7 | # Structural Equation Modeling 2006 8 | # ------------------------------------------------ 9 | 10 | library("lavaan") 11 | 12 | # Data ---- 13 | # data were available as mean vectors, standard deviation vectors 14 | # and covariance matrices of the indicators by group 15 | 16 | # 7th graders 17 | n7 <- 380 18 | means7 <- c(3.13552, 2.99061, 3.06945, 1.70069, 1.52705, 1.54483) 19 | sd7 <- c(0.66770, 0.68506, 0.70672, 0.71418, 0.66320, 0.65276) 20 | lower7 <- ' 21 | 1.00000 22 | 0.75854 1.00000 23 | 0.76214 0.78705 1.00000 24 | 0.02766 0.00973 -.05762 1.00000 25 | -.06112 -.06105 -.14060 0.78501 1.00000 26 | -.02222 -.05180 -.10250 0.81616 0.81076 1.00000' 27 | seventh.cor <- getCov(lower7, names = c("PosAFF1", "PosAFF2", "PosAFF3", "NegAFF1", "NegAFF2", "NegAFF3")) 28 | seventh.cov <- (sd7 %*% t(sd7)) * seventh.cor 29 | 30 | n8 <- 379 31 | means8 <- c(3.07338, 2.84716, 2.97882, 1.71700, 1.57955, 1.55001) 32 | sd8 <- c(0.70299, 0.71780, 0.76208, 0.65011, 0.60168, 0.61420) 33 | lower8 <- ' 34 | 1.00000 35 | 0.81366 1.00000 36 | 0.84980 0.83523 1.00000 37 | -0.18804 -0.15524 -0.21520 1.00000 38 | -0.28875 -0.24951 -0.33769 0.78418 1.00000 39 | -0.29342 -0.21022 -0.30553 0.79952 0.83156 1.00000' 40 | eighth.cor <- getCov(lower8, names = c("PosAFF1", "PosAFF2", "PosAFF3", "NegAFF1", "NegAFF2", "NegAFF3")) 41 | eighth.cov <- (sd8 %*% t(sd8)) * eighth.cor 42 | 43 | # Combine data into lists ---- 44 | cov.grades <- list(seventh.cov, eighth.cov) 45 | mean.grades <- list(means7, means8) 46 | n.grades <- list(n7, n8) 47 | 48 | # Method 1: Fixed latent parameters 49 | method1.mod <- ' 50 | group: 1 51 | # Measurement Model 52 | Pos =~ PosAFF1 + PosAFF2 + PosAFF3 53 | Neg =~ NegAFF1 + NegAFF2 + NegAFF3 54 | 55 | # Intercepts 56 | PosAFF1 ~ 1 57 | PosAFF2 ~ 1 58 | PosAFF3 ~ 1 59 | NegAFF1 ~ 1 60 | NegAFF2 ~ 1 61 | NegAFF3 ~ 1 62 | 63 | # Latent Means 64 | Pos ~ 0*1 65 | Neg ~ 0*1 66 | 67 | # Latent Covariances 68 | Pos ~~ 1*Pos 69 | Neg ~~ 1*Neg 70 | Pos ~~ Neg 71 | 72 | # Residual Variances 73 | PosAFF1 ~~ PosAFF1 74 | PosAFF2 ~~ PosAFF2 75 | PosAFF3 ~~ PosAFF3 76 | NegAFF1 ~~ NegAFF1 77 | NegAFF2 ~~ NegAFF2 78 | NegAFF3 ~~ NegAFF3 79 | 80 | group: 2 81 | # Measurement Model 82 | Pos =~ PosAFF1 + PosAFF2 + PosAFF3 83 | Neg =~ NegAFF1 + NegAFF2 + NegAFF3 84 | 85 | # Intercepts 86 | PosAFF1 ~ 1 87 | PosAFF2 ~ 1 88 | PosAFF3 ~ 1 89 | NegAFF1 ~ 1 90 | NegAFF2 ~ 1 91 | NegAFF3 ~ 1 92 | 93 | # Latent Means 94 | Pos ~ 1 95 | Neg ~ 1 96 | 97 | # Latent Covariances 98 | Pos ~~ Pos 99 | Neg ~~ Neg 100 | Pos ~~ Neg 101 | 102 | # Residual Variances 103 | PosAFF1 ~~ PosAFF1 104 | PosAFF2 ~~ PosAFF2 105 | PosAFF3 ~~ PosAFF3 106 | NegAFF1 ~~ NegAFF1 107 | NegAFF2 ~~ NegAFF2 108 | NegAFF3 ~~ NegAFF3 109 | ' 110 | method1.fit <- lavaan(method1.mod, sample.cov = cov.grades, sample.nobs = n.grades, sample.mean = mean.grades, group.equal = c("loadings", "intercepts")) 111 | summary(method1.fit, standardized = TRUE) 112 | 113 | # Method 2a: Fixed marker variable, lowest variance indicators ---- 114 | method2a.mod <- ' 115 | # Measurement Model 116 | Pos =~ 1*PosAFF1 + PosAFF2 + PosAFF3 117 | Neg =~ NegAFF1 + 1*NegAFF2 + NegAFF3 118 | 119 | # Intercepts 120 | PosAFF1 ~ 0*1 121 | PosAFF2 ~ 1 122 | PosAFF3 ~ 1 123 | NegAFF1 ~ 1 124 | NegAFF2 ~ 0*1 125 | NegAFF3 ~ 1 126 | 127 | # Latent Means 128 | Pos ~ 1 129 | Neg ~ 1 130 | 131 | # Latent Covariances 132 | Pos ~~ Pos 133 | Neg ~~ Neg 134 | Pos ~~ Neg 135 | 136 | # Residual Variances 137 | PosAFF1 ~~ PosAFF1 138 | PosAFF2 ~~ PosAFF2 139 | PosAFF3 ~~ PosAFF3 140 | 141 | NegAFF1 ~~ NegAFF1 142 | NegAFF2 ~~ NegAFF2 143 | NegAFF3 ~~ NegAFF3 144 | ' 145 | method2a.fit <- lavaan(method2a.mod, sample.cov = cov.grades, sample.nobs = n.grades, sample.mean = mean.grades, group.equal = c("loadings", "intercepts")) 146 | summary(method2a.fit, standardized = TRUE) 147 | 148 | # Method 2b: Fixed marker variable, middle variance indicators ---- 149 | method2b.mod <- ' 150 | # Measurement Model 151 | Pos =~ PosAFF1 + 1*PosAFF2 + PosAFF3 152 | Neg =~ NegAFF1 + NegAFF2 + 1*NegAFF3 153 | 154 | # Intercepts 155 | PosAFF1 ~ 1 156 | PosAFF2 ~ 0*1 157 | PosAFF3 ~ 1 158 | NegAFF1 ~ 1 159 | NegAFF2 ~ 1 160 | NegAFF3 ~ 0*1 161 | 162 | # Latent Means 163 | Pos ~ 1 164 | Neg ~ 1 165 | 166 | # Latent Covariances 167 | Pos ~~ Pos 168 | Neg ~~ Neg 169 | Pos ~~ Neg 170 | 171 | # Residual Variances 172 | PosAFF1 ~~ PosAFF1 173 | PosAFF2 ~~ PosAFF2 174 | PosAFF3 ~~ PosAFF3 175 | 176 | NegAFF1 ~~ NegAFF1 177 | NegAFF2 ~~ NegAFF2 178 | NegAFF3 ~~ NegAFF3 179 | ' 180 | method2b.fit <- lavaan(method2b.mod, sample.cov = cov.grades, sample.nobs = n.grades, sample.mean = mean.grades, group.equal = c("loadings", "intercepts")) 181 | summary(method2b.fit, standardized = TRUE) 182 | 183 | # Method 2c: Fixed marker variable, highest variance indicators ---- 184 | method2c.mod <- ' 185 | # Measurement Model 186 | Pos =~ PosAFF1 + PosAFF2 + 1*PosAFF3 187 | Neg =~ 1*NegAFF1 + NegAFF2 + NegAFF3 188 | 189 | # Intercepts 190 | PosAFF1 ~ 1 191 | PosAFF2 ~ 1 192 | PosAFF3 ~ 0*1 193 | NegAFF1 ~ 0*1 194 | NegAFF2 ~ 1 195 | NegAFF3 ~ 1 196 | 197 | # Latent Means 198 | Pos ~ 1 199 | Neg ~ 1 200 | 201 | # Latent Covariances 202 | Pos ~~ Pos 203 | Neg ~~ Neg 204 | Pos ~~ Neg 205 | 206 | # Residual Variances 207 | PosAFF1 ~~ PosAFF1 208 | PosAFF2 ~~ PosAFF2 209 | PosAFF3 ~~ PosAFF3 210 | 211 | NegAFF1 ~~ NegAFF1 212 | NegAFF2 ~~ NegAFF2 213 | NegAFF3 ~~ NegAFF3 214 | ' 215 | method2c.fit <- lavaan(method2c.mod, sample.cov = cov.grades, sample.nobs = n.grades, sample.mean = mean.grades, group.equal = c("loadings", "intercepts")) 216 | summary(method2c.fit, standardized = TRUE) 217 | 218 | # Method 3: Effects-coding constraints ---- 219 | # - restricts the factor loadings to sum to the number of loadings per factor 220 | # - restricts the intercepts to sum to zero per factor 221 | method3.mod <- ' 222 | # Measurement Model 223 | Pos =~ lp1*PosAFF1 + lp2*PosAFF2 + lp3*PosAFF3 224 | Neg =~ ln1*NegAFF1 + ln2*NegAFF2 + ln3*NegAFF3 225 | 226 | # Intercepts 227 | PosAFF1 ~ tp1*1 228 | PosAFF2 ~ tp2*1 229 | PosAFF3 ~ tp3*1 230 | NegAFF1 ~ tn1*1 231 | NegAFF2 ~ tn2*1 232 | NegAFF3 ~ tn3*1 233 | 234 | # Latent Means 235 | Pos ~ 1 236 | Neg ~ 1 237 | 238 | # Latent Covariances 239 | Pos ~~ Pos 240 | Neg ~~ Neg 241 | Pos ~~ Neg 242 | 243 | # Residual Variances 244 | PosAFF1 ~~ PosAFF1 245 | PosAFF2 ~~ PosAFF2 246 | PosAFF3 ~~ PosAFF3 247 | 248 | NegAFF1 ~~ NegAFF1 249 | NegAFF2 ~~ NegAFF2 250 | NegAFF3 ~~ NegAFF3 251 | 252 | # Define constrains for effects-coding 253 | lp1 == 3 - lp2 - lp3 254 | ln1 == 3 - ln2 - ln3 255 | 256 | tp1 == 0 - tp2 - tp3 257 | tn1 == 0 - tn2 - tn3 258 | ' 259 | method3.fit <- lavaan(method3.mod, sample.cov = cov.grades, sample.nobs = n.grades, sample.mean = mean.grades, group.equal = c("loadings", "intercepts")) 260 | summary(method3.fit, standardized = TRUE) 261 | 262 | # Show that the models are equivalent ---- 263 | compare.fit <- rbind( 264 | fitmeasures(method1.fit, fit.measures = c("TLI", "CFI", "RMSEA")), 265 | fitmeasures(method2a.fit, fit.measures = c("TLI", "CFI", "RMSEA")), 266 | fitmeasures(method2b.fit, fit.measures = c("TLI", "CFI", "RMSEA")), 267 | fitmeasures(method2c.fit, fit.measures = c("TLI", "CFI", "RMSEA")), 268 | fitmeasures(method3.fit, fit.measures = c("TLI", "CFI", "RMSEA"))) 269 | 270 | rownames(compare.fit) <- c("Method 1", paste0("Method 2", c("a", "b", "c")), "Method 3") 271 | compare.fit 272 | -------------------------------------------------------------------------------- /millsap2004-FIorderedCategorical.R: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------ 2 | # Assessing factorial invariance in ordered-categorical measures 3 | # 4 | # Millsap & Yun-Tein (2004) 5 | # 6 | # Multivariate Behavioral Research 7 | # ------------------------------------------------ 8 | 9 | library("lavaan") 10 | library("MASS") 11 | 12 | # Generating data using Appendix B ---- 13 | 14 | # setting up conditions of the simulation 15 | n1 <- 5000 16 | n2 <- 5000 17 | p <- 6 18 | mu.vec <- rep(0, 6) 19 | sigma.matrix <- diag(6) 20 | lambda.vec <- c(.4, .5, .6, .4, .5, .6) 21 | tau.vec <- rep(c(.25, .50, .70), each = 2) 22 | nu <- matrix(c(-.45, .25, .95, 23 | -.45, .25, .95, 24 | -.30, .50, 1.30, 25 | -.20, .50, 1.20, 26 | 0, .70, 1.40, 27 | -.10, .70, 1.50), byrow = TRUE, ncol = 3) 28 | theta <- .30 29 | nu.two <- matrix(c(-.45, .25, .75, 30 | -.45, .55, 1.10, 31 | -.30, .70, 1.30, 32 | -.20, .50, 1.10, 33 | 0, .40, 1.30, 34 | -.10, .50, 1.60), byrow = TRUE, ncol = 3) 35 | theta.three <- .49 36 | 37 | # step 1, generating a random factor score from a normal density 38 | set.seed(31512) # set seed for reproducibility 39 | grp1.xi <- rnorm(n1, mean = 0, sd = sqrt(1)) 40 | grp2.xi <- rnorm(n2, mean = .25, sd = sqrt(1.2)) 41 | 42 | # step 2, generate p x 1 random vector 43 | grp1.mu <- mvrnorm(5000, mu = mu.vec, Sigma = sigma.matrix) 44 | grp2.mu <- mvrnorm(5000, mu = mu.vec, Sigma = sigma.matrix) 45 | 46 | # step 3, calculate latent response variates 47 | # for true models one and two 48 | x.star1.mod1 <- sweep(grp1.xi %*% t(lambda.vec), 2, tau.vec, "+") + sqrt(theta) * grp1.mu 49 | x.star2.mod1 <- sweep(grp2.xi %*% t(lambda.vec), 2, tau.vec, "+") + sqrt(theta) * grp2.mu 50 | 51 | # true model three and group 2 52 | x.star2.mod3 <- sweep(grp2.xi %*% t(lambda.vec), 2, tau.vec, "+") + sqrt(theta.three) * grp2.mu 53 | 54 | # step 4, calculate observed ordinal variables 55 | xObsFromThreshold <- function(dat, thres){ 56 | xobsMat <- matrix(NA, nrow = nrow(dat), ncol = ncol(dat)) 57 | for(i in 1:6){ 58 | xobsMat[, i] <- ifelse(dat[, i] < thres[i, 1], 0, 59 | ifelse(dat[, i] >= thres[i, 1] & dat[, i] < thres[i, 2], 1, 60 | ifelse(dat[, i] >= thres[i, 2] & dat[, i] < thres[i, 3], 2, 3))) 61 | } 62 | return(xobsMat) 63 | } 64 | # true model one - group 1 & 2 65 | x.obs1.mod1 <- xObsFromThreshold(x.star1.mod1, nu) 66 | x.obs2.mod1 <- xObsFromThreshold(x.star2.mod1, nu) 67 | 68 | # true model two - group 2 69 | x.obs2.mod2 <- xObsFromThreshold(x.star2.mod1, nu.two) 70 | 71 | # true model three 72 | x.obs2.mod3 <- xObsFromThreshold(x.star2.mod3, nu) 73 | 74 | # combine the data sets and convert variables to ordinal 75 | true.m1 <- data.frame(rbind(x.obs1.mod1, x.obs2.mod1)) 76 | true.m1$group <- rep(1:2, each = 5000) 77 | 78 | true.m2 <- data.frame(rbind(x.obs1.mod1, x.obs2.mod2)) 79 | true.m2$group <- rep(1:2, each = 5000) 80 | 81 | true.m3 <- data.frame(rbind(x.obs1.mod1, x.obs2.mod3)) 82 | true.m3$group <- rep(1:2, each = 5000) 83 | 84 | true.m1[,1:6] <- lapply(true.m1[, 1:6], ordered) 85 | true.m2[,1:6] <- lapply(true.m2[, 1:6], ordered) 86 | true.m3[,1:6] <- lapply(true.m3[, 1:6], ordered) 87 | 88 | # Run the models in lavaan ---- 89 | 90 | # define the model 91 | mod <- ' 92 | fac1 =~ X1 + X2 + X3 + X4 + X5 + X6 93 | ' 94 | 95 | # true model one 96 | baseline.fit1 <- cfa(mod, true.m1, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus") 97 | loadings.fit1 <- cfa(mod, true.m1, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = "loadings") 98 | thresholds.fit1 <- cfa(mod, true.m1, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = c("loadings", "thresholds")) 99 | residuals.fit1 <- cfa(mod, true.m1, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = c("loadings", "thresholds", "residuals")) 100 | 101 | table3 <- rbind(fitMeasures(baseline.fit1, 102 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 103 | fitMeasures(loadings.fit1, 104 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 105 | fitMeasures(thresholds.fit1, 106 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 107 | fitMeasures(residuals.fit1, 108 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi"))) 109 | rownames(table3) <- c("Baseline", "Invariant - Loadings", "Invariant - Loadings & Thresholds", "Invariant - Loadings, Thresholds, & Residuals") 110 | table3 111 | anova(baseline.fit1, loadings.fit1) 112 | anova(loadings.fit1, thresholds.fit1) 113 | anova(thresholds.fit1, residuals.fit1) # full invariance found! 114 | summary(residuals.fit1, standardized = TRUE, fit.measures = TRUE) 115 | 116 | # true model two 117 | baseline.fit2 <- cfa(mod, true.m2, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus") 118 | loadings.fit2 <- cfa(mod, true.m2, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = "loadings") 119 | thresholds.fit2 <- cfa(mod, true.m2, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = c("loadings", "thresholds")) 120 | residuals.fit2 <- cfa(mod, true.m2, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = c("loadings", "thresholds", "residuals")) 121 | 122 | table5 <- rbind(fitMeasures(baseline.fit2, 123 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 124 | fitMeasures(loadings.fit2, 125 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 126 | fitMeasures(thresholds.fit2, 127 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 128 | fitMeasures(residuals.fit2, 129 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi"))) 130 | rownames(table5) <- c("Baseline", "Invariant - Loadings", "Invariant - Loadings & Thresholds", "Invariant - Loadings, Thresholds, & Residuals") 131 | table5 132 | anova(baseline.fit2, loadings.fit2) 133 | anova(loadings.fit2, thresholds.fit2) # invariance only of loading found! 134 | anova(thresholds.fit2, residuals.fit2) 135 | summary(loadings.fit2, standardized = TRUE, fit.measures = TRUE) 136 | 137 | # true model three 138 | baseline.fit3 <- cfa(mod, true.m3, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus") 139 | loadings.fit3 <- cfa(mod, true.m3, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = "loadings") 140 | thresholds.fit3 <- cfa(mod, true.m3, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = c("loadings", "thresholds")) 141 | residuals.fit3 <- cfa(mod, true.m3, estimator = "WLS", group = "group", parameterization = "theta", mimic = "Mplus", group.equal = c("loadings", "thresholds", "residuals")) 142 | 143 | table7 <- rbind(fitMeasures(baseline.fit3, 144 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 145 | fitMeasures(loadings.fit3, 146 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 147 | fitMeasures(thresholds.fit3, 148 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi")), 149 | fitMeasures(residuals.fit3, 150 | fit.measures = c("chisq", "df", "pvalue", "rmsea", "cfi"))) 151 | rownames(table7) <- c("Baseline", "Invariant - Loadings", "Invariant - Loadings & Thresholds", "Invariant - Loadings, Thresholds, & Residuals") 152 | table7 153 | anova(baseline.fit3, loadings.fit3) 154 | anova(loadings.fit3, thresholds.fit3) 155 | anova(thresholds.fit3, residuals.fit3) # invariance of thresholds found! 156 | summary(thresholds.fit3, standardized = TRUE, fit.measures = TRUE) 157 | 158 | 159 | 160 | -------------------------------------------------------------------------------- /raykov2018-dichotomousMI.R: -------------------------------------------------------------------------------- 1 | # ------------------------------------------------ 2 | # Examining measurement invariance and differential item functioning with 3 | # discrete latent construct indicators: A note on a multiple testing procedure 4 | # 5 | # Raykov, Dimitrov, Marcoulides, Li, & Menold 6 | # 7 | # Educational and Psychological Measurement 2018 8 | # ------------------------------------------------ 9 | 10 | # convert a factor to a character 11 | factorNumeric <- function(x) as.numeric(as.character(x)) 12 | 13 | library("lavaan") 14 | 15 | # Read in data 16 | masc.math <- read.table("data/masc_math.dat", header = TRUE) 17 | 18 | # Assuming items 1 through 9 are the 9 items 19 | # Note, gender == 0 is male and gender == 1 is female 20 | masc.sub <- subset(masc.math, select = c("gender", paste0("item.", 1:9))) 21 | 22 | cfa.mod <- " 23 | math =~ 1*item.1 + item.2 + item.3 + item.4 + item.5 + 24 | item.6 + item.7 + item.8 + item.9 25 | 26 | math ~ 1 27 | math ~~ math 28 | 29 | item.1 | 1 * t1 30 | item.2 | t1 31 | item.3 | t1 32 | item.4 | t1 33 | item.5 | t1 34 | item.6 | t1 35 | item.7 | t1 36 | item.8 | t1 37 | item.9 | t1 38 | " 39 | 40 | # step 1 - assess acceptable fit boys & girls model 41 | male.fit <- cfa(cfa.mod, data = subset(masc.sub, gender == 0), 42 | ordered = paste0("item.", 1:9), estimator = "WLSMV", parameterization = "theta") 43 | female.fit <- cfa(cfa.mod, data = subset(masc.sub, gender == 1), 44 | ordered = paste0("item.", 1:9), estimator = "WLSMV") 45 | gender.fit <- rbind(fitMeasures(male.fit, c("chisq", "df", "rmsea", "cfi", "tli")), 46 | fitMeasures(female.fit, c("chisq", "df", "rmsea", "cfi", "tli"))) 47 | rownames(gender.fit) <- c("male", "female") 48 | gender.fit 49 | 50 | # Note, the model fits well for both groups, but(!) this is not the clearly not the 51 | # data used in the manuscript 52 | 53 | # step 2 - fit the strong invariance model 54 | strong.mod <- " 55 | group: 1 56 | math =~ lam1 * item.1 + lam2 * item.2 + lam3 * item.3 + lam4 * item.4 + 57 | lam5 * item.5 + lam6 * item.6 + lam7 * item.7 + lam8 * item.8 + 58 | lam9 * item.9 59 | 60 | math ~ 0 61 | math ~~ 1 * math 62 | 63 | item.1 | tau1 * t1 64 | item.2 | tau2 * t1 65 | item.3 | tau3 * t1 66 | item.4 | tau4 * t1 67 | item.5 | tau5 * t1 68 | item.6 | tau6 * t1 69 | item.7 | tau7 * t1 70 | item.8 | tau8 * t1 71 | item.9 | tau9 * t1 72 | 73 | group: 2 74 | math =~ lam1 * item.1 + lam2 * item.2 + lam3 * item.3 + lam4 * item.4 + 75 | lam5 * item.5 + lam6 * item.6 + lam7 * item.7 + lam8 * item.8 + 76 | lam9 * item.9 77 | 78 | math ~ 1 79 | math ~~ var1 * math 80 | 81 | item.1 | tau1 * t1 82 | item.2 | tau2 * t1 83 | item.3 | tau3 * t1 84 | item.4 | tau4 * t1 85 | item.5 | tau5 * t1 86 | item.6 | tau6 * t1 87 | item.7 | tau7 * t1 88 | item.8 | tau8 * t1 89 | item.9 | tau9 * t1 90 | " 91 | strong.fit <- lavaan(strong.mod, data = masc.sub, 92 | ordered = paste0("item.", 1:9), estimator = "WLSMV", group = "gender", parameterization = "theta") 93 | fitMeasures(strong.fit, c("chisq", "df", "rmsea")) 94 | summary(strong.fit, fit.measures = TRUE) 95 | 96 | # step 3 - test invariance of all the parameters 97 | params.to.test <- paste0(rep(c("lam", "tau"), each = 9), 1:9) 98 | param.matrix <- NULL 99 | 100 | for(i in params.to.test){ 101 | tmp.mod <- gsub(paste(i, "\\* "), "", strong.mod) 102 | tmp.fit <- lavaan(tmp.mod, data = masc.sub, 103 | ordered = paste0("item.", 1:9), estimator = "WLSMV", group = "gender", 104 | parameterization = "theta") 105 | test.stat <- lavTestLRT(strong.fit, tmp.fit, method = "satorra.bentler.2001") 106 | param.matrix <- rbind(param.matrix, 107 | cbind(i, 108 | test.stat$`Chisq diff`[2], 109 | 1, 110 | test.stat$`Pr`[2] 111 | )) 112 | } 113 | params.mi <- data.frame(param.matrix) 114 | params.mi[, 2:4] <- lapply(params.mi[, 2:4], factorNumeric) 115 | 116 | # apply BH corections 117 | params.mi$p.adj <- p.adjust(params.mi$V4, method = "BH") 118 | colnames(params.mi) <- c("params", "chisq.diff", "df", "p", "p.adj") 119 | print(params.mi, digits = 3) 120 | --------------------------------------------------------------------------------