==3347074== Memcheck, a memory error detector ==3347074== Copyright (C) 2002-2026, and GNU GPL'd, by Julian Seward et al. ==3347074== Using Valgrind-3.27.1 and LibVEX; rerun with -h for copyright info ==3347074== Command: /data/localhost/ripley/R/R-devel-vg/bin/exec/R --vanilla ==3347074== R Under development (unstable) (2026-10-23 r90751) -- "Unsuffered Consequences" Copyright (C) 2026 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > pkgname <- "slca" > source(file.path(R.home("share"), "R", "examples-header.R")) > options(warn = 1) > library('slca') > > base::assign(".oldSearch", base::search(), pos = 'CheckExEnv') > base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv') > cleanEx() > nameEx("addhealth") > ### * addhealth > > flush(stderr()); flush(stdout()) > > ### Name: addhealth > ### Title: Adolescent Depression Data from the Add Health Study > ### Aliases: addhealth > ### Keywords: datasets > > ### ** Examples > > library(magrittr) > data <- addhealth[1:200,] > lta5 <- slca( + DEP1(5) ~ S1w1 + S2w1 + S3w1 + S4w1 + D1w1 + D2w1 + F1w1 + F2w1, + DEP2(5) ~ S1w2 + S2w2 + S3w2 + S4w2 + D1w2 + D2w2 + F1w2 + F2w2, + DEP1 ~ DEP2 + ) %>% estimate(data, control = list(em.tol = 1e-6)) > lta5inv <- slca( + DEP1(5) ~ S1w1 + S2w1 + S3w1 + S4w1 + D1w1 + D2w1 + F1w1 + F2w1, + DEP2(5) ~ S1w2 + S2w2 + S3w2 + S4w2 + D1w2 + D2w2 + F1w2 + F2w2, + DEP1 ~ DEP2, + constraints = c("DEP1", "DEP2") + ) %>% estimate(data, control = list(em.tol = 1e-6)) > > compare(lta5inv, lta5, test = "chisq") Analysis of Relative Model Fit Model H0: lta5inv Model H1: lta5 Df logLik AIC BIC Gsq Res. Df Pr(>Chi) lta5inv 64 -1401.6 2931.3 3142.4 lta5 104 -1377.7 2963.4 3306.4 47.868 40 0.1837 > lta5inv %>% param() PI : (DEP1) class 1 2 3 4 5 0.1536 0.1877 0.3636 0.0939 0.2013 TAU : (A) parent child 1 2 3 4 5 1 0.2957 0.1914 0.1129 0.1431 0.0763 2 0.0000 0.7665 0.0000 0.0000 0.3376 3 0.7042 0.0421 0.7172 0.1575 0.2401 4 0.0001 0.0001 0.1197 0.4280 0.0125 5 0.0000 0.0000 0.0502 0.2714 0.3335 parent DEP1 child DEP2 RHO : (a) class response 1 2 3 4 5 1(V1) 0.2755 0.7270 0.0533 0.9067 0.1446 2 0.7245 0.2730 0.9467 0.0933 0.8554 1(V2) 0.4112 0.8620 0.0742 0.9455 0.2028 2 0.5888 0.1380 0.9258 0.0545 0.7972 1(V3) 0.6359 0.8097 0.0379 0.4941 0.2756 2 0.3641 0.1903 0.9621 0.5059 0.7244 1(V4) 0.6607 0.9245 0.1139 1.0000 0.3906 2 0.3393 0.0755 0.8861 0.0000 0.6094 1(V5) 0.0834 0.7440 0.0962 0.0847 0.6884 2 0.9166 0.2560 0.9038 0.9153 0.3116 1(V6) 0.0000 0.9410 0.0172 0.2606 0.6804 2 1.0000 0.0590 0.9828 0.7394 0.3196 1(V7) 0.0615 0.5735 0.0000 0.3693 0.1559 2 0.9385 0.4265 1.0000 0.6307 0.8441 1(V8) 0.0000 0.3693 0.0155 0.1817 0.1403 2 1.0000 0.6307 0.9845 0.8183 0.8597 V1 V2 V3 V4 V5 V6 V7 V8 DEP1 S1w1 S2w1 S3w1 S4w1 D1w1 D2w1 F1w1 F2w1 DEP2 S1w2 S2w2 S3w2 S4w2 D1w2 D2w2 F1w2 F2w2 > > > > cleanEx() detaching ‘package:magrittr’ > nameEx("compare") > ### * compare > > flush(stderr()); flush(stdout()) > > ### Name: compare > ### Title: Compare Two Fitted 'slca' Models > ### Aliases: compare > > ### ** Examples > > library(magrittr) > set.seed(20261017) > data <- gss7677[gss7677$COHORT == "YOUNG", ] > stat2 <- slca(status(2) ~ PAPRES + PADEG + MADEG) %>% + estimate(data = data, control = list(verbose = FALSE)) > stat3 <- slca(status(3) ~ PAPRES + PADEG + MADEG) %>% + estimate(data = data, control = list(verbose = FALSE)) > stat4 <- slca(status(4) ~ PAPRES + PADEG + MADEG) %>% + estimate(data = data, control = list(verbose = FALSE)) > > gof(stat2, stat3, stat4) Analysis of Goodness of Fit Table Df logLik AIC BIC Gsq Res. Df stat2 21 -1179.2 2400.4 2489.0 115.980 53 stat3 32 -1139.3 2342.6 2477.7 36.204 42 stat4 43 -1135.4 2356.8 2538.3 28.424 31 > gof(stat2, stat3, stat4, test = "chisq") Analysis of Goodness of Fit Table Df logLik AIC BIC Gsq Res. Df Pr(>Chi) stat2 21 -1179.2 2400.4 2489.0 115.980 53 1.34e-06 *** stat3 32 -1139.3 2342.6 2477.7 36.204 42 0.7225 stat4 43 -1135.4 2356.8 2538.3 28.424 31 0.5992 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > compare(stat3, stat4) Analysis of Relative Model Fit Model H0: stat3 Model H1: stat4 Df logLik AIC BIC Gsq Res. Df stat3 32 -1139.3 2342.6 2477.7 stat4 43 -1135.4 2356.8 2538.3 7.7805 11 > compare(stat3, stat4, test = "chisq") Analysis of Relative Model Fit Model H0: stat3 Model H1: stat4 Df logLik AIC BIC Gsq Res. Df Pr(>Chi) stat3 32 -1139.3 2342.6 2477.7 stat4 43 -1135.4 2356.8 2538.3 7.7805 11 0.7328 > > > > cleanEx() detaching ‘package:magrittr’ > nameEx("confint.slcafit") > ### * confint.slcafit > > flush(stderr()); flush(stdout()) > > ### Name: confint.slcafit > ### Title: Confidence Intervals for Model Parameters > ### Aliases: confint.slcafit > > ### ** Examples > > param(nlsy_jlcpa, index = TRUE) PI : (PROF) class 1 2 3 4 0.2790 (1) 0.2146 (2) 0.2869 (3) 0.2195 (4) TAU : (A) parent child 1 2 3 4 1 0.0000 (5) 0.0000 (10) 0.1745 (15) 0.0000 (20) 2 0.0901 (6) 0.4456 (11) 0.1939 (16) 0.0403 (21) 3 0.3771 (7) 0.1675 (12) 0.1825 (17) 0.7018 (22) 4 0.2620 (8) 0.0000 (13) 0.0000 (18) 0.0813 (23) 5 0.2709 (9) 0.3869 (14) 0.4491 (19) 0.1766 (24) parent PROF child SUB_98 (B) parent child 1 2 3 4 1 0.0000 (25) 0.0114 (30) 0.8719 (35) 0.0000 (40) 2 0.0000 (26) 0.9617 (31) 0.0000 (36) 0.0079 (41) 3 0.0000 (27) 0.0000 (32) 0.0965 (37) 0.9210 (42) 4 0.9941 (28) 0.0000 (33) 0.0000 (38) 0.0711 (43) 5 0.0059 (29) 0.0269 (34) 0.0316 (39) 0.0000 (44) parent PROF child SUB_03 (C) parent child 1 2 3 4 1 0.0019 (45) 0.1821 (50) 0.8024 (55) 0.0319 (60) 2 0.0097 (46) 0.7202 (51) 0.0000 (56) 0.0000 (61) 3 0.0573 (47) 0.0442 (52) 0.1844 (57) 0.7168 (62) 4 0.9311 (48) 0.0356 (53) 0.0084 (58) 0.2473 (63) 5 0.0000 (49) 0.0179 (54) 0.0048 (59) 0.0040 (64) parent PROF child SUB_08 (D) parent child 1 2 3 4 5 1 0.0254 (65) 0.0457 (68) 0.9505 (71) 0.7958 (74) 0.1613 (77) 2 0.7516 (66) 0.6690 (69) 0.0051 (72) 0.0000 (75) 0.1631 (78) 3 0.2230 (67) 0.2853 (70) 0.0444 (73) 0.2042 (76) 0.6755 (79) parent SUB_98 SUB_03 SUB_08 child SMK_98 SMK_03 SMK_08 (E) parent child 1 2 3 4 5 1 0.5664 (80) 0.8533 (83) 0.0335 (86) 0.7521 (89) 0.2872 (92) 2 0.2229 (81) 0.1264 (84) 0.2589 (87) 0.2414 (90) 0.6127 (93) 3 0.2107 (82) 0.0203 (85) 0.7075 (88) 0.0065 (91) 0.1001 (94) parent SUB_98 SUB_03 SUB_08 child DRK_98 DRK_03 DRK_08 (F) parent child 1 2 3 4 5 1 0.0871 (95) 0.1442 (98) 0.0317 (101) 0.1525 (104) 0.7278 (107) 2 0.0617 (96) 0.8387 (99) 0.0095 (102) 0.2302 (105) 0.1258 (108) 3 0.8512 (97) 0.0170 (100) 0.9588 (103) 0.6173 (106) 0.1463 (109) parent SUB_98 SUB_03 SUB_08 child MRJ_98 MRJ_03 MRJ_08 RHO : (a) class response 1 2 3 1(V1) 0.0134 (110) 1.0000 (118) 1.0000 (126) 2 0.9866 (111) 0.0000 (119) 0.0000 (127) 1(V2) 0.0000 (112) 1.0000 (120) 0.6701 (128) 2 1.0000 (113) 0.0000 (121) 0.3299 (129) 1(V3) 0.0000 (114) 0.8833 (122) 0.0000 (130) 2 1.0000 (115) 0.1167 (123) 1.0000 (131) 1(V4) 0.0000 (116) 0.6460 (124) 0.0000 (132) 2 1.0000 (117) 0.3540 (125) 1.0000 (133) V1 V2 V3 V4 SMK_98 ESMK_98 FSMK_98 DSMK_98 HSMK_98 SMK_03 ESMK_03 FSMK_03 DSMK_03 HSMK_03 SMK_08 ESMK_08 FSMK_08 DSMK_08 HSMK_08 (b) class response 1 2 3 1(V1) 1.0000 (134) 1.0000 (142) 0.0759 (150) 2 0.0000 (135) 0.0000 (143) 0.9241 (151) 1(V2) 1.0000 (136) 0.5760 (144) 0.0000 (152) 2 0.0000 (137) 0.4240 (145) 1.0000 (153) 1(V3) 0.6934 (138) 0.0000 (146) 0.0000 (154) 2 0.3066 (139) 1.0000 (147) 1.0000 (155) 1(V4) 0.8061 (140) 0.0000 (148) 0.0000 (156) 2 0.1939 (141) 1.0000 (149) 1.0000 (157) V1 V2 V3 V4 DRK_98 EDRK_98 CDRK_98 WDRK_98 BDRK_98 DRK_03 EDRK_03 CDRK_03 WDRK_03 BDRK_03 DRK_08 EDRK_08 CDRK_08 WDRK_08 BDRK_08 (c) class response 1 2 3 1(V1) 0.5189 (158) 1.0000 (166) 0.0002 (174) 2 0.4811 (159) 0.0000 (167) 0.9998 (175) 1(V2) 0.0000 (160) 1.0000 (168) 0.0000 (176) 2 1.0000 (161) 0.0000 (169) 1.0000 (177) 1(V3) 0.0000 (162) 0.5259 (170) 0.0000 (178) 2 1.0000 (163) 0.4741 (171) 1.0000 (179) 1(V4) 0.0000 (164) 0.3720 (172) 0.0000 (180) 2 1.0000 (165) 0.6280 (173) 1.0000 (181) V1 V2 V3 V4 MRJ_98 EMRJ_98 CMRJ_98 OMRJ_98 SMRJ_98 MRJ_03 EMRJ_03 CMRJ_03 OMRJ_03 SMRJ_03 MRJ_08 EMRJ_08 CMRJ_08 OMRJ_08 SMRJ_08 > confint(nlsy_jlcpa) 2.5 % 97.5 % (1) 2.196632e-01 3.384007e-01 (2) 1.708777e-01 2.582819e-01 (3) 2.377812e-01 3.359991e-01 (4) 1.607946e-01 2.782015e-01 (5) 1.316219e-147 2.962555e-147 (6) 2.865600e-02 1.514643e-01 (7) 2.817499e-01 4.724122e-01 (8) 1.577645e-01 3.661524e-01 (9) 1.666665e-01 3.751342e-01 (10) 7.265367e-18 1.356439e-17 (11) 3.351151e-01 5.560190e-01 (12) 9.349363e-02 2.415669e-01 (13) 3.052471e-09 5.698944e-09 (14) 2.699013e-01 5.039040e-01 (15) 8.636855e-02 2.625399e-01 (16) 1.136580e-01 2.741749e-01 (17) 1.163294e-01 2.487285e-01 (18) 2.414352e-25 4.143108e-25 (19) 3.307031e-01 5.674976e-01 (20) 7.034261e-81 2.663338e-80 (21) -8.554393e-03 8.913669e-02 (22) 6.014447e-01 8.021353e-01 (23) -5.208181e-03 1.679023e-01 (24) 7.378316e-02 2.793605e-01 (25) -3.365407e-26 4.112143e-26 (26) -2.996881e-75 3.661846e-75 (27) -9.303904e-07 1.136831e-06 (28) 9.351496e-01 1.053074e+00 (29) -5.307307e-02 6.484923e-02 (30) -9.982077e-02 1.226243e-01 (31) 8.482744e-01 1.075050e+00 (32) 1.523339e-199 1.523339e-199 (33) -8.289540e-100 1.757511e-99 (34) -4.809295e-02 1.019645e-01 (35) 7.829478e-01 9.608499e-01 (36) -1.195434e-11 3.513308e-11 (37) 2.886429e-02 1.641170e-01 (38) -4.341679e-82 1.275993e-81 (39) -3.260598e-02 9.582698e-02 (40) -4.963485e-26 1.642774e-25 (41) -3.376576e-02 4.962831e-02 (42) 7.269635e-01 1.114959e+00 (43) -1.217313e-01 2.639468e-01 (44) -8.170873e-49 2.704329e-48 (45) -1.934174e-02 2.313701e-02 (46) -1.574984e-02 3.515286e-02 (47) -6.158788e-02 1.760997e-01 (48) 8.071224e-01 1.055167e+00 (49) -4.583996e-129 9.547265e-129 (50) 6.279219e-02 3.014505e-01 (51) 5.887958e-01 8.516152e-01 (52) -8.285468e-03 9.661303e-02 (53) -1.985109e-02 9.110167e-02 (54) -3.202804e-02 6.779624e-02 (55) 7.123386e-01 8.925108e-01 (56) -6.418826e-39 7.687447e-39 (57) 1.170281e-01 2.517426e-01 (58) -4.374604e-02 6.054986e-02 (59) -4.845173e-02 5.802777e-02 (60) -2.924168e-02 9.305138e-02 (61) 7.216797e-232 7.216797e-232 (62) 6.056283e-01 8.279084e-01 (63) 1.390506e-01 3.556173e-01 (64) -3.099270e-02 3.897843e-02 (65) -1.939799e-02 7.022763e-02 (66) 6.818585e-01 8.214010e-01 (67) 1.666418e-01 2.792690e-01 (68) 1.832904e-02 7.314001e-02 (69) 6.162033e-01 7.218193e-01 (70) 2.371028e-01 3.334056e-01 (71) 9.163161e-01 9.846204e-01 (72) -8.141767e-03 1.830527e-02 (73) 1.412244e-02 7.477748e-02 (74) 7.491208e-01 8.425406e-01 (75) 1.196215e-11 1.905923e-11 (76) 1.574594e-01 2.508792e-01 (77) 8.931824e-02 2.333613e-01 (78) 9.639739e-02 2.298591e-01 (79) 5.870730e-01 7.639909e-01 (80) 5.133754e-01 6.193661e-01 (81) 1.727128e-01 2.730651e-01 (82) 1.682509e-01 2.532297e-01 (83) 8.074661e-01 8.991574e-01 (84) 8.326637e-02 1.695867e-01 (85) 2.061184e-03 3.846230e-02 (86) -1.991548e-02 8.701094e-02 (87) 1.836337e-01 3.342563e-01 (88) 6.194496e-01 7.955649e-01 (89) 6.997628e-01 8.044282e-01 (90) 1.919496e-01 2.909129e-01 (91) -1.021852e-02 2.316502e-02 (92) 2.049584e-01 3.694083e-01 (93) 5.265049e-01 6.988820e-01 (94) 4.371735e-02 1.565291e-01 (95) 1.996969e-02 1.541847e-01 (96) 1.501435e-02 1.084684e-01 (97) 7.695210e-01 9.328419e-01 (98) 7.254894e-02 2.159447e-01 (99) 7.496561e-01 9.278350e-01 (100) -1.313749e-02 4.715276e-02 (101) 2.213799e-03 6.109436e-02 (102) -6.743734e-04 1.971026e-02 (103) 9.271295e-01 9.905265e-01 (104) 7.665047e-02 2.283733e-01 (105) 1.953820e-01 2.649226e-01 (106) 5.368540e-01 6.978177e-01 (107) 3.927324e-01 1.062935e+00 (108) 5.567487e-02 1.959617e-01 (109) -1.837854e-01 4.764817e-01 (110) -7.588212e-03 3.436836e-02 (111) 9.656316e-01 1.007588e+00 (112) 0.000000e+00 0.000000e+00 (113) 1.000000e+00 1.000000e+00 (114) 0.000000e+00 0.000000e+00 (115) 1.000000e+00 1.000000e+00 (116) 0.000000e+00 0.000000e+00 (117) 1.000000e+00 1.000000e+00 (118) 1.000000e+00 1.000000e+00 (119) 0.000000e+00 0.000000e+00 (120) 1.000000e+00 1.000000e+00 (121) 0.000000e+00 0.000000e+00 (122) 8.542618e-01 9.122978e-01 (123) 8.770220e-02 1.457382e-01 (124) 6.151722e-01 6.769268e-01 (125) 3.230732e-01 3.848278e-01 (126) 1.000000e+00 1.000000e+00 (127) 0.000000e+00 0.000000e+00 (128) 6.184276e-01 7.216748e-01 (129) 2.783252e-01 3.815724e-01 (130) 0.000000e+00 0.000000e+00 (131) 1.000000e+00 1.000000e+00 (132) 0.000000e+00 0.000000e+00 (133) 1.000000e+00 1.000000e+00 (134) 1.000000e+00 1.000000e+00 (135) 0.000000e+00 0.000000e+00 (136) 1.000000e+00 1.000000e+00 (137) 0.000000e+00 0.000000e+00 (138) 6.665516e-01 7.202240e-01 (139) 2.797760e-01 3.334484e-01 (140) 7.827662e-01 8.294459e-01 (141) 1.705541e-01 2.172338e-01 (142) 1.000000e+00 1.000000e+00 (143) 0.000000e+00 0.000000e+00 (144) 5.150970e-01 6.369518e-01 (145) 3.630482e-01 4.849030e-01 (146) 2.266886e-173 2.266886e-173 (147) 1.000000e+00 1.000000e+00 (148) 3.659486e-127 3.659486e-127 (149) 1.000000e+00 1.000000e+00 (150) -4.844300e-03 1.567197e-01 (151) 8.432803e-01 1.004844e+00 (152) 0.000000e+00 0.000000e+00 (153) 1.000000e+00 1.000000e+00 (154) 0.000000e+00 0.000000e+00 (155) 1.000000e+00 1.000000e+00 (156) 0.000000e+00 0.000000e+00 (157) 1.000000e+00 1.000000e+00 (158) 3.129676e-01 7.248330e-01 (159) 2.751670e-01 6.870324e-01 (160) 9.689486e-106 9.689486e-106 (161) 1.000000e+00 1.000000e+00 (162) 0.000000e+00 0.000000e+00 (163) 1.000000e+00 1.000000e+00 (164) 0.000000e+00 0.000000e+00 (165) 1.000000e+00 1.000000e+00 (166) 1.000000e+00 1.000000e+00 (167) 0.000000e+00 0.000000e+00 (168) 1.000000e+00 1.000000e+00 (169) 0.000000e+00 0.000000e+00 (170) 4.825718e-01 5.691765e-01 (171) 4.308235e-01 5.174282e-01 (172) 3.318994e-01 4.121566e-01 (173) 5.878434e-01 6.681006e-01 (174) 2.279783e-04 2.354243e-04 (175) 9.997646e-01 9.997720e-01 (176) 0.000000e+00 0.000000e+00 (177) 1.000000e+00 1.000000e+00 (178) 0.000000e+00 0.000000e+00 (179) 1.000000e+00 1.000000e+00 (180) 0.000000e+00 0.000000e+00 (181) 1.000000e+00 1.000000e+00 > confint(nlsy_jlcpa, 1:4) 2.5 % 97.5 % (1) 0.2196632 0.3384007 (2) 0.1708777 0.2582819 (3) 0.2377812 0.3359991 (4) 0.1607946 0.2782015 > > > > cleanEx() > nameEx("estimate") > ### * estimate > > flush(stderr()); flush(stdout()) > > ### Name: estimate > ### Title: Estimate Parameters of an 'slca' Object > ### Aliases: estimate estimate.slca > > ### ** Examples > > m <- slca(lc[3] ~ y1 + y2 + y3 + y4) > pi <- rep(1 / 3, 3) > rho <- c(.9, .1, .9, .1, .9, .1, .9, .1, # class 1 + .9, .1, .9, .1, .1, .9, .1, .9, # class 2 + .1, .9, .1, .9, .1, .9, .1, .9) # class 3 > dt <- simulate(m, 200, parm = c(pi, rho)) > estimate(m, dt$response) Structural Latent Variable Model Latent variables (Root*): Label: lc* nclass: 3 Measurement model: lc -> { y1, y2, y3, y4 } a LogLik: -427.8788> > # Several estimation methods > estimate(m, dt$response, method = "em", + control = slcaControl(verbose = TRUE)) # default EM iteration begin. 1 iterations logLik: -553.25 diff: inf 2 iterations logLik: -522.52 diff: 30.73754 3 iterations logLik: -521.93 diff: 0.59020 4 iterations logLik: -519.38 diff: 2.55066 5 iterations logLik: -509.82 diff: 9.55268 6 iterations logLik: -487.48 diff: 22.34057 7 iterations logLik: -467.65 diff: 19.82896 8 iterations logLik: -460.13 diff: 7.52590 9 iterations logLik: -452.91 diff: 7.21634 10 iterations logLik: -442.42 diff: 10.49271 11 iterations logLik: -433.83 diff: 8.58685 12 iterations logLik: -430.63 diff: 3.19686 13 iterations logLik: -430.03 diff: 0.60020 14 iterations logLik: -429.94 diff: 0.09607 15 iterations logLik: -429.90 diff: 0.03872 16 iterations logLik: -429.87 diff: 0.03469 17 iterations logLik: -429.83 diff: 0.03581 18 iterations logLik: -429.79 diff: 0.03752 19 iterations logLik: -429.75 diff: 0.03941 20 iterations logLik: -429.71 diff: 0.04145 21 iterations logLik: -429.67 diff: 0.04362 22 iterations logLik: -429.62 diff: 0.04589 23 iterations logLik: -429.57 diff: 0.04824 24 iterations logLik: -429.52 diff: 0.05062 25 iterations logLik: -429.47 diff: 0.05300 26 iterations logLik: -429.41 diff: 0.05531 27 iterations logLik: -429.36 diff: 0.05751 28 iterations logLik: -429.30 diff: 0.05953 29 iterations logLik: -429.24 diff: 0.06132 30 iterations logLik: -429.17 diff: 0.06280 31 iterations logLik: -429.11 diff: 0.06394 32 iterations logLik: -429.04 diff: 0.06467 33 iterations logLik: -428.98 diff: 0.06498 34 iterations logLik: -428.91 diff: 0.06484 35 iterations logLik: -428.85 diff: 0.06425 36 iterations logLik: -428.79 diff: 0.06321 37 iterations logLik: 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Structural Latent Variable Model Latent variables (Root*): Label: lc* nclass: 3 Measurement model: lc -> { y1, y2, y3, y4 } a LogLik: -427.8788> estimate(m, dt$response, method = "nlm", + control = slcaControl(verbose = TRUE)) nlm iteration begin. .. done. Structural Latent Variable Model Latent variables (Root*): Label: lc* nclass: 3 Measurement model: lc -> { y1, y2, y3, y4 } a LogLik: -427.8788> estimate(m, dt$response, method = "hybrid", + control = slcaControl(verbose = TRUE)) EM iteration begin. 1 iterations logLik: -552.77 diff: inf 2 iterations logLik: -522.57 diff: 30.19606 3 iterations logLik: -522.14 diff: 0.42830 4 iterations logLik: -520.27 diff: 1.87431 5 iterations logLik: -512.87 diff: 7.39935 6 iterations logLik: -492.77 diff: 20.09398 7 iterations logLik: -469.49 diff: 23.28377 8 iterations logLik: -459.07 diff: 10.42084 9 iterations logLik: -451.53 diff: 7.53782 10 iterations logLik: -441.94 diff: 9.58951 11 iterations logLik: -433.81 diff: 8.13171 12 iterations logLik: -430.51 diff: 3.30056 13 iterations logLik: -429.84 diff: 0.66797 14 iterations logLik: -429.70 diff: 0.14603 15 iterations logLik: -429.61 diff: 0.08725 16 iterations logLik: -429.53 diff: 0.08177 17 iterations logLik: -429.44 diff: 0.08180 18 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Structural Latent Variable Model Latent variables (Root*): Label: lc* nclass: 3 Measurement model: lc -> { y1, y2, y3, y4 } a LogLik: -427.8788> > # Parameter restriction > mf <- estimate(m, dt$response) > param(mf, index = TRUE) PI : (lc) class 1 2 3 0.2839 (1) 0.2949 (2) 0.4212 (3) RHO : (a) class response 1 2 3 1(V1) 1.0000 (4) 0.8542 (12) 0.2237 (20) 2 0.0000 (5) 0.1458 (13) 0.7763 (21) 1(V2) 1.0000 (6) 0.8655 (14) 0.0733 (22) 2 0.0000 (7) 0.1345 (15) 0.9267 (23) 1(V3) 0.1743 (8) 0.7968 (16) 0.0844 (24) 2 0.8257 (9) 0.2032 (17) 0.9156 (25) 1(V4) 0.0000 (10) 1.0000 (18) 0.1190 (26) 2 1.0000 (11) 0.0000 (19) 0.8810 (27) V1 V2 V3 V4 lc y1 y2 y3 y4 > mf0 <- estimate(mf, fix2zero = c(4, 6, 8, 10)) > param(mf0) PI : (lc) class 1 2 3 0.2204 0.2156 0.5641 RHO : (a) class response 1 2 3 1(V1) 0.0000 0.8665 0.7858 2 1.0000 0.1335 0.2142 1(V2) 0.0000 0.8768 0.6754 2 1.0000 0.1232 0.3246 1(V3) 0.0000 1.0000 0.1851 2 1.0000 0.0000 0.8149 1(V4) 0.0000 1.0000 0.2295 2 1.0000 0.0000 0.7705 V1 V2 V3 V4 lc y1 y2 y3 y4 > > # Estimation control > estimate(m, dt$response, control = slcaControl(nrep = 3, verbose = TRUE)) Inital parameter test: 1 / 3 logLik: -427.8788 2 / 3 logLik: -427.8788 3 / 3 logLik: -427.8788 1th parameter set has been selected. EM iteration begin. 1 iterations logLik: -427.88 diff: inf 2 iterations logLik: -427.88 diff: 2.7e-12 .. done. Structural Latent Variable Model Latent variables (Root*): Label: lc* nclass: 3 Measurement model: lc -> { y1, y2, y3, y4 } a LogLik: -427.8788> estimate(m, dt$response, control = slcaControl(init.param = c(pi, rho))) Structural Latent Variable Model Latent variables (Root*): Label: lc* nclass: 3 Measurement model: lc -> { y1, y2, y3, y4 } a LogLik: -427.8788> > > > cleanEx() > nameEx("gof") > ### * gof > > flush(stderr()); flush(stdout()) > > ### Name: gof > ### Title: Goodness-of-Fit Test for Fitted 'slca' Model > ### Aliases: gof gof.slcafit > > ### ** Examples > > library(magrittr) > set.seed(20261017) > data <- gss7677[gss7677$COHORT == "YOUNG", ] > stat2 <- slca(status(2) ~ PAPRES + PADEG + MADEG) %>% + estimate(data = data, control = list(verbose = FALSE)) > stat3 <- slca(status(3) ~ PAPRES + PADEG + MADEG) %>% + estimate(data = data, control = list(verbose = FALSE)) > stat4 <- slca(status(4) ~ PAPRES + PADEG + MADEG) %>% + estimate(data = data, control = list(verbose = FALSE)) > > gof(stat2, stat3, stat4) Analysis of Goodness of Fit Table Df logLik AIC BIC Gsq Res. Df stat2 21 -1179.2 2400.4 2489.0 115.980 53 stat3 32 -1139.3 2342.6 2477.7 36.204 42 stat4 43 -1135.4 2356.8 2538.3 28.424 31 > gof(stat2, stat3, stat4, test = "chisq") Analysis of Goodness of Fit Table Df logLik AIC BIC Gsq Res. Df Pr(>Chi) stat2 21 -1179.2 2400.4 2489.0 115.980 53 1.34e-06 *** stat3 32 -1139.3 2342.6 2477.7 36.204 42 0.7225 stat4 43 -1135.4 2356.8 2538.3 28.424 31 0.5992 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > compare(stat3, stat4) Analysis of Relative Model Fit Model H0: stat3 Model H1: stat4 Df logLik AIC BIC Gsq Res. Df stat3 32 -1139.3 2342.6 2477.7 stat4 43 -1135.4 2356.8 2538.3 7.7805 11 > compare(stat3, stat4, test = "chisq") Analysis of Relative Model Fit Model H0: stat3 Model H1: stat4 Df logLik AIC BIC Gsq Res. Df Pr(>Chi) stat3 32 -1139.3 2342.6 2477.7 stat4 43 -1135.4 2356.8 2538.3 7.7805 11 0.7328 > > > > cleanEx() detaching ‘package:magrittr’ > nameEx("gss7677") > ### * gss7677 > > flush(stderr()); flush(stdout()) > > ### Name: gss7677 > ### Title: GSS 1976-1977 Data on Social Status and Tolerance towards > ### Minorities > ### Aliases: gss7677 > ### Keywords: datasets > > ### ** Examples > > library(magrittr) > gss500 <- gss7677[1:500,] %>% na.omit > model_stat <- slca(status(3) ~ PAPRES + PADEG + MADEG) %>% + estimate(data = gss500, control = list(em.tol = 1e-6)) > summary(model_stat) Structural Latent Class Model Summary of model structure Number of manifest variables 3 Number of latent class variables 1 Latent variables (Root*): Label: status* nclass: 3 Measurement model: status -> { PAPRES, PADEG, MADEG } a Summary of manifest variables Categories for each variable: response 1 2 3 4 5 PAPRES LOW MIDIUM HIGH PADEG LT-HS HIGH-SCH COLLEGE BACHELOR GRADUATE MADEG LT-HS HIGH-SCH COLLEGE BACHELOR GRADUATE Frequencies for each categories: response 1 2 3 4 5 PAPRES 62 102 19 0 PADEG 104 63 11 5 0 MADEG 86 84 4 8 1 0 Summary of model fit Number of observations 183 Number of free parameters 32 Log-likelihood -477.892 Information criteria Akaike (AIC) 1019.785 Bayesian (BIC) 1122.488 Chi-squared Tests Residual degree of freedom (df) 42 Pearson Chi-squared (X-squared) 21.976 P(>Chi) 0.995 Likelihood Ratio (G-squared) 23.705 P(>Chi) 0.990 > param(model_stat) PI : (status) class 1 2 3 0.3817 0.4289 0.1893 RHO : (a) class response 1 2 3 1(V1) 0.3704 0.4602 0.0000 2 0.5901 0.4777 0.6718 3 0.0395 0.0621 0.3282 1(V2) 0.9942 0.4401 0.0000 2 0.0000 0.5344 0.6076 3 0.0000 0.0000 0.0000 4 0.0000 0.0255 0.2598 5 0.0058 0.0000 0.1326 1(V3) 0.9929 0.1460 0.1496 2 0.0000 0.8104 0.5884 3 0.0000 0.0000 0.1155 4 0.0071 0.0436 0.1177 5 0.0000 0.0000 0.0289 V1 V2 V3 status PAPRES PADEG MADEG > > model_tol <- slca(tol(4) ~ TOLRAC + TOLCOM + TOLHOMO + TOLATH + TOLMIL) %>% + estimate(data = gss500, control = list(em.tol = 1e-6)) > summary(model_tol) Structural Latent Class Model Summary of model structure Number of manifest variables 5 Number of latent class variables 1 Latent variables (Root*): Label: tol* nclass: 4 Measurement model: tol -> { TOLRAC, TOLCOM, TOLHOMO, TOLATH, TOLMIL } a Summary of manifest variables Categories for each variable: response 1 2 TOLRAC TOLERANT INTOLERANT TOLCOM TOLERANT INTOLERANT TOLHOMO TOLERANT INTOLERANT TOLATH TOLERANT INTOLERANT TOLMIL TOLERANT INTOLERANT Frequencies for each categories: response 1 2 TOLRAC 80 103 0 TOLCOM 78 105 0 TOLHOMO 92 91 0 TOLATH 88 95 0 TOLMIL 83 100 0 Summary of model fit Number of observations 183 Number of free parameters 23 Log-likelihood -439.750 Information criteria Akaike (AIC) 925.500 Bayesian (BIC) 999.318 Chi-squared Tests Residual degree of freedom (df) 8 Pearson Chi-squared (X-squared) 10.766 P(>Chi) 0.215 Likelihood Ratio (G-squared) 11.299 P(>Chi) 0.185 > param(model_tol) PI : (tol) class 1 2 3 4 0.2059 0.3590 0.3570 0.0782 RHO : (a) class response 1 2 3 4 1(V1) 0.0000 1.0000 0.0000 1.0000 2 1.0000 0.0000 1.0000 0.0000 1(V2) 0.3840 0.8620 0.0541 0.2359 2 0.6160 0.1380 0.9459 0.7641 1(V3) 0.7366 0.8924 0.0192 0.3059 2 0.2634 0.1076 0.9808 0.6941 1(V4) 0.4846 0.9536 0.0114 0.4442 2 0.5154 0.0464 0.9886 0.5558 1(V5) 0.3237 0.9894 0.0889 0.0000 2 0.6763 0.0106 0.9111 1.0000 V1 V2 V3 V4 V5 tol TOLRAC TOLCOM TOLHOMO TOLATH TOLMIL > > model_lta <- slca( + status(3) ~ PAPRES + PADEG + MADEG, + tol(4) ~ TOLRAC + TOLCOM + TOLHOMO + TOLATH + TOLMIL, + status ~ tol + ) %>% estimate(data = gss500, control = list(em.tol = 1e-6)) > summary(model_lta) Structural Latent Class Model Summary of model structure Number of manifest variables 8 Number of latent class variables 2 Latent variables (Root*): Label: status* tol nclass: 3 4 Measurement model: status -> { PAPRES, PADEG, MADEG } a tol -> { TOLRAC, TOLCOM, TOLHOMO, TOLATH, TOLMIL } b Structural model: status -> { tol } Dependency constraints: A status -> tol Tree of structural model: status -> tol Summary of manifest variables Categories for each variable: response 1 2 3 4 5 PAPRES LOW MIDIUM HIGH PADEG LT-HS HIGH-SCH COLLEGE BACHELOR GRADUATE MADEG LT-HS HIGH-SCH COLLEGE BACHELOR GRADUATE TOLRAC TOLERANT INTOLERANT TOLCOM TOLERANT INTOLERANT TOLHOMO TOLERANT INTOLERANT TOLATH TOLERANT INTOLERANT TOLMIL TOLERANT INTOLERANT Frequencies for each categories: response 1 2 3 4 5 PAPRES 62 102 19 0 PADEG 104 63 11 5 0 MADEG 86 84 4 8 1 0 TOLRAC 80 103 0 TOLCOM 78 105 0 TOLHOMO 92 91 0 TOLATH 88 95 0 TOLMIL 83 100 0 Summary of model fit Number of observations 183 Number of free parameters 61 Log-likelihood -897.906 Information criteria Akaike (AIC) 1917.812 Bayesian (BIC) 2113.590 Chi-squared Tests Residual degree of freedom (df) 2338 Pearson Chi-squared (X-squared) 641.025 P(>Chi) 1.000 Likelihood Ratio (G-squared) 276.662 P(>Chi) 1.000 > param(model_lta) PI : (status) class 1 2 3 0.3005 0.2801 0.4195 TAU : (A) parent child 1 2 3 1 0.4188 0.0000 0.5962 2 0.2933 0.5879 0.2134 3 0.2879 0.1306 0.0000 4 0.0000 0.2815 0.1904 parent status child tol RHO : (a) class response 1 2 3 1(V1) 0.5100 0.1527 0.3404 2 0.2670 0.7159 0.6596 3 0.2231 0.1314 0.0000 1(V2) 0.5148 0.0000 0.9861 2 0.4117 0.7876 0.0000 3 0.0000 0.0000 0.0000 4 0.0000 0.1938 0.0139 5 0.0735 0.0187 0.0000 1(V3) 0.4286 0.0000 0.8133 2 0.5438 0.8062 0.1664 3 0.0000 0.0780 0.0000 4 0.0276 0.0962 0.0202 5 0.0000 0.0195 0.0000 V1 V2 V3 status PAPRES PADEG MADEG (b) class response 1 2 3 4 1(V1) 0.0560 0.9901 0.3644 0.2038 2 0.9440 0.0099 0.6356 0.7962 1(V2) 0.0069 0.8766 0.2639 0.5741 2 0.9931 0.1234 0.7361 0.4259 1(V3) 0.0846 0.9074 0.6506 0.5056 2 0.9154 0.0926 0.3494 0.4944 1(V4) 0.0000 0.9677 1.0000 0.1673 2 1.0000 0.0323 0.0000 0.8327 1(V5) 0.0761 1.0000 0.2231 0.3478 2 0.9239 0.0000 0.7769 0.6522 V1 V2 V3 V4 V5 tol TOLRAC TOLCOM TOLHOMO TOLATH TOLMIL > > > > > cleanEx() detaching ‘package:magrittr’ > nameEx("nlsy97") > ### * nlsy97 > > flush(stderr()); flush(stdout()) > > ### Name: nlsy97 > ### Title: NLSY97 Substance Use Data > ### Aliases: nlsy97 > ### Keywords: datasets > > ### ** Examples > > library(magrittr) > nlsy_smoke <- slca(SMK_98(3) ~ ESMK_98 + FSMK_98 + DSMK_98 + HSMK_98) %>% + estimate(data = nlsy97, control = list(verbose = FALSE)) > summary(nlsy_smoke) Structural Latent Class Model Summary of model structure Number of manifest variables 4 Number of latent class variables 1 Latent variables (Root*): Label: SMK_98* nclass: 3 Measurement model: SMK_98 -> { ESMK_98, FSMK_98, DSMK_98, HSMK_98 } a Summary of manifest variables Categories for each variable: response 1 2 ESMK_98 Yes No FSMK_98 Yes No DSMK_98 Yes No HSMK_98 Yes No Frequencies for each categories: response 1 2 ESMK_98 558 446 0 FSMK_98 413 591 0 DSMK_98 179 825 0 HSMK_98 115 889 0 Summary of model fit Number of observations 1004 Number of free parameters 14 Log-likelihood -1536.221 Information criteria Akaike (AIC) 3100.442 Bayesian (BIC) 3169.207 Chi-squared Tests Residual degree of freedom (df) 1 Pearson Chi-squared (X-squared) 118.589 P(>Chi) 0.000 Likelihood Ratio (G-squared) 125.712 P(>Chi) 0.000 > > > # A reproducible subset and two-class models keep these syntax examples small. > # Choose class counts and fit the full data separately for substantive inference. > set.seed(20261017) > example_data <- nlsy97[sample.int(nrow(nlsy97), 500), ] > # JLCA > model_jlca <- slca( + SMK_98(2) ~ ESMK_98 + FSMK_98 + DSMK_98 + HSMK_98, + DRK_98(2) ~ EDRK_98 + CDRK_98 + WDRK_98 + BDRK_98, + MRJ_98(2) ~ EMRJ_98 + CMRJ_98 + OMRJ_98 + SMRJ_98, + SUB_98(2) ~ SMK_98 + DRK_98 + MRJ_98 + ) %>% estimate(data = example_data, control = slcaControl(em.tol = 1e-6)) > summary(model_jlca) Structural Latent Class Model Summary of model structure Number of manifest variables 12 Number of latent class variables 4 Latent variables (Root*): Label: SMK_98 DRK_98 MRJ_98 SUB_98* nclass: 2 2 2 2 Measurement model: SMK_98 -> { ESMK_98, FSMK_98, DSMK_98, HSMK_98 } a DRK_98 -> { EDRK_98, CDRK_98, WDRK_98, BDRK_98 } b MRJ_98 -> { EMRJ_98, CMRJ_98, OMRJ_98, SMRJ_98 } c Structural model: SUB_98 -> { SMK_98, DRK_98, MRJ_98 } Dependency constraints: A B C SUB_98 -> SMK_98 SUB_98 -> DRK_98 SUB_98 -> MRJ_98 Tree of structural model: SUB_98 -> SMK_98 -> DRK_98 -> MRJ_98 Summary of manifest variables Categories for each variable: response 1 2 ESMK_98 Yes No FSMK_98 Yes No DSMK_98 Yes No HSMK_98 Yes No EDRK_98 Yes No CDRK_98 Yes No WDRK_98 Yes No BDRK_98 Yes No EMRJ_98 Yes No CMRJ_98 Yes No OMRJ_98 Yes No SMRJ_98 Yes No Frequencies for each categories: response 1 2 ESMK_98 280 220 0 FSMK_98 204 296 0 DSMK_98 93 407 0 HSMK_98 52 448 0 EDRK_98 374 126 0 CDRK_98 265 235 0 WDRK_98 110 390 0 BDRK_98 137 363 0 EMRJ_98 191 309 0 CMRJ_98 110 390 0 OMRJ_98 42 458 0 SMRJ_98 44 456 0 Summary of model fit Number of observations 500 Number of free parameters 31 Log-likelihood -2156.738 Information criteria Akaike (AIC) 4375.477 Bayesian (BIC) 4506.130 Chi-squared Tests Residual degree of freedom (df) 4064 Pearson Chi-squared (X-squared) 492.146 P(>Chi) 1.000 Likelihood Ratio (G-squared) 419.510 P(>Chi) 1.000 > param(model_jlca) PI : (SUB_98) class 1 2 0.5853 0.4147 TAU : (A) parent child 1 2 1 0.1386 0.7883 2 0.8614 0.2117 parent SUB_98 child SMK_98 (B) parent child 1 2 1 0.2782 0.8855 2 0.7218 0.1145 parent SUB_98 child DRK_98 (C) parent child 1 2 1 0.0248 0.4956 2 0.9752 0.5044 parent SUB_98 child MRJ_98 RHO : (a) class response 1 2 1(V1) 1.0000 0.2568 2 0.0000 0.7432 1(V2) 1.0000 0.0000 2 0.0000 1.0000 1(V3) 0.4559 0.0000 2 0.5441 1.0000 1(V4) 0.2549 0.0000 2 0.7451 1.0000 V1 V2 V3 V4 SMK_98 ESMK_98 FSMK_98 DSMK_98 HSMK_98 (b) class response 1 2 1(V1) 1.0000 0.4638 2 0.0000 0.5362 1(V2) 1.0000 0.0000 2 0.0000 1.0000 1(V3) 0.4151 0.0000 2 0.5849 1.0000 1(V4) 0.5170 0.0000 2 0.4830 1.0000 V1 V2 V3 V4 DRK_98 EDRK_98 CDRK_98 WDRK_98 BDRK_98 (c) class response 1 2 1(V1) 1.0000 0.2077 2 0.0000 0.7923 1(V2) 1.0000 0.0000 2 0.0000 1.0000 1(V3) 0.3818 0.0000 2 0.6182 1.0000 1(V4) 0.4000 0.0000 2 0.6000 1.0000 V1 V2 V3 V4 MRJ_98 EMRJ_98 CMRJ_98 OMRJ_98 SMRJ_98 > > # JLCPA > nlsy_jlcpa <- slca( + SMK_98(2) ~ ESMK_98 + FSMK_98 + DSMK_98 + HSMK_98, + DRK_98(2) ~ EDRK_98 + CDRK_98 + WDRK_98 + BDRK_98, + MRJ_98(2) ~ EMRJ_98 + CMRJ_98 + OMRJ_98 + SMRJ_98, + SUB_98(2) ~ SMK_98 + DRK_98 + MRJ_98, + SMK_03(2) ~ ESMK_03 + FSMK_03 + DSMK_03 + HSMK_03, + DRK_03(2) ~ EDRK_03 + CDRK_03 + WDRK_03 + BDRK_03, + MRJ_03(2) ~ EMRJ_03 + CMRJ_03 + OMRJ_03 + SMRJ_03, + SUB_03(2) ~ SMK_03 + DRK_03 + MRJ_03, + SMK_08(2) ~ ESMK_08 + FSMK_08 + DSMK_08 + HSMK_08, + DRK_08(2) ~ EDRK_08 + CDRK_08 + WDRK_08 + BDRK_08, + MRJ_08(2) ~ EMRJ_08 + CMRJ_08 + OMRJ_08 + SMRJ_08, + SUB_08(2) ~ SMK_08 + DRK_08 + MRJ_08, + PROF(2) ~ SUB_98 + SUB_03 + SUB_08, + constraints = list( + c("SMK_98", "SMK_03", "SMK_08"), + c("DRK_98", "DRK_03", "DRK_08"), + c("MRJ_98", "MRJ_03", "MRJ_08"), + c("SUB_98 ~ SMK_98", "SUB_03 ~ SMK_03", "SUB_08 ~ SMK_08"), + c("SUB_98 ~ DRK_98", "SUB_03 ~ DRK_03", "SUB_08 ~ DRK_08"), + c("SUB_98 ~ MRJ_98", "SUB_03 ~ MRJ_03", "SUB_08 ~ MRJ_08") + ) + ) %>% estimate(example_data, control = slcaControl(em.tol = 1e-6)) > > nlsy_jlcpa$convergence EM nlm TRUE NA > > > > cleanEx() detaching ‘package:magrittr’ > nameEx("regress") > ### * regress > > flush(stderr()); flush(stdout()) > > ### Name: regress > ### Title: Regress Exogenous Variables on Latent Variables > ### Aliases: regress regress.slcafit > > ### ** Examples > > library(magrittr) > names(nlsy97) [1] "SEX" "RACE" "ESMK_98" "FSMK_98" "DSMK_98" "HSMK_98" "EDRK_98" [8] "CDRK_98" "WDRK_98" "BDRK_98" "EMRJ_98" "CMRJ_98" "OMRJ_98" "SMRJ_98" [15] "ESMK_03" "FSMK_03" "DSMK_03" "HSMK_03" "EDRK_03" "CDRK_03" "WDRK_03" [22] "BDRK_03" "EMRJ_03" "CMRJ_03" "OMRJ_03" "SMRJ_03" "ESMK_08" "FSMK_08" [29] "DSMK_08" "HSMK_08" "EDRK_08" "CDRK_08" "WDRK_08" "BDRK_08" "EMRJ_08" [36] "CMRJ_08" "OMRJ_08" "SMRJ_08" > nlsy_jlcpa %>% regress(SMK_98 ~ SEX, nlsy97) Coefficients: class (Intercept) SEXFemale 1/3 0.3983 -0.4445 2/3 -0.6614 0.0804 > > > > cleanEx() detaching ‘package:magrittr’ > nameEx("reorder.slcafit") > ### * reorder.slcafit > > flush(stderr()); flush(stdout()) > > ### Name: reorder.slcafit > ### Title: Reorder Latent Class Membership of Latent Class Variables > ### Aliases: reorder.slcafit > > ### ** Examples > > library(magrittr) > nlsy_jlcpa %>% param PI : (PROF) class 1 2 3 4 0.2790 0.2146 0.2869 0.2195 TAU : (A) parent child 1 2 3 4 1 0.0000 0.0000 0.1745 0.0000 2 0.0901 0.4456 0.1939 0.0403 3 0.3771 0.1675 0.1825 0.7018 4 0.2620 0.0000 0.0000 0.0813 5 0.2709 0.3869 0.4491 0.1766 parent PROF child SUB_98 (B) parent child 1 2 3 4 1 0.0000 0.0114 0.8719 0.0000 2 0.0000 0.9617 0.0000 0.0079 3 0.0000 0.0000 0.0965 0.9210 4 0.9941 0.0000 0.0000 0.0711 5 0.0059 0.0269 0.0316 0.0000 parent PROF child SUB_03 (C) parent child 1 2 3 4 1 0.0019 0.1821 0.8024 0.0319 2 0.0097 0.7202 0.0000 0.0000 3 0.0573 0.0442 0.1844 0.7168 4 0.9311 0.0356 0.0084 0.2473 5 0.0000 0.0179 0.0048 0.0040 parent PROF child SUB_08 (D) parent child 1 2 3 4 5 1 0.0254 0.0457 0.9505 0.7958 0.1613 2 0.7516 0.6690 0.0051 0.0000 0.1631 3 0.2230 0.2853 0.0444 0.2042 0.6755 parent SUB_98 SUB_03 SUB_08 child SMK_98 SMK_03 SMK_08 (E) parent child 1 2 3 4 5 1 0.5664 0.8533 0.0335 0.7521 0.2872 2 0.2229 0.1264 0.2589 0.2414 0.6127 3 0.2107 0.0203 0.7075 0.0065 0.1001 parent SUB_98 SUB_03 SUB_08 child DRK_98 DRK_03 DRK_08 (F) parent child 1 2 3 4 5 1 0.0871 0.1442 0.0317 0.1525 0.7278 2 0.0617 0.8387 0.0095 0.2302 0.1258 3 0.8512 0.0170 0.9588 0.6173 0.1463 parent SUB_98 SUB_03 SUB_08 child MRJ_98 MRJ_03 MRJ_08 RHO : (a) class response 1 2 3 1(V1) 0.0134 1.0000 1.0000 2 0.9866 0.0000 0.0000 1(V2) 0.0000 1.0000 0.6701 2 1.0000 0.0000 0.3299 1(V3) 0.0000 0.8833 0.0000 2 1.0000 0.1167 1.0000 1(V4) 0.0000 0.6460 0.0000 2 1.0000 0.3540 1.0000 V1 V2 V3 V4 SMK_98 ESMK_98 FSMK_98 DSMK_98 HSMK_98 SMK_03 ESMK_03 FSMK_03 DSMK_03 HSMK_03 SMK_08 ESMK_08 FSMK_08 DSMK_08 HSMK_08 (b) class response 1 2 3 1(V1) 1.0000 1.0000 0.0759 2 0.0000 0.0000 0.9241 1(V2) 1.0000 0.5760 0.0000 2 0.0000 0.4240 1.0000 1(V3) 0.6934 0.0000 0.0000 2 0.3066 1.0000 1.0000 1(V4) 0.8061 0.0000 0.0000 2 0.1939 1.0000 1.0000 V1 V2 V3 V4 DRK_98 EDRK_98 CDRK_98 WDRK_98 BDRK_98 DRK_03 EDRK_03 CDRK_03 WDRK_03 BDRK_03 DRK_08 EDRK_08 CDRK_08 WDRK_08 BDRK_08 (c) class response 1 2 3 1(V1) 0.5189 1.0000 0.0002 2 0.4811 0.0000 0.9998 1(V2) 0.0000 1.0000 0.0000 2 1.0000 0.0000 1.0000 1(V3) 0.0000 0.5259 0.0000 2 1.0000 0.4741 1.0000 1(V4) 0.0000 0.3720 0.0000 2 1.0000 0.6280 1.0000 V1 V2 V3 V4 MRJ_98 EMRJ_98 CMRJ_98 OMRJ_98 SMRJ_98 MRJ_03 EMRJ_03 CMRJ_03 OMRJ_03 SMRJ_03 MRJ_08 EMRJ_08 CMRJ_08 OMRJ_08 SMRJ_08 > > # Reorder the RHO parameters as ascending order > reordered1 <- nlsy_jlcpa %>% + reorder(SMK_98 = c(1, 3, 2), + DRK_98 = c(3, 2, 1), + MRJ_98 = c(3, 1, 2)) ==3347074== Invalid read of size 8 ==3347074== at 0x1571A64B: upInit(int*, double const*, double*, int, int, int, int*) (/data/localhost/ripley/R/packages/tests-vg/slca/src/upward.cpp:14) ==3347074== by 0x15709FDD: calcModel(Rcpp::Vector<13, Rcpp::PreserveStorage>, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<14, Rcpp::PreserveStorage>, Rcpp::Vector<10, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, int, int, int, int, int, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>) (/data/localhost/ripley/R/packages/tests-vg/slca/src/calculate.cpp:108) ==3347074== by 0x156FB9DF: _slca_calcModel (/data/localhost/ripley/R/packages/tests-vg/slca/src/RcppExports.cpp:71) ==3347074== by 0x496DEB: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:936) ==3347074== by 0x497903: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437) ==3347074== by 0x4D967D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518) ==3347074== by 0x4E4BCA: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167) ==3347074== by 0x4E6A66: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398) ==3347074== by 0x4E77FF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314) ==3347074== by 0x4E82F1: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333) ==3347074== by 0x53262C: dispatchMethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:473) ==3347074== Address 0xc9f3c08 is 0 bytes after a block of size 1,496 alloc'd ==3347074== at 0x4841AE6: malloc (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:447) ==3347074== by 0x52BD81: Rf_allocVector3 (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:2897) ==3347074== by 0x4A09DF: Rf_allocVector (/data/localhost/ripley/R/svn/R-devel/src/include/Rinlinedfuns.h:609) ==3347074== by 0x4A09DF: duplicate1 (/data/localhost/ripley/R/svn/R-devel/src/main/duplicate.c:348) ==3347074== by 0x4D820B: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8253) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518) ==3347074== by 0x4E4BCA: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167) ==3347074== by 0x4E6A66: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398) ==3347074== by 0x4E77FF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314) ==3347074== by 0x4E82F1: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333) ==3347074== by 0x53262C: dispatchMethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:473) ==3347074== by 0x532CAE: Rf_usemethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:513) ==3347074== by 0x532F01: do_usemethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:579) ==3347074== ==3347074== Invalid read of size 8 ==3347074== at 0x1570B945: calcModel(Rcpp::Vector<13, Rcpp::PreserveStorage>, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<14, Rcpp::PreserveStorage>, Rcpp::Vector<10, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, int, int, int, int, int, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>) (/data/localhost/ripley/R/packages/tests-vg/slca/src/calculate.cpp:188) ==3347074== by 0x156FB9DF: _slca_calcModel (/data/localhost/ripley/R/packages/tests-vg/slca/src/RcppExports.cpp:71) ==3347074== by 0x496DEB: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:936) ==3347074== by 0x497903: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437) ==3347074== by 0x4D967D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518) ==3347074== by 0x4E4BCA: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167) ==3347074== by 0x4E6A66: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398) ==3347074== by 0x4E77FF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314) ==3347074== by 0x4E82F1: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333) ==3347074== by 0x53262C: dispatchMethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:473) ==3347074== by 0x532CAE: Rf_usemethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:513) ==3347074== Address 0x1a54df50 is 0 bytes after a block of size 1,453,840 alloc'd ==3347074== at 0x4841AE6: malloc (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:447) ==3347074== by 0x52BD81: Rf_allocVector3 (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:2897) ==3347074== by 0x15708430: Vector (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/vector/Vector.h:125) ==3347074== by 0x15708430: calcModel(Rcpp::Vector<13, Rcpp::PreserveStorage>, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<14, Rcpp::PreserveStorage>, Rcpp::Vector<10, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, int, int, int, int, int, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>) (/data/localhost/ripley/R/packages/tests-vg/slca/src/calculate.cpp:20) ==3347074== by 0x156FB9DF: _slca_calcModel (/data/localhost/ripley/R/packages/tests-vg/slca/src/RcppExports.cpp:71) ==3347074== by 0x496DEB: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:936) ==3347074== by 0x497903: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437) ==3347074== by 0x4D967D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518) ==3347074== by 0x4E4BCA: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167) ==3347074== by 0x4E6A66: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398) ==3347074== by 0x4E77FF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314) ==3347074== by 0x4E82F1: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333) ==3347074== ==3347074== Invalid write of size 8 ==3347074== at 0x1570B94E: calcModel(Rcpp::Vector<13, Rcpp::PreserveStorage>, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<14, Rcpp::PreserveStorage>, Rcpp::Vector<10, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, int, int, int, int, int, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>) (/data/localhost/ripley/R/packages/tests-vg/slca/src/calculate.cpp:188) ==3347074== by 0x156FB9DF: _slca_calcModel (/data/localhost/ripley/R/packages/tests-vg/slca/src/RcppExports.cpp:71) ==3347074== by 0x496DEB: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:936) ==3347074== by 0x497903: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437) ==3347074== by 0x4D967D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518) ==3347074== by 0x4E4BCA: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167) ==3347074== by 0x4E6A66: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398) ==3347074== by 0x4E77FF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314) ==3347074== by 0x4E82F1: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333) ==3347074== by 0x53262C: dispatchMethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:473) ==3347074== by 0x532CAE: Rf_usemethod (/data/localhost/ripley/R/svn/R-devel/src/main/objects.c:513) ==3347074== Address 0x1a54df50 is 0 bytes after a block of size 1,453,840 alloc'd ==3347074== at 0x4841AE6: malloc (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:447) ==3347074== by 0x52BD81: Rf_allocVector3 (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:2897) ==3347074== by 0x15708430: Vector (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/vector/Vector.h:125) ==3347074== by 0x15708430: calcModel(Rcpp::Vector<13, Rcpp::PreserveStorage>, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<14, Rcpp::PreserveStorage>, Rcpp::Vector<10, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, int, int, int, int, int, int, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>, Rcpp::Vector<13, Rcpp::PreserveStorage>) (/data/localhost/ripley/R/packages/tests-vg/slca/src/calculate.cpp:20) ==3347074== by 0x156FB9DF: _slca_calcModel (/data/localhost/ripley/R/packages/tests-vg/slca/src/RcppExports.cpp:71) ==3347074== by 0x496DEB: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:936) ==3347074== by 0x497903: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437) ==3347074== by 0x4D967D: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533) ==3347074== by 0x4E4801: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518) ==3347074== by 0x4E4BCA: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167) ==3347074== by 0x4E6A66: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398) ==3347074== by 0x4E77FF: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314) ==3347074== by 0x4E82F1: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333) ==3347074== > reordered1 %>% param PI : (PROF) class 1 2 3 4 0.2790 0.2146 0.2869 0.2195 TAU : (A) parent child 1 2 3 4 1 0.0000 0.0000 0.1745 0.0000 2 0.0901 0.4456 0.1939 0.0403 3 0.3771 0.1675 0.1825 0.7018 4 0.2620 0.0000 0.0000 0.0813 5 0.2709 0.3869 0.4491 0.1766 parent PROF child SUB_98 (B) parent child 1 2 3 4 1 0.0000 0.0114 0.8719 0.0000 2 0.0000 0.9617 0.0000 0.0079 3 0.0000 0.0000 0.0965 0.9210 4 0.9941 0.0000 0.0000 0.0711 5 0.0059 0.0269 0.0316 0.0000 parent PROF child SUB_03 (C) parent child 1 2 3 4 1 0.0019 0.1821 0.8024 0.0319 2 0.0097 0.7202 0.0000 0.0000 3 0.0573 0.0442 0.1844 0.7168 4 0.9311 0.0356 0.0084 0.2473 5 0.0000 0.0179 0.0048 0.0040 parent PROF child SUB_08 (D) parent child 1 2 3 4 5 1 0.0254 0.0457 0.9505 0.7958 0.1613 2 0.2230 0.2853 0.0444 0.2042 0.6755 3 0.7516 0.6690 0.0051 0.0000 0.1631 parent SUB_98 SUB_03 SUB_08 child SMK_98 SMK_03 SMK_08 (E) parent child 1 2 3 4 5 1 0.2107 0.0203 0.7075 0.0065 0.1001 2 0.2229 0.1264 0.2589 0.2414 0.6127 3 0.5664 0.8533 0.0335 0.7521 0.2872 parent SUB_98 SUB_03 SUB_08 child DRK_98 DRK_03 DRK_08 (F) parent child 1 2 3 4 5 1 0.8512 0.0170 0.9588 0.6173 0.1463 2 0.0871 0.1442 0.0317 0.1525 0.7278 3 0.0617 0.8387 0.0095 0.2302 0.1258 parent SUB_98 SUB_03 SUB_08 child MRJ_98 MRJ_03 MRJ_08 RHO : (a) class response 1 2 3 1(V1) 0.0134 1.0000 1.0000 2 0.9866 0.0000 0.0000 1(V2) 0.0000 0.6701 1.0000 2 1.0000 0.3299 0.0000 1(V3) 0.0000 0.0000 0.8833 2 1.0000 1.0000 0.1167 1(V4) 0.0000 0.0000 0.6460 2 1.0000 1.0000 0.3540 V1 V2 V3 V4 SMK_98 ESMK_98 FSMK_98 DSMK_98 HSMK_98 SMK_03 ESMK_03 FSMK_03 DSMK_03 HSMK_03 SMK_08 ESMK_08 FSMK_08 DSMK_08 HSMK_08 (b) class response 1 2 3 1(V1) 0.0759 1.0000 1.0000 2 0.9241 0.0000 0.0000 1(V2) 0.0000 0.5760 1.0000 2 1.0000 0.4240 0.0000 1(V3) 0.0000 0.0000 0.6934 2 1.0000 1.0000 0.3066 1(V4) 0.0000 0.0000 0.8061 2 1.0000 1.0000 0.1939 V1 V2 V3 V4 DRK_98 EDRK_98 CDRK_98 WDRK_98 BDRK_98 DRK_03 EDRK_03 CDRK_03 WDRK_03 BDRK_03 DRK_08 EDRK_08 CDRK_08 WDRK_08 BDRK_08 (c) class response 1 2 3 1(V1) 0.0002 0.5189 1.0000 2 0.9998 0.4811 0.0000 1(V2) 0.0000 0.0000 1.0000 2 1.0000 1.0000 0.0000 1(V3) 0.0000 0.0000 0.5259 2 1.0000 1.0000 0.4741 1(V4) 0.0000 0.0000 0.3720 2 1.0000 1.0000 0.6280 V1 V2 V3 V4 MRJ_98 EMRJ_98 CMRJ_98 OMRJ_98 SMRJ_98 MRJ_03 EMRJ_03 CMRJ_03 OMRJ_03 SMRJ_03 MRJ_08 EMRJ_08 CMRJ_08 OMRJ_08 SMRJ_08 > # Label class1: nonuse > # class2: lifetime use > # class3: current use > > # Reorder the TAU parameters for joint classes as ascending order > reordered2 <- reordered1 %>% + reorder(SUB_98 = c(3, 4, 5, 1, 2)) > reordered2 %>% param PI : (PROF) class 1 2 3 4 0.2790 0.2146 0.2869 0.2195 TAU : (A) parent child 1 2 3 4 1 0.3771 0.1675 0.1825 0.7018 2 0.2620 0.0000 0.0000 0.0813 3 0.2709 0.3869 0.4491 0.1766 4 0.0000 0.0000 0.1745 0.0000 5 0.0901 0.4456 0.1939 0.0403 parent PROF child SUB_98 (B) parent child 1 2 3 4 1 0.0000 0.0000 0.0965 0.9210 2 0.9941 0.0000 0.0000 0.0711 3 0.0059 0.0269 0.0316 0.0000 4 0.0000 0.0114 0.8719 0.0000 5 0.0000 0.9617 0.0000 0.0079 parent PROF child SUB_03 (C) parent child 1 2 3 4 1 0.0573 0.0442 0.1844 0.7168 2 0.9311 0.0356 0.0084 0.2473 3 0.0000 0.0179 0.0048 0.0040 4 0.0019 0.1821 0.8024 0.0319 5 0.0097 0.7202 0.0000 0.0000 parent PROF child SUB_08 (D) parent child 1 2 3 4 5 1 0.9505 0.7958 0.1613 0.0254 0.0457 2 0.0444 0.2042 0.6755 0.2230 0.2853 3 0.0051 0.0000 0.1631 0.7516 0.6690 parent SUB_98 SUB_03 SUB_08 child SMK_98 SMK_03 SMK_08 (E) parent child 1 2 3 4 5 1 0.7075 0.0065 0.1001 0.2107 0.0203 2 0.2589 0.2414 0.6127 0.2229 0.1264 3 0.0335 0.7521 0.2872 0.5664 0.8533 parent SUB_98 SUB_03 SUB_08 child DRK_98 DRK_03 DRK_08 (F) parent child 1 2 3 4 5 1 0.9588 0.6173 0.1463 0.8512 0.0170 2 0.0317 0.1525 0.7278 0.0871 0.1442 3 0.0095 0.2302 0.1258 0.0617 0.8387 parent SUB_98 SUB_03 SUB_08 child MRJ_98 MRJ_03 MRJ_08 RHO : (a) class response 1 2 3 1(V1) 0.0134 1.0000 1.0000 2 0.9866 0.0000 0.0000 1(V2) 0.0000 0.6701 1.0000 2 1.0000 0.3299 0.0000 1(V3) 0.0000 0.0000 0.8833 2 1.0000 1.0000 0.1167 1(V4) 0.0000 0.0000 0.6460 2 1.0000 1.0000 0.3540 V1 V2 V3 V4 SMK_98 ESMK_98 FSMK_98 DSMK_98 HSMK_98 SMK_03 ESMK_03 FSMK_03 DSMK_03 HSMK_03 SMK_08 ESMK_08 FSMK_08 DSMK_08 HSMK_08 (b) class response 1 2 3 1(V1) 0.0759 1.0000 1.0000 2 0.9241 0.0000 0.0000 1(V2) 0.0000 0.5760 1.0000 2 1.0000 0.4240 0.0000 1(V3) 0.0000 0.0000 0.6934 2 1.0000 1.0000 0.3066 1(V4) 0.0000 0.0000 0.8061 2 1.0000 1.0000 0.1939 V1 V2 V3 V4 DRK_98 EDRK_98 CDRK_98 WDRK_98 BDRK_98 DRK_03 EDRK_03 CDRK_03 WDRK_03 BDRK_03 DRK_08 EDRK_08 CDRK_08 WDRK_08 BDRK_08 (c) class response 1 2 3 1(V1) 0.0002 0.5189 1.0000 2 0.9998 0.4811 0.0000 1(V2) 0.0000 0.0000 1.0000 2 1.0000 1.0000 0.0000 1(V3) 0.0000 0.0000 0.5259 2 1.0000 1.0000 0.4741 1(V4) 0.0000 0.0000 0.3720 2 1.0000 1.0000 0.6280 V1 V2 V3 V4 MRJ_98 EMRJ_98 CMRJ_98 OMRJ_98 SMRJ_98 MRJ_03 EMRJ_03 CMRJ_03 OMRJ_03 SMRJ_03 MRJ_08 EMRJ_08 CMRJ_08 OMRJ_08 SMRJ_08 > # Label class1: nonuse > # class2: heavy drinking only > # class3: not heavy use > # class4: heavy drinking & smoking > # class5: heavy use > > # Reorder the TAU paramters for profiles as ascending order > reordered3 <- reordered2 %>% + reorder(PROF = c(4, 1, 3, 2)) > reordered3 %>% param PI : (PROF) class 1 2 3 4 0.2195 0.2790 0.2869 0.2146 TAU : (A) parent child 1 2 3 4 1 0.7018 0.3771 0.1825 0.1675 2 0.0813 0.2620 0.0000 0.0000 3 0.1766 0.2709 0.4491 0.3869 4 0.0000 0.0000 0.1745 0.0000 5 0.0403 0.0901 0.1939 0.4456 parent PROF child SUB_98 (B) parent child 1 2 3 4 1 0.9210 0.0000 0.0965 0.0000 2 0.0711 0.9941 0.0000 0.0000 3 0.0000 0.0059 0.0316 0.0269 4 0.0000 0.0000 0.8719 0.0114 5 0.0079 0.0000 0.0000 0.9617 parent PROF child SUB_03 (C) parent child 1 2 3 4 1 0.7168 0.0573 0.1844 0.0442 2 0.2473 0.9311 0.0084 0.0356 3 0.0040 0.0000 0.0048 0.0179 4 0.0319 0.0019 0.8024 0.1821 5 0.0000 0.0097 0.0000 0.7202 parent PROF child SUB_08 (D) parent child 1 2 3 4 5 1 0.9505 0.7958 0.1613 0.0254 0.0457 2 0.0444 0.2042 0.6755 0.2230 0.2853 3 0.0051 0.0000 0.1631 0.7516 0.6690 parent SUB_98 SUB_03 SUB_08 child SMK_98 SMK_03 SMK_08 (E) parent child 1 2 3 4 5 1 0.7075 0.0065 0.1001 0.2107 0.0203 2 0.2589 0.2414 0.6127 0.2229 0.1264 3 0.0335 0.7521 0.2872 0.5664 0.8533 parent SUB_98 SUB_03 SUB_08 child DRK_98 DRK_03 DRK_08 (F) parent child 1 2 3 4 5 1 0.9588 0.6173 0.1463 0.8512 0.0170 2 0.0317 0.1525 0.7278 0.0871 0.1442 3 0.0095 0.2302 0.1258 0.0617 0.8387 parent SUB_98 SUB_03 SUB_08 child MRJ_98 MRJ_03 MRJ_08 RHO : (a) class response 1 2 3 1(V1) 0.0134 1.0000 1.0000 2 0.9866 0.0000 0.0000 1(V2) 0.0000 0.6701 1.0000 2 1.0000 0.3299 0.0000 1(V3) 0.0000 0.0000 0.8833 2 1.0000 1.0000 0.1167 1(V4) 0.0000 0.0000 0.6460 2 1.0000 1.0000 0.3540 V1 V2 V3 V4 SMK_98 ESMK_98 FSMK_98 DSMK_98 HSMK_98 SMK_03 ESMK_03 FSMK_03 DSMK_03 HSMK_03 SMK_08 ESMK_08 FSMK_08 DSMK_08 HSMK_08 (b) class response 1 2 3 1(V1) 0.0759 1.0000 1.0000 2 0.9241 0.0000 0.0000 1(V2) 0.0000 0.5760 1.0000 2 1.0000 0.4240 0.0000 1(V3) 0.0000 0.0000 0.6934 2 1.0000 1.0000 0.3066 1(V4) 0.0000 0.0000 0.8061 2 1.0000 1.0000 0.1939 V1 V2 V3 V4 DRK_98 EDRK_98 CDRK_98 WDRK_98 BDRK_98 DRK_03 EDRK_03 CDRK_03 WDRK_03 BDRK_03 DRK_08 EDRK_08 CDRK_08 WDRK_08 BDRK_08 (c) class response 1 2 3 1(V1) 0.0002 0.5189 1.0000 2 0.9998 0.4811 0.0000 1(V2) 0.0000 0.0000 1.0000 2 1.0000 1.0000 0.0000 1(V3) 0.0000 0.0000 0.5259 2 1.0000 1.0000 0.4741 1(V4) 0.0000 0.0000 0.3720 2 1.0000 1.0000 0.6280 V1 V2 V3 V4 MRJ_98 EMRJ_98 CMRJ_98 OMRJ_98 SMRJ_98 MRJ_03 EMRJ_03 CMRJ_03 OMRJ_03 SMRJ_03 MRJ_08 EMRJ_08 CMRJ_08 OMRJ_08 SMRJ_08 > # Label class1: nonuse stayer > # class2: heavy drinking advancer > # class3: heavy drk & smk advancer > # class4: heavy use advancer > > > > cleanEx() detaching ‘package:magrittr’ > nameEx("simulate.slca") > ### * simulate.slca > > flush(stderr()); flush(stdout()) > > ### Name: simulate.slca > ### Title: Simulate Data from an 'slca' Model > ### Aliases: simulate.slca > > ### ** Examples > > m1 <- slca(lc1[3] ~ x1 + x2 + x3 + x4 + x5, + lc2[4] ~ y1 + y2 + y3 + y4 + y5) > sim <- simulate(m1, 1000) > sapply(sim$class, table) $lc1 1 2 3 222 328 450 $lc2 1 2 3 4 332 63 280 325 > > # simulate data with defined number of levels of manifest items > m2 <- slca(lc1[3] ~ x1 + x2 + x3 + x4) > sim <- simulate(m2, nlevel = c(3, 3, 3, 3)) > d <- sim$response > sapply(d, table) x1 x2 x3 x4 1 201 176 118 146 2 155 144 227 205 3 144 180 155 149 > > sim <- simulate(m2, nlevel = c(x1 = 2, x3 = 3, x4 = 4)) > d <- sim$response > sapply(d, table) $x1 1 2 215 285 $x2 1 2 112 388 $x3 1 2 3 66 254 180 $x4 1 2 3 4 45 42 230 183 > > # simulate data with user-defined parameters > pi <- rep(1 / 3, 3) > rho <- c(.9, .1, .9, .1, .9, .1, .9, .1, + .9, .1, .9, .1, .1, .9, .1, .9, + .1, .9, .1, .9, .1, .9, .1, .9) > par <- c(pi, rho) > m3 <- slca(lc[3] ~ y1 + y2 + y3 + y4) > sim <- simulate(m3, parm = par) > mf <- estimate(m3, sim$response) > param(mf) PI : (lc) class 1 2 3 0.3278 0.3898 0.2825 RHO : (a) class response 1 2 3 1(V1) 0.0000 0.9702 0.9199 2 1.0000 0.0298 0.0801 1(V2) 0.1298 0.7879 0.9146 2 0.8702 0.2121 0.0854 1(V3) 0.0810 0.0795 1.0000 2 0.9190 0.9205 0.0000 1(V4) 0.0614 0.1955 0.8484 2 0.9386 0.8045 0.1516 V1 V2 V3 V4 lc y1 y2 y3 y4 > > > > cleanEx() > nameEx("slca") > ### * slca > > flush(stderr()); flush(stdout()) > > ### Name: slca > ### Title: Construct Structural Latent Class Model > ### Aliases: slca > > ### ** Examples > > # Standard LCA > slca(lc[3] ~ y1 + y2 + y3 + y4) Structural Latent Variable Model Latent variables (Root*): Label: lc* nclass: 3 Measurement model: lc -> { y1, y2, y3, y4 } a > # Latent transition analysis (LTA) > slca(lx[3] ~ x1 + x2 + x3 + x4, + ly[2] ~ y1 + y2 + y3 + y4, + lx ~ ly) Structural Latent Variable Model Latent variables (Root*): Label: lx* ly nclass: 3 2 Measurement model: lx -> { x1, x2, x3, x4 } a ly -> { y1, y2, y3, y4 } b Structural model: lx -> { ly } Dependency constraints: A lx -> ly > # LTA with measurement invariance > slca(l1[3] ~ y11 + y21 + y31 + y41, + l2[3] ~ y12 + y22 + y32 + y42, + l1 ~ l2, constraints = c("l1", "l2")) Structural Latent Variable Model Latent variables (Root*): Label: l1* l2 nclass: 3 3 Measurement model: l1 -> { y11, y21, y31, y41 } a l2 -> { y12, y22, y32, y42 } a Structural model: l1 -> { l2 } Dependency constraints: A l1 -> l2 > # Joint latent class analysis > slca(lx[2] ~ x1 + x2 + x3 + x4, + ly[3] ~ y1 + y2 + y3 + y4, + lz[2] ~ z1 + z2 + z3 + z4, + jc[3] ~ lx + ly + lz) Structural Latent Variable Model Latent variables (Root*): Label: lx ly lz jc* nclass: 2 3 2 3 Measurement model: lx -> { x1, x2, x3, x4 } a ly -> { y1, y2, y3, y4 } b lz -> { z1, z2, z3, z4 } c Structural model: jc -> { lx, ly, lz } Dependency constraints: A B C jc -> lx jc -> ly jc -> lz > # Latent class profile analysis (with measurement invariance) > slca(l1[3] ~ x1 + x2 + x3 + x4, + l2[3] ~ y1 + y2 + y3 + y4, + l3[3] ~ z1 + z2 + z3 + z4, + pf[4] ~ l1 + l2 + l3, + constraints = c("l1", "l2", "l3")) Structural Latent Variable Model Latent variables (Root*): Label: l1 l2 l3 pf* nclass: 3 3 3 4 Measurement model: l1 -> { x1, x2, x3, x4 } a l2 -> { y1, y2, y3, y4 } a l3 -> { z1, z2, z3, z4 } a Structural model: pf -> { l1, l2, l3 } Dependency constraints: A B C pf -> l1 pf -> l2 pf -> l3 > > > > ### *