Plot Model
First the data have to be encoded from Rank to binary. Examples of encoding are shown bellow.
| Rank | Rank | Rank |
|---|---|---|
| A | B | C |
| 1 | 2 | 3 |
| binary | binary | binary |
|---|---|---|
| {A, B} | {A, C} | {B, C} |
| 1 | 1 | 1 |
| Rank | Rank | Rank |
|---|---|---|
| A | B | C |
| 2 | 1 | 3 |
| binary | binary | binary |
|---|---|---|
| {A, B} | {A, C} | {B, C} |
| 0 | 1 | 1 |
## i1i2 i1i3 i2i3 i4i5 i4i6 i5i6 i7i8 i7i9 i8i9 i10i11 i10i12 i11i12 ## 1 1 0 0 1 0 0 1 1 1 0 1 1 ## 2 0 0 1 0 0 0 0 0 1 0 0 0 ## 3 0 0 1 0 0 1 0 1 1 0 0 0 ## 4 0 0 1 1 1 0 1 1 0 0 0 0 ## 5 1 1 1 0 0 1 1 1 0 1 0 0 ## 6 1 1 1 0 0 1 1 0 0 0 1 1
blocks<-set_block(c("i1","i2","i3"),
traits=c("t1","t2","t3"),
signs=c(1,1,1))+
set_block(c("i4","i5","i6"),
traits=c("t1","t2","t3"),
signs=c(-1,1,1))+
set_block(c("i7","i8","i9"),
traits=c("t1","t2","t3"),
signs=c(1,1,-1))+
set_block(c("i10","i11","i12"),
traits=c("t1","t2","t3"),
signs=c(1,-1,1))
print(data.frame(blocks[[1]]))## items traits names signs items.1 traits.1 names.1 signs.1 items.2 traits.2 names.2 signs.2 items.3 traits.3 names.3 signs.3 ## 1 i1 t1 i1 1 i4 t1 i4 -1 i7 t1 i7 1 i10 t1 i10 1 ## 2 i2 t2 i2 1 i5 t2 i5 1 i8 t2 i8 1 i11 t2 i11 -1 ## 3 i3 t3 i3 1 i6 t3 i6 1 i9 t3 i9 -1 i12 t3 i12 1
triplets_long<-make_TIRT_data(data=triplets,blocks=blocks,direction="larger",format="pairwise",family="bernoulli",range=c(0,1))
head(triplets_long)## # A tibble: 6 × 11 ## person block comparison itemC trait1 trait2 item1 item2 sign1 sign2 response #### 1 1 1 1 1 t1 t2 i1 i2 1 1 1 ## 2 2 1 1 1 t1 t2 i1 i2 1 1 0 ## 3 3 1 1 1 t1 t2 i1 i2 1 1 0 ## 4 4 1 1 1 t1 t2 i1 i2 1 1 0 ## 5 5 1 1 1 t1 t2 i1 i2 1 1 1 ## 6 6 1 1 1 t1 t2 i1 i2 1 1 1
# Fit indices
pt<-options(fit=c("GFI","AGFI","RMSEA","NFI","NNFI","CFI","RNI","IFI","SRMR","AIC","AICc","BIC","CAIC"))
fit_index<-data.frame(fit=lavaan::inspect(model,"fit",add.labels=TRUE,add.class=TRUE,list.by.group=TRUE,drop.list.single.group=TRUE))
round(fit_index,2)## fit ## npar 35.00 ## fmin 0.17 ## chisq 69.54 ## df 43.00 ## pvalue NA ## chisq.scaled 46.71 ## df.scaled 43.00 ## pvalue.scaled 0.32 ## chisq.scaling.factor 2.01 ## baseline.chisq 1615.94 ## baseline.df 66.00 ## baseline.pvalue NA ## baseline.chisq.scaled 446.56 ## baseline.df.scaled 66.00 ## baseline.pvalue.scaled 0.00 ## baseline.chisq.scaling.factor 3.81 ## cfi 0.98 ## tli 0.97 ## cfi.scaled 0.99 ## tli.scaled 0.99 ## cfi.robust NA ## tli.robust NA ## nnfi 0.97 ## rfi 0.93 ## nfi 0.96 ## pnfi 0.62 ## ifi 0.98 ## rni 0.98 ## nnfi.scaled 0.99 ## rfi.scaled 0.84 ## nfi.scaled 0.90 ## pnfi.scaled 0.58 ## ifi.scaled 0.99 ## rni.scaled 0.99 ## nnfi.robust NA ## rni.robust NA ## rmsea 0.06 ## rmsea.ci.lower 0.03 ## rmsea.ci.upper 0.08 ## rmsea.ci.level 0.90 ## rmsea.pvalue 0.33 ## rmsea.close.h0 0.05 ## rmsea.notclose.pvalue 0.04 ## rmsea.notclose.h0 0.08 ## rmsea.scaled 0.02 ## rmsea.ci.lower.scaled 0.00 ## rmsea.ci.upper.scaled 0.05 ## rmsea.pvalue.scaled 0.92 ## rmsea.notclose.pvalue.scaled 0.00 ## rmsea.robust NA ## rmsea.ci.lower.robust NA ## rmsea.ci.upper.robust NA ## rmsea.pvalue.robust NA ## rmsea.notclose.pvalue.robust NA ## rmr 0.06 ## rmr_nomean 0.07 ## srmr 0.07 ## srmr_bentler 0.06 ## srmr_bentler_nomean 0.07 ## crmr 0.07 ## crmr_nomean 0.07 ## srmr_mplus NA ## srmr_mplus_nomean NA ## cn_05 170.72 ## cn_01 194.06 ## gfi 0.97 ## agfi 0.94 ## pgfi 0.53 ## mfi 0.94 ## wrmr 0.94
# Explained variance
r_squared<-data.frame(r_squared=lavaan::inspect(model,"rsquare",add.labels=TRUE,add.class=TRUE,list.by.group=TRUE,drop.list.single.group=TRUE))
r_squared## r_squared ## i1i2 0.6063962 ## i1i3 0.5592264 ## i2i3 0.4278743 ## i4i5 0.5230762 ## i4i6 0.6047728 ## i5i6 0.3926475 ## i7i8 0.5656510 ## i7i9 0.5186509 ## i8i9 0.5715663 ## i10i11 0.4101206 ## i10i12 0.5753794 ## i11i12 0.5960012
# Estimates
unstandardized_estimates<-lavaan::inspect(model,"est",add.labels=TRUE,add.class=TRUE,list.by.group=TRUE,drop.list.single.group=TRUE)
unstandardized_estimates## $lambda ## trait1 trait2 trait3 ## i1i2 1.174 -1.264 0.000 ## i1i3 1.174 0.000 -1.224 ## i4i5 -1.389 -1.240 0.000 ## i4i6 -1.389 0.000 -0.923 ## i7i8 1.036 -1.006 0.000 ## i7i9 1.036 0.000 0.998 ## i10i11 1.113 0.595 0.000 ## i10i12 1.113 0.000 -1.231 ## i2i3 0.000 1.264 -1.224 ## i5i6 0.000 1.240 -0.923 ## i8i9 0.000 1.006 0.998 ## i11i12 0.000 -0.595 -1.231 ## ## $theta ## i1i2 i1i3 i4i5 i4i6 i7i8 i7i9 i10i11 i10i12 i2i3 i5i6 i8i9 i11i12 ## i1i2 2.432 ## i1i3 1.000 2.078 ## i4i5 0.000 0.000 2.344 ## i4i6 0.000 0.000 1.000 1.958 ## i7i8 0.000 0.000 0.000 0.000 2.016 ## i7i9 0.000 0.000 0.000 0.000 1.000 2.081 ## i10i11 0.000 0.000 0.000 0.000 0.000 0.000 1.795 ## i10i12 0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.864 ## i2i3 -1.432 1.078 0.000 0.000 0.000 0.000 0.000 0.000 2.510 ## i5i6 0.000 0.000 -1.344 0.958 0.000 0.000 0.000 0.000 0.000 2.302 ## i8i9 0.000 0.000 0.000 0.000 -1.016 1.081 0.000 0.000 0.000 0.000 2.097 ## i11i12 0.000 0.000 0.000 0.000 0.000 0.000 -0.795 0.864 0.000 0.000 0.000 1.659 ## ## $psi ## trait1 trait2 trait3 ## trait1 1.000 ## trait2 -0.260 1.000 ## trait3 0.083 0.394 1.000 ## ## $nu ## intrcp ## i1i2 0 ## i1i3 0 ## i4i5 0 ## i4i6 0 ## i7i8 0 ## i7i9 0 ## i10i11 0 ## i10i12 0 ## i2i3 0 ## i5i6 0 ## i8i9 0 ## i11i12 0 ## ## $alpha ## intrcp ## trait1 0 ## trait2 0 ## trait3 0 ## ## $tau ## thrshl ## i1i2|t1 0.470 ## i1i3|t1 -1.170 ## i4i5|t1 0.648 ## i4i6|t1 1.135 ## i7i8|t1 -0.859 ## i7i9|t1 -1.181 ## i10i11|t1 0.533 ## i10i12|t1 1.514 ## i2i3|t1 -1.917 ## i5i6|t1 0.646 ## i8i9|t1 -0.475 ## i11i12|t1 0.892 ## ## $delta ## scales ## i1i2 0.402 ## i1i3 0.461 ## i4i5 0.451 ## i4i6 0.449 ## i7i8 0.464 ## i7i9 0.481 ## i10i11 0.573 ## i10i12 0.477 ## i2i3 0.477 ## i5i6 0.514 ## i8i9 0.452 ## i11i12 0.493
standardized_estimates<-lavaan::inspect(model,"std",add.labels=TRUE,add.class=TRUE,list.by.group=TRUE,drop.list.single.group=TRUE)
standardized_estimates## $lambda ## trait1 trait2 trait3 ## i1i2 0.472 -0.508 0.000 ## i1i3 0.541 0.000 -0.564 ## i4i5 -0.626 -0.559 0.000 ## i4i6 -0.624 0.000 -0.415 ## i7i8 0.481 -0.467 0.000 ## i7i9 0.498 0.000 0.480 ## i10i11 0.638 0.341 0.000 ## i10i12 0.531 0.000 -0.588 ## i2i3 0.000 0.603 -0.584 ## i5i6 0.000 0.637 -0.474 ## i8i9 0.000 0.455 0.451 ## i11i12 0.000 -0.294 -0.608 ## ## $theta ## i1i2 i1i3 i4i5 i4i6 i7i8 i7i9 i10i11 i10i12 i2i3 i5i6 i8i9 i11i12 ## i1i2 0.394 ## i1i3 0.445 0.441 ## i4i5 0.000 0.000 0.477 ## i4i6 0.000 0.000 0.467 0.395 ## i7i8 0.000 0.000 0.000 0.000 0.434 ## i7i9 0.000 0.000 0.000 0.000 0.488 0.481 ## i10i11 0.000 0.000 0.000 0.000 0.000 0.000 0.590 ## i10i12 0.000 0.000 0.000 0.000 0.000 0.000 0.547 0.425 ## i2i3 -0.580 0.472 0.000 0.000 0.000 0.000 0.000 0.000 0.572 ## i5i6 0.000 0.000 -0.579 0.451 0.000 0.000 0.000 0.000 0.000 0.607 ## i8i9 0.000 0.000 0.000 0.000 -0.494 0.517 0.000 0.000 0.000 0.000 0.428 ## i11i12 0.000 0.000 0.000 0.000 0.000 0.000 -0.461 0.491 0.000 0.000 0.000 0.404 ## ## $psi ## trait1 trait2 trait3 ## trait1 1.000 ## trait2 -0.260 1.000 ## trait3 0.083 0.394 1.000 ## ## $nu ## intrcp ## i1i2 0 ## i1i3 0 ## i4i5 0 ## i4i6 0 ## i7i8 0 ## i7i9 0 ## i10i11 0 ## i10i12 0 ## i2i3 0 ## i5i6 0 ## i8i9 0 ## i11i12 0 ## ## $alpha ## intrcp ## trait1 0 ## trait2 0 ## trait3 0 ## ## $tau ## thrshl ## i1i2|t1 0.189 ## i1i3|t1 -0.539 ## i4i5|t1 0.292 ## i4i6|t1 0.510 ## i7i8|t1 -0.399 ## i7i9|t1 -0.568 ## i10i11|t1 0.305 ## i10i12|t1 0.722 ## i2i3|t1 -0.915 ## i5i6|t1 0.332 ## i8i9|t1 -0.215 ## i11i12|t1 0.440 ## ## $delta ## scales ## i1i2 1 ## i1i3 1 ## i4i5 1 ## i4i6 1 ## i7i8 1 ## i7i9 1 ## i10i11 1 ## i10i12 1 ## i2i3 1 ## i5i6 1 ## i8i9 1 ## i11i12 1
lavaan::parameterEstimates(model,se=TRUE,
zstat=TRUE,
pvalue=TRUE,
ci=TRUE,
level=0.95,
boot.ci.type="perc",
standardized=TRUE,
fmi=FALSE,
remove.system.eq=TRUE,
remove.eq=FALSE,
remove.ineq=FALSE,
remove.def=FALSE,
rsquare=TRUE,
add.attributes=TRUE)## ## Latent Variables: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all ## trait1 =~ ## i1i2 (L1) 1.174 0.513 2.289 0.022 0.169 2.179 1.174 0.472 ## i1i3 (L1) 1.174 0.513 2.289 0.022 0.169 2.179 1.174 0.541 ## i4i5 (L4) -1.389 0.607 -2.289 0.022 -2.578 -0.199 -1.389 -0.626 ## i4i6 (L4) -1.389 0.607 -2.289 0.022 -2.578 -0.199 -1.389 -0.624 ## i7i8 (L7) 1.036 0.375 2.765 0.006 0.302 1.770 1.036 0.481 ## i7i9 (L7) 1.036 0.375 2.765 0.006 0.302 1.770 1.036 0.498 ## i10i11 (L10) 1.113 0.429 2.591 0.010 0.271 1.954 1.113 0.638 ## i10i12 (L10) 1.113 0.429 2.591 0.010 0.271 1.954 1.113 0.531 ## trait2 =~ ## i1i2 (L2n) -1.264 0.504 -2.507 0.012 -2.252 -0.276 -1.264 -0.508 ## i2i3 (L2) 1.264 0.504 2.507 0.012 0.276 2.252 1.264 0.603 ## i4i5 (L5n) -1.240 0.517 -2.398 0.016 -2.253 -0.226 -1.240 -0.559 ## i5i6 (L5) 1.240 0.517 2.398 0.016 0.226 2.253 1.240 0.637 ## i7i8 (L8n) -1.006 0.371 -2.713 0.007 -1.732 -0.279 -1.006 -0.467 ## i8i9 (L8) 1.006 0.371 2.713 0.007 0.279 1.732 1.006 0.455 ## i10i11 (L11n) 0.595 0.273 2.180 0.029 0.060 1.130 0.595 0.341 ## i11i12 (L11) -0.595 0.273 -2.180 0.029 -1.130 -0.060 -0.595 -0.294 ## trait3 =~ ## i1i3 (L3n) -1.224 0.461 -2.656 0.008 -2.127 -0.321 -1.224 -0.564 ## i2i3 (L3n) -1.224 0.461 -2.656 0.008 -2.127 -0.321 -1.224 -0.584 ## i4i6 (L6n) -0.923 0.401 -2.302 0.021 -1.710 -0.137 -0.923 -0.415 ## i5i6 (L6n) -0.923 0.401 -2.302 0.021 -1.710 -0.137 -0.923 -0.474 ## i7i9 (L9n) 0.998 0.339 2.944 0.003 0.334 1.663 0.998 0.480 ## i8i9 (L9n) 0.998 0.339 2.944 0.003 0.334 1.663 0.998 0.451 ## i10i12 (L12n) -1.231 0.423 -2.909 0.004 -2.061 -0.402 -1.231 -0.588 ## i11i12 (L12n) -1.231 0.423 -2.909 0.004 -2.061 -0.402 -1.231 -0.608 ## ## Covariances: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all ## trait1 ~~ ## trait2 -0.260 0.147 -1.766 0.077 -0.548 0.029 -0.260 -0.260 ## trait3 0.083 0.163 0.513 0.608 -0.236 0.402 0.083 0.083 ## trait2 ~~ ## trait3 0.394 0.139 2.842 0.004 0.122 0.665 0.394 0.394 ## .i1i2 ~~ ## .i1i3 (P1) 1.000 NA NA NA 1.000 0.445 ## .i2i3 (P2n) -1.432 1.272 -1.126 0.260 -3.925 1.060 -1.432 -0.580 ## .i1i3 ~~ ## .i2i3 (P3) 1.078 1.114 0.968 0.333 -1.105 3.261 1.078 0.472 ## .i4i5 ~~ ## .i4i6 (P4) 1.000 1.000 1.000 1.000 0.467 ## .i5i6 (P5n) -1.344 1.134 -1.186 0.236 -3.567 0.878 -1.344 -0.579 ## .i4i6 ~~ ## .i5i6 (P6) 0.958 0.883 1.084 0.278 -0.774 2.689 0.958 0.451 ## .i7i8 ~~ ## .i7i9 (P7) 1.000 1.000 1.000 1.000 0.488 ## .i8i9 (P8n) -1.016 0.801 -1.269 0.204 -2.586 0.553 -1.016 -0.494 ## .i7i9 ~~ ## .i8i9 (P9) 1.081 0.879 1.230 0.219 -0.642 2.803 1.081 0.517 ## .i10i11 ~~ ## .i10i12 (P10) 1.000 1.000 1.000 1.000 0.547 ## .i11i12 (P11n) -0.795 0.677 -1.174 0.240 -2.123 0.532 -0.795 -0.461 ## .i10i12 ~~ ## .i11i12 (P12) 0.864 0.726 1.191 0.234 -0.558 2.286 0.864 0.491 ## ## Intercepts: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all ## .i1i2 0.000 0.000 0.000 0.000 0.000 ## .i1i3 0.000 0.000 0.000 0.000 0.000 ## .i4i5 0.000 0.000 0.000 0.000 0.000 ## .i4i6 0.000 0.000 0.000 0.000 0.000 ## .i7i8 0.000 0.000 0.000 0.000 0.000 ## .i7i9 0.000 0.000 0.000 0.000 0.000 ## .i10i11 0.000 0.000 0.000 0.000 0.000 ## .i10i12 0.000 0.000 0.000 0.000 0.000 ## .i2i3 0.000 0.000 0.000 0.000 0.000 ## .i5i6 0.000 0.000 0.000 0.000 0.000 ## .i8i9 0.000 0.000 0.000 0.000 0.000 ## .i11i12 0.000 0.000 0.000 0.000 0.000 ## trait1 0.000 0.000 0.000 0.000 0.000 ## trait2 0.000 0.000 0.000 0.000 0.000 ## trait3 0.000 0.000 0.000 0.000 0.000 ## ## Thresholds: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all ## i1i2|t1 0.470 0.259 1.815 0.069 -0.037 0.978 0.470 0.189 ## i1i3|t1 -1.170 0.413 -2.835 0.005 -1.979 -0.361 -1.170 -0.539 ## i4i5|t1 0.648 0.282 2.297 0.022 0.095 1.201 0.648 0.292 ## i4i6|t1 1.135 0.413 2.748 0.006 0.326 1.945 1.135 0.510 ## i7i8|t1 -0.859 0.273 -3.147 0.002 -1.395 -0.324 -0.859 -0.399 ## i7i9|t1 -1.181 0.331 -3.565 0.000 -1.830 -0.532 -1.181 -0.568 ## i10i11|t1 0.533 0.211 2.525 0.012 0.119 0.947 0.533 0.305 ## i10i12|t1 1.514 0.439 3.447 0.001 0.653 2.374 1.514 0.722 ## i2i3|t1 -1.917 0.711 -2.696 0.007 -3.311 -0.524 -1.917 -0.915 ## i5i6|t1 0.646 0.284 2.273 0.023 0.089 1.203 0.646 0.332 ## i8i9|t1 -0.475 0.244 -1.944 0.052 -0.954 0.004 -0.475 -0.215 ## i11i12|t1 0.892 0.313 2.846 0.004 0.278 1.505 0.892 0.440 ## ## Variances: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all ## trat1 1.000 1.000 1.000 1.000 1.000 ## trat2 1.000 1.000 1.000 1.000 1.000 ## trat3 1.000 1.000 1.000 1.000 1.000 ## .i1i2 (P1P2) 2.432 1.272 1.912 0.056 -0.060 4.925 2.432 0.394 ## .i1i3 (P1P3) 2.078 1.114 1.866 0.062 -0.105 4.261 2.078 0.441 ## .i2i3 (P2P3) 2.510 2.163 1.161 0.246 -1.729 6.750 2.510 0.572 ## .i4i5 (P4P5) 2.344 1.134 2.067 0.039 0.122 4.567 2.344 0.477 ## .i4i6 (P4P6) 1.958 0.883 2.216 0.027 0.226 3.689 1.958 0.395 ## .i5i6 (P5P6) 2.302 1.828 1.260 0.208 -1.280 5.884 2.302 0.607 ## .i7i8 (P7P8) 2.016 0.801 2.518 0.012 0.447 3.586 2.016 0.434 ## .i7i9 (P7P9) 2.081 0.879 2.368 0.018 0.358 3.803 2.081 0.481 ## .i8i9 (P8P9) 2.097 1.430 1.466 0.143 -0.706 4.900 2.097 0.428 ## .i1011 (P10P11) 1.795 0.677 2.650 0.008 0.468 3.123 1.795 0.590 ## .i1012 (P10P12) 1.864 0.726 2.569 0.010 0.442 3.286 1.864 0.425 ## .i1112 (P11P) 1.659 1.176 1.411 0.158 -0.646 3.964 1.659 0.404 ## ## Scales y*: ## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper Std.lv Std.all ## i1i2 0.402 0.402 0.402 0.402 1.000 ## i1i3 0.461 0.461 0.461 0.461 1.000 ## i4i5 0.451 0.451 0.451 0.451 1.000 ## i4i6 0.449 0.449 0.449 0.449 1.000 ## i7i8 0.464 0.464 0.464 0.464 1.000 ## i7i9 0.481 0.481 0.481 0.481 1.000 ## i10i11 0.573 0.573 0.573 0.573 1.000 ## i10i12 0.477 0.477 0.477 0.477 1.000 ## i2i3 0.477 0.477 0.477 0.477 1.000 ## i5i6 0.514 0.514 0.514 0.514 1.000 ## i8i9 0.452 0.452 0.452 0.452 1.000 ## i11i12 0.493 0.493 0.493 0.493 1.000 ## ## R-Square: ## Estimate ## i1i2 0.606 ## i1i3 0.559 ## i2i3 0.428 ## i4i5 0.523 ## i4i6 0.605 ## i5i6 0.393 ## i7i8 0.566 ## i7i9 0.519 ## i8i9 0.572 ## i10i11 0.410 ## i10i12 0.575 ## i11i12 0.596 ## ## Constraints: ## |Slack| ## L2 - (-L2n) 0.000 ## L5 - (-L5n) 0.000 ## L8 - (-L8n) 0.000 ## L11 - (-L11n) 0.000 ## P1P2 - (P1-P2n) 0.000 ## P1P3 - (P1+P3) 0.000 ## P2P3 - (-P2n+P3) 0.000 ## P4P5 - (P4-P5n) 0.000 ## P4P6 - (P4+P6) 0.000 ## P5P6 - (-P5n+P6) 0.000 ## P7P8 - (P7-P8n) 0.000 ## P7P9 - (P7+P9) 0.000 ## P8P9 - (-P8n+P9) 0.000 ## P10P11 - (P10-P11n) 0.000 ## P10P12 - (P10+P12) 0.000 ## P11P12 - (-P11n+P12) 0.000 ## P1 - (1) 0.000 ## P4 - (1) 0.000 ## P7 - (1) 0.000 ## P10 - (1) 0.000
# Modification indices
lavaan::modificationIndices(model,
standardized=TRUE,
cov.std=TRUE,
information="expected",
power=TRUE,
delta=0.1,
alpha=0.05,
high.power=0.75,
sort.=TRUE,
minimum.value=0,
free.remove=FALSE,
na.remove=TRUE,
op=NULL)## Warning: lavaan->modificationIndices():
## the modindices() function ignores equality constraints; use lavTestScore() to assess the impact of releasing one or multiple constraints.## lhs op rhs mi epc sepc.all delta ncp power decision ## 122 trait1 =~ i2i3 13.197 -0.644 -0.307 0.1 0.318 0.087 **(m)** ## 180 i10i12 ~~ i5i6 11.332 1.077 0.520 0.1 0.098 0.061 **(m)** ## 153 i4i5 ~~ i7i9 9.980 -1.287 -0.583 0.1 0.060 0.057 **(m)** ## 176 i10i11 ~~ i2i3 8.760 -0.835 -0.393 0.1 0.126 0.065 **(m)** ## 123 trait1 =~ i5i6 7.414 0.433 0.222 0.1 0.396 0.096 **(m)** ## 179 i10i12 ~~ i2i3 5.250 -0.838 -0.387 0.1 0.075 0.059 **(m)** ## 173 i7i9 ~~ i2i3 5.138 -0.818 -0.358 0.1 0.077 0.059 **(m)** ## 125 trait1 =~ i11i12 4.999 0.340 0.168 0.1 0.434 0.101 **(m)** ## 177 i10i11 ~~ i5i6 4.190 0.543 0.267 0.1 0.142 0.066 **(m)** ## 166 i7i8 ~~ i10i11 3.730 -0.623 -0.327 0.1 0.096 0.061 (i) ## 148 i1i3 ~~ i10i12 3.665 0.777 0.395 0.1 0.061 0.057 (i) ## 184 i2i3 ~~ i11i12 3.331 0.663 0.325 0.1 0.076 0.059 (i) ## 128 trait2 =~ i7i9 2.733 -0.391 -0.188 0.1 0.179 0.071 (i) ## 130 trait3 =~ i1i2 2.463 -0.305 -0.123 0.1 0.265 0.081 (i) ## 175 i7i9 ~~ i11i12 2.422 0.581 0.313 0.1 0.072 0.058 (i) ## 139 i1i2 ~~ i10i12 2.366 0.670 0.314 0.1 0.053 0.056 (i) ## 124 trait1 =~ i8i9 2.266 0.275 0.124 0.1 0.299 0.085 (i) ## 140 i1i2 ~~ i5i6 2.229 0.601 0.254 0.1 0.062 0.057 (i) ## 155 i4i5 ~~ i10i12 2.213 -0.565 -0.270 0.1 0.069 0.058 (i) ## 145 i1i3 ~~ i7i8 2.210 -0.601 -0.294 0.1 0.061 0.057 (i) ## 149 i1i3 ~~ i5i6 2.191 -0.493 -0.225 0.1 0.090 0.060 (i) ## 186 i5i6 ~~ i11i12 2.154 -0.475 -0.243 0.1 0.096 0.061 (i) ## 135 i1i2 ~~ i4i6 2.130 0.671 0.308 0.1 0.047 0.055 (i) ## 170 i7i8 ~~ i11i12 1.807 0.495 0.271 0.1 0.074 0.058 (i) ## 138 i1i2 ~~ i10i11 1.734 0.473 0.226 0.1 0.078 0.059 (i) ## 154 i4i5 ~~ i10i11 1.730 0.445 0.217 0.1 0.087 0.060 (i) ## 156 i4i5 ~~ i2i3 1.460 -0.466 -0.192 0.1 0.067 0.058 (i) ## 150 i1i3 ~~ i8i9 1.409 0.500 0.239 0.1 0.056 0.056 (i) ## 41 i10i12 ~~ i10i12 1.258 -0.493 -0.425 0.1 0.052 0.056 (i) ## 38 i7i9 ~~ i7i9 1.220 -0.529 -0.481 0.1 0.044 0.055 (i) ## 134 i1i2 ~~ i4i5 1.161 0.492 0.206 0.1 0.048 0.056 (i) ## 174 i7i9 ~~ i5i6 1.032 -0.353 -0.161 0.1 0.083 0.060 (i) ## 171 i7i9 ~~ i10i11 1.024 -0.321 -0.166 0.1 0.099 0.061 (i) ## 151 i1i3 ~~ i11i12 1.018 -0.385 -0.207 0.1 0.069 0.058 (i) ## 132 trait3 =~ i7i8 0.869 0.194 0.090 0.1 0.231 0.077 (i) ## 182 i2i3 ~~ i5i6 0.822 -0.330 -0.137 0.1 0.075 0.059 (i) ## 144 i1i3 ~~ i4i6 0.727 -0.342 -0.169 0.1 0.062 0.057 (i) ## 143 i1i3 ~~ i4i5 0.719 0.326 0.148 0.1 0.068 0.058 (i) ## 40 i10i11 ~~ i10i11 0.684 0.342 0.590 0.1 0.058 0.057 (i) ## 127 trait2 =~ i4i6 0.662 -0.150 -0.067 0.1 0.294 0.084 (i) ## 5 trait1 =~ i7i8 0.651 -0.122 -0.057 0.1 0.435 0.101 (i) ## 34 i4i5 ~~ i4i5 0.637 -0.509 -0.477 0.1 0.025 0.053 (i) ## 50 i7i8 ~~ i8i9 0.587 0.258 0.126 0.1 0.088 0.060 (i) ## 24 trait3 =~ i11i12 0.582 0.156 0.077 0.1 0.238 0.078 (i) ## 39 i8i9 ~~ i8i9 0.578 0.423 0.428 0.1 0.032 0.054 (i) ## 131 trait3 =~ i4i5 0.560 -0.133 -0.060 0.1 0.317 0.087 (i) ## 53 i10i11 ~~ i11i12 0.525 0.181 0.105 0.1 0.160 0.069 (i) ## 142 i1i2 ~~ i11i12 0.503 0.299 0.149 0.1 0.056 0.056 (i) ## 172 i7i9 ~~ i10i12 0.465 -0.257 -0.130 0.1 0.071 0.058 (i) ## 6 trait1 =~ i7i9 0.421 0.079 0.038 0.1 0.673 0.130 (i) ## 187 i8i9 ~~ i11i12 0.408 0.262 0.140 0.1 0.059 0.057 (i) ## 157 i4i5 ~~ i8i9 0.406 -0.262 -0.118 0.1 0.059 0.057 (i) ## 36 i5i6 ~~ i5i6 0.380 0.346 0.607 0.1 0.032 0.054 (i) ## 168 i7i8 ~~ i2i3 0.363 -0.241 -0.107 0.1 0.062 0.057 (i) ## 162 i4i6 ~~ i10i12 0.346 0.230 0.120 0.1 0.066 0.058 (i) ## 16 trait2 =~ i11i12 0.323 -0.078 -0.038 0.1 0.537 0.113 (i) ## 161 i4i6 ~~ i10i11 0.314 -0.188 -0.100 0.1 0.089 0.060 (i) ## 32 i1i3 ~~ i1i3 0.272 0.272 0.441 0.1 0.037 0.054 (i) ## 14 trait2 =~ i8i9 0.262 -0.095 -0.043 0.1 0.289 0.084 (i) ## 54 i10i12 ~~ i11i12 0.247 0.149 0.085 0.1 0.111 0.063 (i) ## 17 trait3 =~ i1i3 0.240 0.063 0.029 0.1 0.597 0.121 (i) ## 164 i4i6 ~~ i8i9 0.225 0.194 0.096 0.1 0.060 0.057 (i) ## 11 trait2 =~ i4i5 0.224 -0.055 -0.025 0.1 0.743 0.139 (i) ## 167 i7i8 ~~ i10i12 0.217 -0.178 -0.092 0.1 0.068 0.058 (i) ## 129 trait2 =~ i10i12 0.214 0.089 0.042 0.1 0.273 0.082 (i) ## 23 trait3 =~ i10i12 0.209 -0.056 -0.027 0.1 0.664 0.129 (i) ## 37 i7i8 ~~ i7i8 0.207 0.228 0.434 0.1 0.040 0.055 (i) ## 12 trait2 =~ i5i6 0.205 -0.050 -0.026 0.1 0.812 0.147 (i) ## 49 i7i8 ~~ i7i9 0.197 0.141 0.069 0.1 0.100 0.061 (i) ## 48 i4i6 ~~ i5i6 0.195 -0.135 -0.064 0.1 0.107 0.062 (i) ## 137 i1i2 ~~ i7i9 0.190 0.188 0.084 0.1 0.054 0.056 (i) ## 13 trait2 =~ i7i8 0.190 -0.069 -0.032 0.1 0.399 0.097 (i) ## 33 i2i3 ~~ i2i3 0.171 -0.199 -0.572 0.1 0.043 0.055 (i) ## 42 i11i12 ~~ i11i12 0.161 0.179 0.404 0.1 0.050 0.056 (i) ## 18 trait3 =~ i2i3 0.154 -0.041 -0.019 0.1 0.931 0.162 (i) ## 185 i5i6 ~~ i8i9 0.148 -0.141 -0.064 0.1 0.074 0.059 (i) ## 44 i1i2 ~~ i2i3 0.122 -0.129 -0.052 0.1 0.074 0.058 (i) ## 43 i1i2 ~~ i1i3 0.122 -0.133 -0.059 0.1 0.069 0.058 (i) ## 15 trait2 =~ i10i11 0.119 -0.029 -0.016 0.1 1.454 0.226 (i) ## 159 i4i6 ~~ i7i8 0.117 -0.140 -0.071 0.1 0.060 0.057 (i) ## 46 i4i5 ~~ i4i6 0.101 0.111 0.052 0.1 0.082 0.059 (i) ## 163 i4i6 ~~ i2i3 0.095 -0.117 -0.053 0.1 0.070 0.058 (i) ## 22 trait3 =~ i8i9 0.094 -0.056 -0.025 0.1 0.297 0.085 (i) ## 51 i7i9 ~~ i8i9 0.093 0.099 0.048 0.1 0.095 0.061 (i) ## 8 trait1 =~ i10i12 0.091 0.034 0.016 0.1 0.770 0.142 (i) ## 158 i4i5 ~~ i11i12 0.091 -0.110 -0.056 0.1 0.075 0.059 (i) ## 126 trait2 =~ i1i3 0.086 0.061 0.028 0.1 0.231 0.077 (i) ## 147 i1i3 ~~ i10i11 0.084 -0.090 -0.047 0.1 0.104 0.062 (i) ## 7 trait1 =~ i10i11 0.084 -0.032 -0.018 0.1 0.839 0.150 (i) ## 9 trait2 =~ i1i2 0.059 -0.049 -0.020 0.1 0.244 0.078 (i) ## 169 i7i8 ~~ i5i6 0.054 -0.083 -0.039 0.1 0.078 0.059 (i) ## 1 trait1 =~ i1i2 0.052 -0.040 -0.016 0.1 0.325 0.088 (i) ## 165 i4i6 ~~ i11i12 0.048 -0.084 -0.046 0.1 0.068 0.058 (i) ## 141 i1i2 ~~ i8i9 0.047 0.103 0.045 0.1 0.045 0.055 (i) ## 21 trait3 =~ i7i9 0.043 0.026 0.012 0.1 0.645 0.126 (i) ## 178 i10i11 ~~ i8i9 0.037 0.063 0.033 0.1 0.091 0.061 (i) ## 146 i1i3 ~~ i7i9 0.034 -0.072 -0.035 0.1 0.066 0.058 (i) ## 183 i2i3 ~~ i8i9 0.034 0.075 0.033 0.1 0.060 0.057 (i) ## 35 i4i6 ~~ i4i6 0.033 0.097 0.395 0.1 0.035 0.054 (i) ## 133 trait3 =~ i10i11 0.032 -0.029 -0.016 0.1 0.396 0.096 (i) ## 19 trait3 =~ i4i6 0.025 0.019 0.009 0.1 0.660 0.128 (i) ## 2 trait1 =~ i1i3 0.024 0.019 0.009 0.1 0.699 0.133 (i) ## 52 i10i11 ~~ i10i12 0.020 0.037 0.020 0.1 0.150 0.067 (i) ## 10 trait2 =~ i2i3 0.016 -0.014 -0.006 0.1 0.885 0.156 (i) ## 20 trait3 =~ i5i6 0.014 -0.011 -0.006 0.1 1.166 0.191 (i) ## 160 i4i6 ~~ i7i9 0.010 -0.042 -0.021 0.1 0.056 0.056 (i) ## 152 i4i5 ~~ i7i8 0.006 0.032 0.015 0.1 0.058 0.057 (i) ## 181 i10i12 ~~ i8i9 0.006 0.031 0.015 0.1 0.061 0.057 (i) ## 31 i1i2 ~~ i1i2 0.003 -0.039 -0.394 0.1 0.023 0.053 (i) ## 47 i4i5 ~~ i5i6 0.002 -0.014 -0.006 0.1 0.107 0.062 (i) ## 45 i1i3 ~~ i2i3 0.002 -0.015 -0.006 0.1 0.097 0.061 (i) ## 4 trait1 =~ i4i6 0.001 -0.006 -0.002 0.1 0.409 0.098 (i) ## 3 trait1 =~ i4i5 0.001 0.003 0.001 0.1 0.722 0.136 (i) ## 136 i1i2 ~~ i7i8 0.000 -0.007 -0.003 0.1 0.043 0.055 (i) ## 65 i8i9 | t1 0.000 0.000 0.000 0.1 0.409 0.098 (i) ## 62 i10i12 | t1 0.000 0.000 0.000 0.1 0.456 0.104 (i) ## 63 i2i3 | t1 0.000 0.000 0.000 0.1 0.456 0.104 (i) ## 58 i4i6 | t1 0.000 0.000 0.000 0.1 0.404 0.097 (i) ## 56 i1i3 | t1 0.000 0.000 0.000 0.1 0.424 0.100 (i) ## 57 i4i5 | t1 0.000 0.000 0.000 0.1 0.407 0.098 (i) ## 61 i10i11 | t1 0.000 0.000 0.000 0.1 0.657 0.128 (i) ## 64 i5i6 | t1 0.000 0.000 0.000 0.1 0.528 0.112 (i) ## 60 i7i9 | t1 0.000 0.000 0.000 0.1 0.463 0.104 (i) ## 30 trait2 ~~ trait3 0.000 0.000 0.000 0.1 5.225 0.628 (i) ## 66 i11i12 | t1 0.000 0.000 0.000 0.1 0.487 0.107 (i) ## 29 trait1 ~~ trait3 0.000 0.000 0.000 0.1 6.167 0.700 (i) ## 28 trait1 ~~ trait2 0.000 0.000 0.000 0.1 5.631 0.660 (i) ## 55 i1i2 | t1 0.000 0.000 0.000 0.1 0.324 0.088 (i) ## 59 i7i8 | t1 0.000 0.000 0.000 0.1 0.431 0.101 (i)
score<-predict(fit_lavaan)
score<-data.frame(score)
score<-reshape(score, direction="wide", idvar="id", timevar="trait")
triplets_new<-make_TIRT_data(data=triplets[1,],blocks=blocks,direction="larger",format="pairwise",family="bernoulli",range=c(0,1))
score_new<-predict(fit_stan,newdata=triplets_new)## ## SAMPLING FOR MODEL 'thurstonian_irt_model_newdata' NOW (CHAIN 1). ## Chain 1: ## Chain 1: Gradient evaluation took 2.2e-05 seconds ## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds. ## Chain 1: Adjust your expectations accordingly! ## Chain 1: ## Chain 1: ## Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup) ## Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup) ## Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup) ## Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup) ## Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup) ## Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup) ## Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling) ## Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling) ## Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling) ## Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling) ## Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling) ## Chain 1: Iteration: 2000 / 2000 [100%] (Sampling) ## Chain 1: ## Chain 1: Elapsed Time: 0.026 seconds (Warm-up) ## Chain 1: 0.022 seconds (Sampling) ## Chain 1: 0.048 seconds (Total) ## Chain 1: ## ## SAMPLING FOR MODEL 'thurstonian_irt_model_newdata' NOW (CHAIN 2). ## Chain 2: ## Chain 2: Gradient evaluation took 9e-06 seconds ## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds. ## Chain 2: Adjust your expectations accordingly! ## Chain 2: ## Chain 2: ## Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup) ## Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup) ## Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup) ## Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup) ## Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup) ## Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup) ## Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling) ## Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling) ## Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling) ## Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling) ## Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling) ## Chain 2: Iteration: 2000 / 2000 [100%] (Sampling) ## Chain 2: ## Chain 2: Elapsed Time: 0.024 seconds (Warm-up) ## Chain 2: 0.027 seconds (Sampling) ## Chain 2: 0.051 seconds (Total) ## Chain 2: ## ## SAMPLING FOR MODEL 'thurstonian_irt_model_newdata' NOW (CHAIN 3). ## Chain 3: ## Chain 3: Gradient evaluation took 7e-06 seconds ## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds. ## Chain 3: Adjust your expectations accordingly! ## Chain 3: ## Chain 3: ## Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup) ## Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup) ## Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup) ## Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup) ## Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup) ## Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup) ## Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling) ## Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling) ## Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling) ## Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling) ## Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling) ## Chain 3: Iteration: 2000 / 2000 [100%] (Sampling) ## Chain 3: ## Chain 3: Elapsed Time: 0.025 seconds (Warm-up) ## Chain 3: 0.024 seconds (Sampling) ## Chain 3: 0.049 seconds (Total) ## Chain 3: ## ## SAMPLING FOR MODEL 'thurstonian_irt_model_newdata' NOW (CHAIN 4). ## Chain 4: ## Chain 4: Gradient evaluation took 1.2e-05 seconds ## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds. ## Chain 4: Adjust your expectations accordingly! ## Chain 4: ## Chain 4: ## Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup) ## Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup) ## Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup) ## Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup) ## Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup) ## Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup) ## Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling) ## Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling) ## Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling) ## Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling) ## Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling) ## Chain 4: Iteration: 2000 / 2000 [100%] (Sampling) ## Chain 4: ## Chain 4: Elapsed Time: 0.024 seconds (Warm-up) ## Chain 4: 0.023 seconds (Sampling) ## Chain 4: 0.047 seconds (Total) ## Chain 4:
## id estimate.trait1 estimate.trait2 estimate.trait3 ## 1 1 0.29959612 -1.207670320 0.142639499 ## 4 2 -0.91546978 0.857938113 0.640172491 ## 7 3 -0.63050199 1.344070053 0.876947847 ## 10 4 -0.95227135 -0.400266051 0.062057101 ## 13 5 0.91060103 -0.013062837 -0.103896716 ## 16 6 0.53474318 -0.657262524 -1.496230931 ## 19 7 0.50084875 0.417296930 -0.100795239 ## 22 8 0.64274984 0.509052826 0.600521614 ## 25 9 -0.14797646 -0.201027003 0.452780639 ## 28 10 -1.67301586 -0.067634487 -0.090043441 ## 31 11 0.19130791 -0.531692241 -1.166319550 ## 34 12 1.22582070 -0.312817717 1.046563217 ## 37 13 1.06819602 0.055147310 0.653777001 ## 40 14 -0.63324308 -0.092162983 0.042143818 ## 43 15 -0.92322735 0.508133969 0.957104997 ## 46 16 -1.36877000 -0.349539762 -0.985868620 ## 49 17 0.33612610 -1.317169585 0.289535847 ## 52 18 -0.25812653 -0.918385667 -0.372330485 ## 55 19 -0.67595255 0.272655349 -0.186202012 ## 58 20 1.02244427 -0.418969750 0.167893731 ## 61 21 -0.18821603 -0.086412967 0.396839492 ## 64 22 1.46593382 -0.090659593 0.214550071 ## 67 23 0.52576641 0.215095080 0.232609958 ## 70 24 -1.23293816 -0.478590482 0.423086466 ## 73 25 -0.18821603 -0.086412967 0.396839492 ## 76 26 1.42281158 -0.928101936 -0.796577676 ## 79 27 0.62246377 0.530430324 0.678305271 ## 82 28 -1.46178687 0.919132708 0.361965550 ## 85 29 0.15182427 1.359832724 0.461633216 ## 88 30 -0.21585559 -1.289381767 -0.080481148 ## 91 31 0.89568844 0.754318688 -0.077895605 ## 94 32 -0.46151059 0.594856339 1.523780847 ## 97 33 0.73595215 -1.306298391 -0.108309897 ## 100 34 -0.13183975 1.167968807 1.267037226 ## 103 35 -0.30351258 0.094679777 0.078611280 ## 106 36 -0.38186034 1.323656713 -0.001251328 ## 109 37 -0.23413822 -1.255498155 -0.863847350 ## 112 38 -0.76901178 1.549168427 0.515933310 ## 115 39 1.19535455 -0.818761519 0.536247921 ## 118 40 0.55200858 -0.889556225 -0.753534296 ## 121 41 -0.16422942 0.185346282 0.077023310 ## 124 42 -0.45108333 -0.372379552 0.275545199 ## 127 43 1.00499245 -1.312281816 -0.919853343 ## 130 44 0.19955936 1.075846042 0.909812544 ## 133 45 -0.18221569 -0.128629332 0.389063792 ## 136 46 -0.60178536 -0.046262342 -1.301456071 ## 139 47 -0.93153688 -0.754980740 0.369345074 ## 142 48 0.19137120 -0.902860435 -0.808760660 ## 145 49 0.16479912 -0.877424434 -0.897265730 ## 148 50 -0.22557273 1.586114614 1.009049733 ## 151 51 0.47854784 -0.030047752 -0.523116802 ## 154 52 0.38218160 -0.342967508 1.322794753 ## 157 53 -0.49502699 -0.048776019 0.104553341 ## 160 54 -1.01690768 0.788111017 -0.926237336 ## 163 55 0.28068671 0.699134950 1.174916170 ## 166 56 0.53450954 -0.408716839 -0.294171598 ## 169 57 -0.24370497 1.114748460 0.342822297 ## 172 58 0.83891775 -0.114682262 1.522413951 ## 175 59 -1.31073957 -0.006270742 -0.731030962 ## 178 60 1.06819602 0.055147310 0.653777001 ## 181 61 0.61043008 -0.161341584 0.500193695 ## 184 62 -0.17463438 0.720616045 0.589519755 ## 187 63 -1.04969817 1.125803745 -0.412366505 ## 190 64 -0.17463438 0.720616045 0.589519755 ## 193 65 0.32502949 0.372177079 -0.150515909 ## 196 66 0.55076514 0.909481364 0.411280814 ## 199 67 0.62246377 0.530430324 0.678305271 ## 202 68 -0.53087101 -1.713030040 -0.913633664 ## 205 69 -1.12269119 1.339625079 0.463378705 ## 208 70 -0.13183975 1.167968807 1.267037226 ## 211 71 0.56737374 0.050219124 0.159730736 ## 214 72 -0.23927976 0.712922540 -0.280403210 ## 217 73 -0.02320602 0.846722056 -0.079373997 ## 220 74 0.61043008 -0.161341584 0.500193695 ## 223 75 -0.86762032 0.177339408 1.325927484 ## 226 76 1.03430618 -0.379008738 0.254024765 ## 229 77 0.97219765 0.446589811 0.377223374 ## 232 78 -0.13183975 1.167968807 1.267037226 ## 235 79 -0.20713794 0.828483091 0.751957099 ## 238 80 -1.84223848 0.924769271 -0.031337051 ## 241 81 0.22230490 -0.937166809 -0.033846499 ## 244 82 -0.92618695 -0.503557208 -0.643349333 ## 247 83 0.20199354 0.299770267 0.534622134 ## 250 84 1.46593382 -0.090659593 0.214550071 ## 253 85 0.73595215 -1.306298391 -0.108309897 ## 256 86 -1.46762978 1.340257438 0.065250696 ## 259 87 -0.28115241 0.423656864 -0.794146919 ## 262 88 -0.56951415 -0.121141223 -1.309260498 ## 265 89 0.22235064 0.956735995 0.723800939 ## 268 90 0.38218160 -0.342967508 1.322794753 ## 271 91 0.62246377 0.530430324 0.678305271 ## 274 92 -0.92322735 0.508133969 0.957104997 ## 277 93 0.74180721 -0.515496381 0.870773720 ## 280 94 1.06819602 0.055147310 0.653777001 ## 283 95 0.22235064 0.956735995 0.723800939 ## 286 96 0.89696384 -0.529380932 -0.693274267 ## 289 97 -0.52729709 0.054914312 0.843091403 ## 292 98 -0.94595556 -0.880794999 -1.169454485 ## 295 99 0.13479088 0.669625628 0.280268839 ## 298 100 -0.95227135 -0.400266051 0.062057101 ## 301 101 -0.83223767 0.461765911 -0.339650704 ## 304 102 -0.11044010 -0.221046539 0.465422403 ## 307 103 -1.15825756 -0.327569769 0.280134199 ## 310 104 0.05417401 -0.483841563 -1.012573127 ## 313 105 1.66293718 -0.510792782 0.599522371 ## 316 106 -0.27045278 -0.785781656 -0.380627305 ## 319 107 0.05417401 -0.483841563 -1.012573127 ## 322 108 0.15880914 0.087424211 0.477594576 ## 325 109 -1.39414339 -0.024400266 -1.392540833 ## 328 110 0.16479912 -0.877424434 -0.897265730 ## 331 111 0.99108098 -1.083993324 -0.872468564 ## 334 112 -0.59656198 -0.954237102 -1.623919678 ## 337 113 0.08470990 -0.687250379 -1.180511985 ## 340 114 -0.24370497 1.114748460 0.342822297 ## 343 115 -0.92618695 -0.503557208 -0.643349333 ## 346 116 -0.46414349 -0.262554663 1.201191902 ## 349 117 1.02244427 -0.418969750 0.167893731 ## 352 118 -1.01690768 0.788111017 -0.926237336 ## 355 119 0.71321865 -1.503357842 -0.936675524 ## 358 120 0.11719862 -0.477828543 -0.529382935 ## 361 121 0.03781862 -0.142462924 -1.384242821 ## 364 122 0.48551395 -0.105896769 -0.313236935 ## 367 123 0.31888701 0.743267323 -0.535591430 ## 370 124 -0.55761160 0.103197573 -1.098872898 ## 373 125 0.29006574 0.909912495 0.572215453 ## 376 126 0.22230490 -0.937166809 -0.033846499 ## 379 127 0.62246377 0.530430324 0.678305271 ## 382 128 1.42281158 -0.928101936 -0.796577676 ## 385 129 0.20050224 -0.532877294 0.359096941 ## 388 130 -0.29182157 -0.518097726 -1.452589365 ## 391 131 -1.70748405 0.084601716 -0.361408946 ## 394 132 -0.21022713 0.500296861 0.526956875 ## 397 133 1.43343801 -0.559783990 -0.228968358 ## 400 134 -0.24370497 1.114748460 0.342822297 ## 403 135 -0.00300603 1.173385952 -0.509483072 ## 406 136 0.81694929 0.051641015 -1.011031502 ## 409 137 1.29110166 -0.471601358 -1.061311970 ## 412 138 1.34938181 0.328549063 -0.059951046 ## 415 139 -0.20713794 0.828483091 0.751957099 ## 418 140 -1.76752609 0.404599199 -0.487476575 ## 421 141 -1.74144693 0.469214402 0.212199134 ## 424 142 0.22230490 -0.937166809 -0.033846499 ## 427 143 -0.13183975 1.167968807 1.267037226 ## 430 144 -0.59534586 1.082894648 -0.070736632 ## 433 145 -0.92322735 0.508133969 0.957104997 ## 436 146 0.10974275 -0.654727801 -1.844673830 ## 439 147 1.62733724 -1.397603888 -0.424101496 ## 442 148 -0.47161364 -0.036813948 -0.703050920 ## 445 149 0.99108098 -1.083993324 -0.872468564 ## 448 150 -0.13952328 -0.935143799 -0.387990731 ## 451 151 -0.13023829 -0.432716062 0.733100051 ## 454 152 -0.55271223 0.726321450 -0.600323287 ## 457 153 0.23467967 -1.264851459 -0.530479830 ## 460 154 -0.92322735 0.508133969 0.957104997 ## 463 155 -0.27859511 -0.849298813 -1.086625269 ## 466 156 -1.01406944 0.919435285 -0.437614713 ## 469 157 -1.80646233 0.529106753 -0.617221814 ## 472 158 -0.04645820 0.142117010 1.396214416 ## 475 159 -1.68554927 -0.021866034 -0.234626503 ## 478 160 -1.04713555 0.711240062 -0.124976513 ## 481 161 -0.63050199 1.344070053 0.876947847 ## 484 162 0.55076514 0.909481364 0.411280814 ## 487 163 -0.51979360 -0.684067717 -0.092414525 ## 490 164 -0.97783340 0.376534848 -1.288646914 ## 493 165 0.60758897 -0.541689243 0.142217441 ## 496 166 -0.25193401 -0.893488062 -1.228888365 ## 499 167 0.41674363 -0.855968053 -0.747486197 ## 502 168 1.22582070 -0.312817717 1.046563217 ## 505 169 0.19940816 -0.580017476 -0.331141109 ## 508 170 1.06819602 0.055147310 0.653777001 ## 511 171 0.13479088 0.669625628 0.280268839 ## 514 172 0.30747768 0.742297680 1.322608068 ## 517 173 -0.94822050 -0.833680092 -1.160461185 ## 520 174 -0.99213468 -0.445717439 -1.403773567 ## 523 175 -1.39617701 0.415009201 -0.991329744 ## 526 176 -0.08453922 -1.093776996 0.529427996 ## 529 177 -0.15687690 -0.178974157 -0.291421257 ## 532 178 0.64274984 0.509052826 0.600521614 ## 535 179 0.16716680 0.590561477 0.265685614 ## 538 180 -0.13593232 0.457881446 1.007944705 ## 541 181 1.22582070 -0.312817717 1.046563217 ## 544 182 -0.19605231 -1.489203935 -1.729930695 ## 547 183 0.81694929 0.051641015 -1.011031502 ## 550 184 -0.49764054 0.516995068 0.180349416 ## 553 185 -1.12269119 1.339625079 0.463378705 ## 556 186 0.62863139 -1.463867877 -0.982358908 ## 559 187 1.38174962 -0.438010843 -0.356013428 ## 562 188 1.00499245 -1.312281816 -0.919853343 ## 565 189 0.91211086 -0.810811473 -0.469249769 ## 568 190 -0.17972894 -0.058695099 -0.972135521 ## 571 191 0.11177255 0.319578728 -0.297883751 ## 574 192 0.46102151 0.330072149 -0.890332424 ## 577 193 0.38218160 -0.342967508 1.322794753 ## 580 194 0.15061356 -0.158993360 -1.402061770 ## 583 195 0.91211086 -0.810811473 -0.469249769 ## 586 196 -0.11065533 -1.276475025 -0.880467681 ## 589 197 -0.63050199 1.344070053 0.876947847 ## 592 198 -1.46178687 0.919132708 0.361965550 ## 595 199 0.61043008 -0.161341584 0.500193695 ## 598 200 0.34403487 -1.670655183 -0.223796394
## id estimate.t1 estimate.t2 estimate.t3 ## 1 1 0.3416555 -1.331856 0.3334887