── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
This is lavaan 0.6-18
lavaan is FREE software! Please report any bugs.
Attaching package: 'psych'
The following object is masked from 'package:lavaan':
cor2cov
The following objects are masked from 'package:ggplot2':
%+%, alpha
library (knitr)library (kableExtra)
Attaching package: 'kableExtra'
The following object is masked from 'package:dplyr':
group_rows
library (lavaanPlot)library (corrplot)
ID X1_1 X1_2 X1_3 X1_4 X1_5 Y1_1 Y1_2
1 1 0.146118 0.101487 0.293875 0.179214 0.101160 -0.098501 0.461699
2 2 0.199538 0.174609 0.245947 0.311929 0.345243 0.401786 0.307111
3 3 -0.236111 0.186394 -0.150179 -0.280665 -0.294195 -0.008738 0.574673
4 4 0.418547 0.010475 0.087123 0.202448 0.190860 0.385775 0.035733
5 5 0.257057 0.274397 0.173706 0.144743 0.149408 0.600394 0.437379
6 6 0.757936 0.161540 -0.056032 -0.020761 0.188245 0.029327 0.656981
Y1_3 Y1_4 Y1_5
1 -0.327241 0.053864 -0.060264
2 0.582601 0.246816 0.281764
3 0.434179 0.525106 0.171506
4 0.098704 0.518664 0.322222
5 0.842866 0.700347 0.502234
6 0.219725 0.682058 0.270192
'data.frame': 1189 obs. of 11 variables:
$ ID : int 1 2 3 4 5 6 7 8 9 10 ...
$ X1_1: num 0.146 0.2 -0.236 0.419 0.257 ...
$ X1_2: num 0.1015 0.1746 0.1864 0.0105 0.2744 ...
$ X1_3: num 0.2939 0.2459 -0.1502 0.0871 0.1737 ...
$ X1_4: num 0.179 0.312 -0.281 0.202 0.145 ...
$ X1_5: num 0.101 0.345 -0.294 0.191 0.149 ...
$ Y1_1: num -0.0985 0.40179 -0.00874 0.38577 0.60039 ...
$ Y1_2: num 0.4617 0.3071 0.5747 0.0357 0.4374 ...
$ Y1_3: num -0.3272 0.5826 0.4342 0.0987 0.8429 ...
$ Y1_4: num 0.0539 0.2468 0.5251 0.5187 0.7003 ...
$ Y1_5: num -0.0603 0.2818 0.1715 0.3222 0.5022 ...
describe (RI_CLPM_Data[,- 1 ])
vars n mean sd median trimmed mad min max range skew kurtosis
X1_1 1 1189 0.24 0.25 0.23 0.24 0.26 -0.46 0.99 1.46 0.07 -0.23
X1_2 2 1189 0.17 0.20 0.17 0.17 0.21 -0.41 0.84 1.25 0.10 -0.12
X1_3 3 1189 0.19 0.20 0.19 0.19 0.20 -0.50 0.94 1.44 -0.03 0.03
X1_4 4 1189 0.12 0.21 0.12 0.12 0.21 -0.51 0.84 1.35 -0.10 -0.04
X1_5 5 1189 0.11 0.21 0.11 0.11 0.21 -0.48 0.81 1.30 0.08 -0.11
Y1_1 6 1189 0.34 0.31 0.32 0.33 0.32 -0.63 1.37 2.00 0.08 -0.01
Y1_2 7 1189 0.35 0.32 0.34 0.35 0.34 -0.57 1.43 2.00 0.07 -0.16
Y1_3 8 1189 0.32 0.33 0.31 0.32 0.33 -0.71 1.62 2.33 0.11 0.23
Y1_4 9 1189 0.38 0.34 0.38 0.38 0.35 -0.63 1.41 2.03 0.07 -0.13
Y1_5 10 1189 0.39 0.33 0.38 0.38 0.33 -0.63 1.32 1.95 0.07 -0.28
se
X1_1 0.01
X1_2 0.01
X1_3 0.01
X1_4 0.01
X1_5 0.01
Y1_1 0.01
Y1_2 0.01
Y1_3 0.01
Y1_4 0.01
Y1_5 0.01
<- cor (RI_CLPM_Data[,- 1 ]) corrplot (cor_matrix, method = "circle" ,addCoef.col = "black" )
<- RI_CLPM_Data %>% pivot_longer (cols = starts_with (c ("X1_" ,"Y1_" )), # Select columns for X1 and Y1 names_to = c (".value" , "Wave" ), # .value keeps X1 and Y1, Wave will be new column names_sep = "_" # Separator for variable and wave number str (RI_CLPM_long)
tibble [5,945 × 4] (S3: tbl_df/tbl/data.frame)
$ ID : int [1:5945] 1 1 1 1 1 2 2 2 2 2 ...
$ Wave: chr [1:5945] "1" "2" "3" "4" ...
$ X1 : num [1:5945] 0.146 0.101 0.294 0.179 0.101 ...
$ Y1 : num [1:5945] -0.0985 0.4617 -0.3272 0.0539 -0.0603 ...
$ Wave <- as.numeric (RI_CLPM_long$ Wave)<- as.data.frame (RI_CLPM_long)
# Plotting individual trajectories (spaghetti plot) ggplot (RI_CLPM_long, aes (x = Wave, y = X1)) + geom_line (alpha = 0.03 , color = "blue" ) + # Individual lines stat_summary (fun = mean, geom = "line" , color = "darkblue" , size = .3 ) + # Group average labs (title = "Plot for X1" ,x = "Wave" , y = "X1" ) + theme_minimal ()
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
# Repeat for Y variable ggplot (RI_CLPM_long, aes (x = Wave, y = Y1)) + geom_line (alpha = 0.03 , color = "red" ) + # Individual lines stat_summary (fun = mean, geom = "line" , color = "darkred" , size = .3 ) + # Group average labs (title = "Plot for Y1" ,x = "Wave" , y = "Y1" ) + theme_minimal ()# Plotting individual trajectories (spaghetti plot) ggplot (RI_CLPM_long, aes (x = Wave, y = X1, group = ID)) + geom_line (alpha = 0.03 , color = "blue" ) + # Individual lines stat_summary (fun = mean, geom = "line" , color = "darkblue" , size = .3 ) + # Group average labs (title = "Spaghetti Plot for X1" ,x = "Wave" , y = "X1" ) + theme_minimal ()# Repeat for Y variable ggplot (RI_CLPM_long, aes (x = Wave, y = Y1, group = ID)) + geom_line (alpha = 0.03 , color = "red" ) + # Individual lines stat_summary (fun = mean, geom = "line" , color = "darkred" , size = .03 ) + # Group average labs (title = "Spaghetti Plot for Y1" ,x = "Wave" , y = "Y1" ) + theme_minimal ()
<- ' I =~ 1*X1_1 + 1*X1_2 + 1*X1_3 + 1*X1_4 + 1*X1_5 S =~ 0*X1_1 + 1*X1_2 + 2*X1_3 + 3*X1_4 + 4*X1_5 ' <- growth (X_lgc_model, estimator= "ML" , data= RI_CLPM_Data, mimic = "Mplus" , missing = "FIML" )summary (X_lgc_fit, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-18 ended normally after 84 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of observations 1189
Number of missing patterns 1
Model Test User Model:
Test statistic 148.118
Degrees of freedom 10
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1040.437
Degrees of freedom 10
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.866
Tucker-Lewis Index (TLI) 0.866
Robust Comparative Fit Index (CFI) 0.866
Robust Tucker-Lewis Index (TLI) 0.866
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) 1203.186
Loglikelihood unrestricted model (H1) 1277.244
Akaike (AIC) -2386.371
Bayesian (BIC) -2335.562
Sample-size adjusted Bayesian (SABIC) -2367.326
Root Mean Square Error of Approximation:
RMSEA 0.108
90 Percent confidence interval - lower 0.093
90 Percent confidence interval - upper 0.123
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.999
Robust RMSEA 0.108
90 Percent confidence interval - lower 0.093
90 Percent confidence interval - upper 0.123
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.999
Standardized Root Mean Square Residual:
SRMR 0.056
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
I =~
X1_1 1.000 0.157 0.628
X1_2 1.000 0.157 0.768
X1_3 1.000 0.157 0.773
X1_4 1.000 0.157 0.760
X1_5 1.000 0.157 0.750
S =~
X1_1 0.000 0.000 0.000
X1_2 1.000 0.044 0.215
X1_3 2.000 0.088 0.432
X1_4 3.000 0.131 0.637
X1_5 4.000 0.175 0.838
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
I ~~
S -0.004 0.001 -7.810 0.000 -0.626 -0.626
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
I 0.225 0.006 37.041 0.000 1.434 1.434
S -0.030 0.002 -15.049 0.000 -0.691 -0.691
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.X1_1 0.038 0.002 17.568 0.000 0.038 0.606
.X1_2 0.024 0.001 18.070 0.000 0.024 0.571
.X1_3 0.026 0.001 20.485 0.000 0.026 0.634
.X1_4 0.026 0.001 19.693 0.000 0.026 0.622
.X1_5 0.023 0.002 14.702 0.000 0.023 0.522
I 0.025 0.002 13.131 0.000 1.000 1.000
S 0.002 0.000 8.961 0.000 1.000 1.000
<- ' I =~ 1*Y1_1 + 1*Y1_2 + 1*Y1_3 + 1*Y1_4 + 1*Y1_5 S =~ 0*Y1_1 + 1*Y1_2 + 2*Y1_3 + 3*Y1_4 + 4*Y1_5 ' <- growth (Y_lgc_model, estimator= "ML" , data= RI_CLPM_Data, mimic = "Mplus" , missing = "FIML" )summary (Y_lgc_fit, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-18 ended normally after 64 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 10
Number of observations 1189
Number of missing patterns 1
Model Test User Model:
Test statistic 150.491
Degrees of freedom 10
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 1503.954
Degrees of freedom 10
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.906
Tucker-Lewis Index (TLI) 0.906
Robust Comparative Fit Index (CFI) 0.906
Robust Tucker-Lewis Index (TLI) 0.906
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) -1118.842
Loglikelihood unrestricted model (H1) -1043.596
Akaike (AIC) 2257.684
Bayesian (BIC) 2308.492
Sample-size adjusted Bayesian (SABIC) 2276.729
Root Mean Square Error of Approximation:
RMSEA 0.109
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.124
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.999
Robust RMSEA 0.109
90 Percent confidence interval - lower 0.094
90 Percent confidence interval - upper 0.124
P-value H_0: Robust RMSEA <= 0.050 0.000
P-value H_0: Robust RMSEA >= 0.080 0.999
Standardized Root Mean Square Residual:
SRMR 0.055
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
I =~
Y1_1 1.000 0.206 0.646
Y1_2 1.000 0.206 0.646
Y1_3 1.000 0.206 0.620
Y1_4 1.000 0.206 0.632
Y1_5 1.000 0.206 0.602
S =~
Y1_1 0.000 0.000 0.000
Y1_2 1.000 0.057 0.180
Y1_3 2.000 0.115 0.346
Y1_4 3.000 0.172 0.528
Y1_5 4.000 0.230 0.671
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
I ~~
S -0.003 0.001 -3.065 0.002 -0.248 -0.248
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
I 0.328 0.008 40.396 0.000 1.592 1.592
S 0.014 0.003 5.301 0.000 0.249 0.249
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
.Y1_1 0.059 0.004 16.581 0.000 0.059 0.583
.Y1_2 0.062 0.003 19.996 0.000 0.062 0.608
.Y1_3 0.066 0.003 20.745 0.000 0.066 0.602
.Y1_4 0.052 0.003 18.770 0.000 0.052 0.488
.Y1_5 0.046 0.003 13.675 0.000 0.046 0.388
I 0.042 0.003 12.440 0.000 1.000 1.000
S 0.003 0.000 8.174 0.000 1.000 1.000
<- ' # Create between components (random intercepts) RIx =~ 1*X1_1 + 1*X1_2 + 1*X1_3 + 1*X1_4 + 1*X1_5 RIy =~ 1*Y1_1 + 1*Y1_2 + 1*Y1_3 + 1*Y1_4 + 1*Y1_5 # Create within-person centered variables w_X1_1 =~ 1*X1_1 w_X1_2 =~ 1*X1_2 w_X1_3 =~ 1*X1_3 w_X1_4 =~ 1*X1_4 w_X1_5 =~ 1*X1_5 w_Y1_1 =~ 1*Y1_1 w_Y1_2 =~ 1*Y1_2 w_Y1_3 =~ 1*Y1_3 w_Y1_4 =~ 1*Y1_4 w_Y1_5 =~ 1*Y1_5 # Estimate lagged effects between within-person centered variables w_X1_2 + w_Y1_2 ~ a*w_X1_1 + b*w_Y1_1 w_X1_3 + w_Y1_3 ~ a*w_X1_2 + b*w_Y1_2 w_X1_4 + w_Y1_4 ~ a*w_X1_3 + b*w_Y1_3 w_X1_5 + w_Y1_5 ~ a*w_X1_4 + b*w_Y1_4 # Estimate covariance between within-person centered variables at first wave w_X1_1 ~~ c*w_Y1_1 # Covariance # Estimate covariances between residuals of within-person centered variables # (i.e., innovations) w_X1_2 ~~ c*w_Y1_2 w_X1_3 ~~ c*w_Y1_3 w_X1_4 ~~ c*w_Y1_4 w_X1_5 ~~ c*w_Y1_5 # Estimate variance and covariance of random intercepts RIx ~~ RIx RIy ~~ RIy RIx ~~ RIy # Estimate (residual) variance of within-person centered variables w_X1_1 ~~ w_X1_1 # Variances w_Y1_1 ~~ w_Y1_1 w_X1_2 ~~ w_X1_2 # Residual variances w_Y1_2 ~~ w_Y1_2 w_X1_3 ~~ w_X1_3 w_Y1_3 ~~ w_Y1_3 w_X1_4 ~~ w_X1_4 w_Y1_4 ~~ w_Y1_4 w_X1_5 ~~ w_X1_5 w_Y1_5 ~~ w_Y1_5 ' <- growth (RICLPM, estimator= "ML" , data= RI_CLPM_Data, mimic = "Mplus" , missing = "FIML" )
Warning: lavaan->lav_object_post_check():
some estimated ov variances are negative
summary (RICLPM_fit, fit.measures = TRUE , standardized= TRUE )
lavaan 0.6-18 ended normally after 346 iterations
Estimator ML
Optimization method NLMINB
Number of model parameters 60
Number of equality constraints 18
Number of observations 1189
Number of missing patterns 1
Model Test User Model:
Test statistic 168.305
Degrees of freedom 23
P-value (Chi-square) 0.000
Model Test Baseline Model:
Test statistic 3166.400
Degrees of freedom 45
P-value 0.000
User Model versus Baseline Model:
Comparative Fit Index (CFI) 0.953
Tucker-Lewis Index (TLI) 0.909
Robust Comparative Fit Index (CFI) 0.955
Robust Tucker-Lewis Index (TLI) 0.911
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) 460.500
Loglikelihood unrestricted model (H1) 544.652
Akaike (AIC) -837.000
Bayesian (BIC) -623.603
Sample-size adjusted Bayesian (SABIC) -757.011
Root Mean Square Error of Approximation:
RMSEA 0.073
90 Percent confidence interval - lower 0.063
90 Percent confidence interval - upper 0.083
P-value H_0: RMSEA <= 0.050 0.000
P-value H_0: RMSEA >= 0.080 0.136
Robust RMSEA 0.072
90 Percent confidence interval - lower 0.061
90 Percent confidence interval - upper 0.083
P-value H_0: Robust RMSEA <= 0.050 0.001
P-value H_0: Robust RMSEA >= 0.080 0.126
Standardized Root Mean Square Residual:
SRMR 0.034
Parameter Estimates:
Standard errors Standard
Information Observed
Observed information based on Hessian
Latent Variables:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
RIx =~
X1_1 1.000 0.095 0.390
X1_2 1.000 0.095 0.466
X1_3 1.000 0.095 0.476
X1_4 1.000 0.095 0.461
X1_5 1.000 0.095 0.449
RIy =~
Y1_1 1.000 0.212 0.690
Y1_2 1.000 0.212 0.631
Y1_3 1.000 0.212 0.646
Y1_4 1.000 0.212 0.651
Y1_5 1.000 0.212 0.632
w_X1_1 =~
X1_1 1.000 0.259 1.056
w_X1_2 =~
X1_2 1.000 0.239 1.166
w_X1_3 =~
X1_3 1.000 0.255 1.271
w_X1_4 =~
X1_4 1.000 0.282 1.364
w_X1_5 =~
X1_5 1.000 0.182 0.855
w_Y1_1 =~
Y1_1 1.000 0.781 2.538
w_Y1_2 =~
Y1_2 1.000 1.283 3.813
w_Y1_3 =~
Y1_3 1.000 1.095 3.332
w_Y1_4 =~
Y1_4 1.000 1.588 4.875
w_Y1_5 =~
Y1_5 1.000 0.179 0.532
Regressions:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
w_X1_2 ~
w_X1_1 (a) 0.128 0.138 0.925 0.355 0.138 0.138
w_Y1_1 (b) 0.003 0.054 0.054 0.957 0.010 0.010
w_Y1_2 ~
w_X1_1 (a) 0.128 0.138 0.925 0.355 0.026 0.026
w_Y1_1 (b) 0.003 0.054 0.054 0.957 0.002 0.002
w_X1_3 ~
w_X1_2 (a) 0.128 0.138 0.925 0.355 0.119 0.119
w_Y1_2 (b) 0.003 0.054 0.054 0.957 0.015 0.015
w_Y1_3 ~
w_X1_2 (a) 0.128 0.138 0.925 0.355 0.028 0.028
w_Y1_2 (b) 0.003 0.054 0.054 0.957 0.003 0.003
w_X1_4 ~
w_X1_3 (a) 0.128 0.138 0.925 0.355 0.115 0.115
w_Y1_3 (b) 0.003 0.054 0.054 0.957 0.011 0.011
w_Y1_4 ~
w_X1_3 (a) 0.128 0.138 0.925 0.355 0.020 0.020
w_Y1_3 (b) 0.003 0.054 0.054 0.957 0.002 0.002
w_X1_5 ~
w_X1_4 (a) 0.128 0.138 0.925 0.355 0.198 0.198
w_Y1_4 (b) 0.003 0.054 0.054 0.957 0.025 0.025
w_Y1_5 ~
w_X1_4 (a) 0.128 0.138 0.925 0.355 0.202 0.202
w_Y1_4 (b) 0.003 0.054 0.054 0.957 0.026 0.026
Covariances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
w_X1_1 ~~
w_Y1_1 (c) 0.013 0.001 14.934 0.000 0.062 0.062
.w_X1_2 ~~
.w_Y1_2 (c) 0.013 0.001 14.934 0.000 0.042 0.042
.w_X1_3 ~~
.w_Y1_3 (c) 0.013 0.001 14.934 0.000 0.046 0.046
.w_X1_4 ~~
.w_Y1_4 (c) 0.013 0.001 14.934 0.000 0.028 0.028
.w_X1_5 ~~
.w_Y1_5 (c) 0.013 0.001 14.934 0.000 0.405 0.405
RIx ~~
RIy 0.010 0.002 5.240 0.000 0.479 0.479
w_X1_1 0.003 0.001 2.197 0.028 0.120 0.120
w_Y1_1 0.002 0.002 1.023 0.306 0.023 0.023
RIy ~~
w_X1_1 0.004 0.002 2.146 0.032 0.069 0.069
w_Y1_1 -0.013 0.003 -4.653 0.000 -0.076 -0.076
Intercepts:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
RIx 0.134 0.012 11.182 0.000 1.406 1.406
RIy 0.289 0.020 14.101 0.000 1.361 1.361
w_X1_1 0.107 0.013 8.386 0.000 0.412 0.412
.w_X1_2 0.025 0.015 1.647 0.100 0.105 0.105
.w_X1_3 0.047 0.009 5.389 0.000 0.183 0.183
.w_X1_4 -0.024 0.011 -2.247 0.025 -0.086 -0.086
.w_X1_5 -0.022 0.009 -2.540 0.011 -0.120 -0.120
w_Y1_1 0.047 0.021 2.272 0.023 0.061 0.061
.w_Y1_2 0.046 0.024 1.901 0.057 0.036 0.036
.w_Y1_3 0.025 0.024 1.057 0.291 0.023 0.023
.w_Y1_4 0.089 0.022 4.009 0.000 0.056 0.056
.w_Y1_5 0.101 0.028 3.648 0.000 0.565 0.565
Variances:
Estimate Std.Err z-value P(>|z|) Std.lv Std.all
RIx 0.009 0.002 5.198 0.000 1.000 1.000
RIy 0.045 0.003 14.754 0.000 1.000 1.000
w_X1_1 0.067 0.071 0.944 0.345 1.000 1.000
w_Y1_1 0.609 11.152 0.055 0.956 1.000 1.000
.w_X1_2 0.056 0.053 1.044 0.296 0.981 0.981
.w_Y1_2 1.645 30.368 0.054 0.957 0.999 0.999
.w_X1_3 0.064 0.058 1.097 0.273 0.985 0.985
.w_Y1_3 1.197 22.068 0.054 0.957 0.999 0.999
.w_X1_4 0.079 0.080 0.985 0.325 0.986 0.986
.w_Y1_4 2.522 46.731 0.054 0.957 1.000 1.000
.w_X1_5 0.032 0.001 38.271 0.000 0.960 0.960
.w_Y1_5 0.031 0.002 19.924 0.000 0.958 0.958
.X1_1 -0.022 0.071 -0.311 0.756 -0.022 -0.365
.X1_2 -0.025 0.053 -0.471 0.638 -0.025 -0.594
.X1_3 -0.034 0.059 -0.579 0.562 -0.034 -0.844
.X1_4 -0.046 0.080 -0.577 0.564 -0.046 -1.072
.X1_5 0.003 0.001 3.700 0.000 0.003 0.068
.Y1_1 -0.534 11.152 -0.048 0.962 -0.534 -5.650
.Y1_2 -1.579 30.368 -0.052 0.959 -1.579 -13.943
.Y1_3 -1.135 22.068 -0.051 0.959 -1.135 -10.519
.Y1_4 -2.461 46.731 -0.053 0.958 -2.461 -23.189
.Y1_5 0.036 0.002 23.290 0.000 0.036 0.317
