Below are summaries of each of our models looking at the cross-lagged relationships between two and three variables. I got the code from this source. This code was put together by the authors of a great paper about CLPM vs RI-CLPM (Hamaker et al., 2015). This tutorial goes through how to turn this RI-CLPM code into CLPM code, which is what I’ve done below.
To make life easier, these models recycle a code chunk which defines variables in each model in terms of X, Y, and M. This is just so that we don’t need to change every single variable name in the code chunk in order to change the variables we’re looking at. The first two sections of each code chunk define which variables are labeled with which letter. Throughout, I define X as the theoretical predictor, Y as the theoretical outcome, and M as the theoretical mediator in situations where we’re looking at three variables. Also, the Ws in front of variable lables are just to denote “within,” since the model decomposes within-person and between-person effects (this is how the code from the source I linked did it).
Further, across all models, I’ve imposed equality constraints on all cross-lagged paths for the sake of parsimony.
Finally, all output now includes 95% CIs, allowing for the comparison of magnitudes of different coefficients.
In this section, I set up 2-variable models with traditional single-order autoregressions (e.g. X at time t+1 is regressed on X at time t) and 2-variable models with additional second-order autoregressions (e.g. X at time t+1 is regressed on X at time t and at time t-1). As you’ll see, these second-order AR models always show better fit, confirmed by likelihood ratio tests.
LadderPEmoCLPM <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*posEmo.1
wy2 =~ 1*posEmo.2
wy3 =~ 1*posEmo.3
wy4 =~ 1*posEmo.4
wy5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2
wy4 ~ a*wx3 + wy3
wy5 ~ a*wx4 + wy4
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2
wx4 ~ wx3 + b*wy3
wx5 ~ wx4 + b*wy4
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderPEmoCLPM.fit <- lavaan(LadderPEmoCLPM, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderPEmoCLPM.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 316.572
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1454.814
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.797
## Tucker-Lewis Index (TLI) 0.695
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3844.445
## Loglikelihood unrestricted model (H1) -3686.159
##
## Akaike (AIC) 7758.890
## Bayesian (BIC) 7905.119
## Sample-size adjusted Bayesian (BIC) 7794.032
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.141
## 90 Percent confidence interval - lower 0.127
## 90 Percent confidence interval - upper 0.155
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.132
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## posEmo.1 1.000 1.000 1.000
## wy2 =~
## posEmo.2 1.000 1.000 1.000
## wy3 =~
## posEmo.3 1.000 1.000 1.000
## wy4 =~
## posEmo.4 1.000 1.000 1.000
## wy5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.995 1.000
##
## 1.005 1.000
##
## 1.006 1.000
##
## 1.003 1.000
##
## 0.994 1.000
##
## 1.003 1.000
##
## 0.994 1.000
##
## 0.995 1.000
##
## 0.984 1.000
##
## 0.996 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.104 0.022 -4.647 0.000 -0.149 -0.060
## wy1 0.589 0.042 14.138 0.000 0.507 0.670
## wy3 ~
## wx2 (a) -0.104 0.022 -4.647 0.000 -0.149 -0.060
## wy2 0.651 0.043 14.995 0.000 0.566 0.736
## wy4 ~
## wx3 (a) -0.104 0.022 -4.647 0.000 -0.149 -0.060
## wy3 0.699 0.042 16.649 0.000 0.617 0.782
## wy5 ~
## wx4 (a) -0.104 0.022 -4.647 0.000 -0.149 -0.060
## wy4 0.698 0.046 15.329 0.000 0.609 0.787
## wx2 ~
## wx1 0.533 0.047 11.326 0.000 0.440 0.625
## wy1 (b) -0.089 0.025 -3.501 0.000 -0.138 -0.039
## wx3 ~
## wx2 0.533 0.052 10.174 0.000 0.431 0.636
## wy2 (b) -0.089 0.025 -3.501 0.000 -0.138 -0.039
## wx4 ~
## wx3 0.510 0.054 9.476 0.000 0.405 0.616
## wy3 (b) -0.089 0.025 -3.501 0.000 -0.138 -0.039
## wx5 ~
## wx4 0.572 0.051 11.209 0.000 0.472 0.671
## wy4 (b) -0.089 0.025 -3.501 0.000 -0.138 -0.039
## Std.lv Std.all
##
## -0.105 -0.105
## 0.594 0.594
##
## -0.106 -0.106
## 0.650 0.650
##
## -0.107 -0.107
## 0.707 0.707
##
## -0.105 -0.105
## 0.689 0.689
##
## 0.527 0.527
## -0.088 -0.088
##
## 0.533 0.533
## -0.088 -0.088
##
## 0.512 0.512
## -0.088 -0.088
##
## 0.577 0.577
## -0.088 -0.088
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.245 0.049 -5.035 0.000 -0.341 -0.150
## .wx2 ~~
## .wy2 -0.053 0.036 -1.485 0.138 -0.124 0.017
## .wx3 ~~
## .wy3 0.024 0.036 0.647 0.517 -0.048 0.095
## .wx4 ~~
## .wy4 -0.003 0.036 -0.078 0.938 -0.073 0.068
## .wx5 ~~
## .wy5 -0.007 0.036 -0.187 0.852 -0.076 0.063
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.246 -0.246
##
## -0.083 -0.083
##
## 0.039 0.039
##
## -0.005 -0.005
##
## -0.012 -0.012
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.001 0.047 0.028 0.978 -0.090 0.093
## .LadderDif.2 -0.010 0.052 -0.194 0.846 -0.111 0.091
## .LadderDif.3 -0.014 0.058 -0.238 0.812 -0.127 0.099
## .LadderDif.4 -0.000 0.062 -0.005 0.996 -0.121 0.120
## .LadderDif.5 -0.006 0.063 -0.092 0.926 -0.129 0.117
## .posEmo.1 -0.003 0.047 -0.068 0.946 -0.095 0.089
## .posEmo.2 -0.009 0.050 -0.185 0.853 -0.108 0.089
## .posEmo.3 -0.001 0.055 -0.014 0.989 -0.110 0.108
## .posEmo.4 -0.005 0.058 -0.078 0.938 -0.119 0.110
## .posEmo.5 0.011 0.061 0.175 0.861 -0.110 0.131
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## 0.001 0.001
## -0.010 -0.010
## -0.014 -0.014
## -0.000 -0.000
## -0.006 -0.006
## -0.003 -0.003
## -0.009 -0.009
## -0.001 -0.001
## -0.005 -0.005
## 0.011 0.011
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.990 0.066 14.960 0.000 0.860 1.119
## wy1 1.006 0.068 14.831 0.000 0.873 1.139
## .wx2 0.699 0.054 12.876 0.000 0.592 0.805
## .wy2 0.598 0.048 12.557 0.000 0.505 0.691
## .wx3 0.694 0.059 11.772 0.000 0.579 0.810
## .wy3 0.528 0.045 11.778 0.000 0.440 0.616
## .wx4 0.719 0.064 11.191 0.000 0.593 0.845
## .wy4 0.448 0.040 11.196 0.000 0.370 0.527
## .wx5 0.634 0.058 10.996 0.000 0.521 0.747
## .wy5 0.483 0.044 10.998 0.000 0.397 0.569
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.691 0.691
## 0.605 0.605
## 0.686 0.686
## 0.533 0.533
## 0.715 0.715
## 0.463 0.463
## 0.641 0.641
## 0.487 0.487
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000# Same model as above code, but fit with d_black dataset this time
LadderPEmoCLPM_b.fit <- lavaan(LadderPEmoCLPM, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderPEmoCLPM_b.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 228.953
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1026.626
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.797
## Tucker-Lewis Index (TLI) 0.696
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3513.843
## Loglikelihood unrestricted model (H1) -3399.367
##
## Akaike (AIC) 7097.686
## Bayesian (BIC) 7243.914
## Sample-size adjusted Bayesian (BIC) 7132.827
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.117
## 90 Percent confidence interval - lower 0.103
## 90 Percent confidence interval - upper 0.132
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.118
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## posEmo.1 1.000 1.000 1.000
## wy2 =~
## posEmo.2 1.000 1.000 1.000
## wy3 =~
## posEmo.3 1.000 1.000 1.000
## wy4 =~
## posEmo.4 1.000 1.000 1.000
## wy5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.998 1.000
##
## 1.010 1.000
##
## 1.008 1.000
##
## 0.996 1.000
##
## 0.998 1.000
##
## 0.995 1.000
##
## 0.991 1.000
##
## 0.992 1.000
##
## 1.002 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.034 0.024 -1.388 0.165 -0.081 0.014
## wy1 0.533 0.051 10.406 0.000 0.433 0.634
## wy3 ~
## wx2 (a) -0.034 0.024 -1.388 0.165 -0.081 0.014
## wy2 0.717 0.045 15.964 0.000 0.629 0.806
## wy4 ~
## wx3 (a) -0.034 0.024 -1.388 0.165 -0.081 0.014
## wy3 0.754 0.044 16.972 0.000 0.667 0.841
## wy5 ~
## wx4 (a) -0.034 0.024 -1.388 0.165 -0.081 0.014
## wy4 0.741 0.048 15.400 0.000 0.647 0.836
## wx2 ~
## wx1 0.255 0.061 4.151 0.000 0.134 0.375
## wy1 (b) -0.026 0.030 -0.858 0.391 -0.084 0.033
## wx3 ~
## wx2 0.542 0.059 9.207 0.000 0.427 0.657
## wy2 (b) -0.026 0.030 -0.858 0.391 -0.084 0.033
## wx4 ~
## wx3 0.466 0.063 7.383 0.000 0.343 0.590
## wy3 (b) -0.026 0.030 -0.858 0.391 -0.084 0.033
## wx5 ~
## wx4 0.483 0.060 8.032 0.000 0.365 0.601
## wy4 (b) -0.026 0.030 -0.858 0.391 -0.084 0.033
## Std.lv Std.all
##
## -0.034 -0.034
## 0.535 0.535
##
## -0.034 -0.034
## 0.721 0.721
##
## -0.034 -0.034
## 0.753 0.753
##
## -0.034 -0.034
## 0.734 0.734
##
## 0.255 0.255
## -0.025 -0.025
##
## 0.536 0.536
## -0.025 -0.025
##
## 0.467 0.467
## -0.025 -0.025
##
## 0.489 0.489
## -0.025 -0.025
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.007 0.047 -0.157 0.876 -0.099 0.084
## .wx2 ~~
## .wy2 -0.012 0.049 -0.241 0.809 -0.108 0.084
## .wx3 ~~
## .wy3 -0.023 0.039 -0.598 0.550 -0.099 0.053
## .wx4 ~~
## .wy4 0.010 0.040 0.263 0.792 -0.067 0.088
## .wx5 ~~
## .wy5 0.018 0.041 0.442 0.659 -0.063 0.099
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.007 -0.007
##
## -0.015 -0.015
##
## -0.040 -0.040
##
## 0.018 0.018
##
## 0.031 0.031
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.007 0.047 -0.154 0.877 -0.099 0.085
## .LadderDif.2 0.005 0.059 0.078 0.938 -0.110 0.119
## .LadderDif.3 0.002 0.065 0.036 0.971 -0.124 0.129
## .LadderDif.4 0.019 0.068 0.277 0.782 -0.115 0.152
## .LadderDif.5 0.008 0.069 0.110 0.912 -0.128 0.143
## .posEmo.1 -0.001 0.047 -0.017 0.986 -0.092 0.090
## .posEmo.2 -0.007 0.056 -0.125 0.901 -0.116 0.102
## .posEmo.3 -0.003 0.060 -0.056 0.956 -0.122 0.115
## .posEmo.4 -0.007 0.064 -0.115 0.909 -0.132 0.118
## .posEmo.5 -0.010 0.067 -0.142 0.887 -0.141 0.122
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.007 -0.007
## 0.005 0.005
## 0.002 0.002
## 0.019 0.019
## 0.008 0.008
## -0.001 -0.001
## -0.007 -0.007
## -0.003 -0.003
## -0.007 -0.007
## -0.010 -0.010
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.998 0.066 15.021 0.000 0.867 1.128
## wy1 0.995 0.066 15.095 0.000 0.866 1.125
## .wx2 0.931 0.079 11.803 0.000 0.777 1.086
## .wy2 0.705 0.061 11.588 0.000 0.586 0.825
## .wx3 0.725 0.068 10.698 0.000 0.592 0.858
## .wy3 0.469 0.044 10.697 0.000 0.383 0.555
## .wx4 0.791 0.077 10.326 0.000 0.641 0.942
## .wy4 0.422 0.041 10.329 0.000 0.342 0.502
## .wx5 0.753 0.075 10.091 0.000 0.606 0.899
## .wy5 0.459 0.045 10.093 0.000 0.370 0.548
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.934 0.934
## 0.713 0.713
## 0.711 0.711
## 0.478 0.478
## 0.779 0.779
## 0.429 0.429
## 0.759 0.759
## 0.457 0.457
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000LadderPEmoCLPM_2AR <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*posEmo.1
wy2 =~ 1*posEmo.2
wy3 =~ 1*posEmo.3
wy4 =~ 1*posEmo.4
wy5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2 + wy1
wy4 ~ a*wx3 + wy3 + wy2
wy5 ~ a*wx4 + wy4 + wy3
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2 + wx1
wx4 ~ wx3 + b*wy3 + wx2
wx5 ~ wx4 + b*wy4 + wx3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderPEmoCLPM_2AR.fit <- lavaan(LadderPEmoCLPM_2AR, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderPEmoCLPM_2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 77.846
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1454.814
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.962
## Tucker-Lewis Index (TLI) 0.928
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3725.082
## Loglikelihood unrestricted model (H1) -3686.159
##
## Akaike (AIC) 7532.164
## Bayesian (BIC) 7703.460
## Sample-size adjusted Bayesian (BIC) 7573.330
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.068
## 90 Percent confidence interval - lower 0.052
## 90 Percent confidence interval - upper 0.085
## P-value RMSEA <= 0.05 0.036
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## posEmo.1 1.000 1.000 1.000
## wy2 =~
## posEmo.2 1.000 1.000 1.000
## wy3 =~
## posEmo.3 1.000 1.000 1.000
## wy4 =~
## posEmo.4 1.000 1.000 1.000
## wy5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.997 1.000
##
## 1.000 1.000
##
## 0.997 1.000
##
## 1.008 1.000
##
## 0.994 1.000
##
## 1.001 1.000
##
## 0.991 1.000
##
## 0.988 1.000
##
## 0.982 1.000
##
## 0.995 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.093 0.021 -4.303 0.000 -0.135 -0.050
## wy1 0.585 0.042 13.910 0.000 0.502 0.667
## wy3 ~
## wx2 (a) -0.093 0.021 -4.303 0.000 -0.135 -0.050
## wy2 0.517 0.056 9.188 0.000 0.407 0.627
## wy1 0.211 0.059 3.577 0.000 0.095 0.326
## wy4 ~
## wx3 (a) -0.093 0.021 -4.303 0.000 -0.135 -0.050
## wy3 0.523 0.055 9.532 0.000 0.416 0.631
## wy2 0.262 0.055 4.715 0.000 0.153 0.370
## wy5 ~
## wx4 (a) -0.093 0.021 -4.303 0.000 -0.135 -0.050
## wy4 0.350 0.057 6.103 0.000 0.237 0.462
## wy3 0.481 0.056 8.511 0.000 0.370 0.591
## wx2 ~
## wx1 0.540 0.047 11.608 0.000 0.449 0.632
## wy1 (b) -0.048 0.024 -2.005 0.045 -0.095 -0.001
## wx3 ~
## wx2 0.294 0.058 5.038 0.000 0.180 0.408
## wy2 (b) -0.048 0.024 -2.005 0.045 -0.095 -0.001
## wx1 0.425 0.057 7.436 0.000 0.313 0.537
## wx4 ~
## wx3 0.316 0.060 5.278 0.000 0.199 0.434
## wy3 (b) -0.048 0.024 -2.005 0.045 -0.095 -0.001
## wx2 0.383 0.063 6.121 0.000 0.260 0.505
## wx5 ~
## wx4 0.341 0.053 6.402 0.000 0.237 0.446
## wy4 (b) -0.048 0.024 -2.005 0.045 -0.095 -0.001
## wx3 0.442 0.054 8.260 0.000 0.337 0.547
## Std.lv Std.all
##
## -0.093 -0.093
## 0.591 0.591
##
## -0.094 -0.094
## 0.518 0.518
## 0.214 0.214
##
## -0.094 -0.094
## 0.527 0.527
## 0.264 0.264
##
## -0.094 -0.094
## 0.345 0.345
## 0.477 0.477
##
## 0.539 0.539
## -0.048 -0.048
##
## 0.295 0.295
## -0.048 -0.048
## 0.425 0.425
##
## 0.313 0.313
## -0.047 -0.047
## 0.380 0.380
##
## 0.346 0.346
## -0.047 -0.047
## 0.444 0.444
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.249 0.049 -5.101 0.000 -0.344 -0.153
## .wx2 ~~
## .wy2 -0.053 0.036 -1.483 0.138 -0.124 0.017
## .wx3 ~~
## .wy3 0.002 0.033 0.047 0.962 -0.063 0.066
## .wx4 ~~
## .wy4 0.012 0.032 0.367 0.713 -0.051 0.075
## .wx5 ~~
## .wy5 0.001 0.028 0.047 0.963 -0.053 0.056
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.249 -0.249
##
## -0.082 -0.082
##
## 0.003 0.003
##
## 0.023 0.023
##
## 0.003 0.003
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.003 0.047 0.063 0.949 -0.089 0.095
## .LadderDif.2 -0.011 0.051 -0.217 0.828 -0.112 0.090
## .LadderDif.3 -0.034 0.055 -0.626 0.531 -0.142 0.073
## .LadderDif.4 -0.013 0.059 -0.221 0.825 -0.129 0.103
## .LadderDif.5 -0.020 0.060 -0.343 0.731 -0.137 0.096
## .posEmo.1 -0.006 0.047 -0.137 0.891 -0.098 0.086
## .posEmo.2 -0.009 0.050 -0.172 0.863 -0.107 0.090
## .posEmo.3 0.005 0.054 0.086 0.931 -0.102 0.111
## .posEmo.4 0.000 0.056 0.006 0.995 -0.110 0.110
## .posEmo.5 0.016 0.058 0.271 0.786 -0.098 0.130
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## 0.003 0.003
## -0.011 -0.011
## -0.034 -0.034
## -0.013 -0.013
## -0.020 -0.021
## -0.006 -0.006
## -0.009 -0.009
## 0.005 0.005
## 0.000 0.000
## 0.016 0.016
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.995 0.067 14.953 0.000 0.864 1.125
## wy1 1.003 0.067 14.874 0.000 0.871 1.135
## .wx2 0.694 0.054 12.920 0.000 0.589 0.800
## .wy2 0.603 0.048 12.528 0.000 0.509 0.698
## .wx3 0.573 0.050 11.521 0.000 0.476 0.671
## .wy3 0.500 0.043 11.662 0.000 0.416 0.585
## .wx4 0.623 0.056 11.189 0.000 0.514 0.732
## .wy4 0.412 0.037 11.201 0.000 0.340 0.484
## .wx5 0.497 0.045 11.000 0.000 0.408 0.585
## .wy5 0.372 0.034 10.991 0.000 0.306 0.438
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.694 0.694
## 0.615 0.615
## 0.576 0.576
## 0.513 0.513
## 0.613 0.613
## 0.428 0.428
## 0.502 0.502
## 0.376 0.376
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderPEmoCLPM_2AR.fit, LadderPEmoCLPM.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## LadderPEmoCLPM_2AR.fit 24 7532.2 7703.5 77.846
## LadderPEmoCLPM.fit 30 7758.9 7905.1 316.572 238.73 6 < 2.2e-16
##
## LadderPEmoCLPM_2AR.fit
## LadderPEmoCLPM.fit ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Same model as above code, but fit with d_black dataset this time
LadderPEmoCLPM_b2AR.fit <- lavaan(LadderPEmoCLPM_2AR, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderPEmoCLPM_b2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 75.407
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1026.626
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.948
## Tucker-Lewis Index (TLI) 0.902
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3437.070
## Loglikelihood unrestricted model (H1) -3399.367
##
## Akaike (AIC) 6956.140
## Bayesian (BIC) 7127.436
## Sample-size adjusted Bayesian (BIC) 6997.306
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.067
## 90 Percent confidence interval - lower 0.050
## 90 Percent confidence interval - upper 0.084
## P-value RMSEA <= 0.05 0.050
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## posEmo.1 1.000 1.000 1.000
## wy2 =~
## posEmo.2 1.000 1.000 1.000
## wy3 =~
## posEmo.3 1.000 1.000 1.000
## wy4 =~
## posEmo.4 1.000 1.000 1.000
## wy5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 0.998 1.000
##
## 1.013 1.000
##
## 1.016 1.000
##
## 1.015 1.000
##
## 0.996 1.000
##
## 0.995 1.000
##
## 0.983 1.000
##
## 0.979 1.000
##
## 0.988 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.031 0.023 -1.353 0.176 -0.076 0.014
## wy1 0.530 0.052 10.265 0.000 0.429 0.631
## wy3 ~
## wx2 (a) -0.031 0.023 -1.353 0.176 -0.076 0.014
## wy2 0.576 0.053 10.924 0.000 0.473 0.679
## wy1 0.247 0.054 4.572 0.000 0.141 0.352
## wy4 ~
## wx3 (a) -0.031 0.023 -1.353 0.176 -0.076 0.014
## wy3 0.517 0.064 8.126 0.000 0.392 0.642
## wy2 0.310 0.063 4.946 0.000 0.187 0.433
## wy5 ~
## wx4 (a) -0.031 0.023 -1.353 0.176 -0.076 0.014
## wy4 0.375 0.065 5.769 0.000 0.247 0.502
## wy3 0.477 0.064 7.472 0.000 0.352 0.602
## wx2 ~
## wx1 0.264 0.061 4.344 0.000 0.145 0.383
## wy1 (b) -0.011 0.029 -0.384 0.701 -0.067 0.045
## wx3 ~
## wx2 0.502 0.060 8.364 0.000 0.384 0.619
## wy2 (b) -0.011 0.029 -0.384 0.701 -0.067 0.045
## wx1 0.174 0.062 2.799 0.005 0.052 0.296
## wx4 ~
## wx3 0.334 0.070 4.751 0.000 0.196 0.472
## wy3 (b) -0.011 0.029 -0.384 0.701 -0.067 0.045
## wx2 0.274 0.072 3.807 0.000 0.133 0.415
## wx5 ~
## wx4 0.287 0.062 4.657 0.000 0.166 0.408
## wy4 (b) -0.011 0.029 -0.384 0.701 -0.067 0.045
## wx3 0.442 0.065 6.810 0.000 0.314 0.569
## Std.lv Std.all
##
## -0.031 -0.031
## 0.531 0.531
##
## -0.031 -0.031
## 0.583 0.583
## 0.250 0.250
##
## -0.032 -0.032
## 0.519 0.519
## 0.315 0.315
##
## -0.032 -0.032
## 0.371 0.371
## 0.474 0.474
##
## 0.264 0.264
## -0.011 -0.011
##
## 0.495 0.495
## -0.011 -0.011
## 0.172 0.172
##
## 0.333 0.333
## -0.011 -0.011
## 0.269 0.269
##
## 0.287 0.287
## -0.011 -0.011
## 0.441 0.441
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.007 0.047 -0.143 0.886 -0.098 0.085
## .wx2 ~~
## .wy2 -0.012 0.049 -0.252 0.801 -0.109 0.084
## .wx3 ~~
## .wy3 -0.010 0.036 -0.277 0.782 -0.081 0.061
## .wx4 ~~
## .wy4 0.016 0.036 0.443 0.658 -0.055 0.088
## .wx5 ~~
## .wy5 -0.044 0.034 -1.294 0.196 -0.110 0.023
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.007 -0.007
##
## -0.015 -0.015
##
## -0.018 -0.018
##
## 0.030 0.030
##
## -0.092 -0.092
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.008 0.047 -0.176 0.860 -0.100 0.084
## .LadderDif.2 0.005 0.059 0.081 0.935 -0.110 0.119
## .LadderDif.3 -0.001 0.064 -0.020 0.984 -0.126 0.124
## .LadderDif.4 0.019 0.067 0.275 0.783 -0.113 0.151
## .LadderDif.5 0.018 0.068 0.260 0.795 -0.116 0.152
## .posEmo.1 -0.003 0.046 -0.058 0.954 -0.094 0.088
## .posEmo.2 -0.006 0.056 -0.111 0.911 -0.115 0.103
## .posEmo.3 -0.009 0.058 -0.160 0.873 -0.123 0.104
## .posEmo.4 -0.014 0.060 -0.228 0.820 -0.132 0.105
## .posEmo.5 -0.020 0.063 -0.316 0.752 -0.142 0.103
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.008 -0.008
## 0.005 0.005
## -0.001 -0.001
## 0.019 0.018
## 0.018 0.018
## -0.003 -0.003
## -0.006 -0.006
## -0.009 -0.009
## -0.014 -0.014
## -0.020 -0.020
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 1.000 0.067 14.992 0.000 0.869 1.131
## wy1 0.993 0.066 15.133 0.000 0.864 1.122
## .wx2 0.927 0.079 11.801 0.000 0.773 1.081
## .wy2 0.710 0.061 11.589 0.000 0.590 0.830
## .wx3 0.698 0.066 10.647 0.000 0.569 0.826
## .wy3 0.425 0.040 10.544 0.000 0.346 0.504
## .wx4 0.742 0.072 10.322 0.000 0.601 0.882
## .wy4 0.379 0.037 10.320 0.000 0.307 0.450
## .wx5 0.619 0.061 10.096 0.000 0.499 0.739
## .wy5 0.363 0.036 10.097 0.000 0.293 0.433
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.930 0.930
## 0.717 0.717
## 0.680 0.680
## 0.440 0.440
## 0.719 0.719
## 0.395 0.395
## 0.601 0.601
## 0.372 0.372
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderPEmoCLPM_b2AR.fit, LadderPEmoCLPM_b.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## LadderPEmoCLPM_b2AR.fit 24 6956.1 7127.4 75.407
## LadderPEmoCLPM_b.fit 30 7097.7 7243.9 228.953 153.55 6 < 2.2e-16
##
## LadderPEmoCLPM_b2AR.fit
## LadderPEmoCLPM_b.fit ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LadderHealthCLPM <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gHealth.1 + 1*gHealth.2 + 1*gHealth.3 + 1*gHealth.4 + 1*gHealth.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gHealth.1
wy2 =~ 1*gHealth.2
wy3 =~ 1*gHealth.3
wy4 =~ 1*gHealth.4
wy5 =~ 1*gHealth.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2
wy4 ~ a*wx3 + wy3
wy5 ~ a*wx4 + wy4
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2
wx4 ~ wx3 + b*wy3
wx5 ~ wx4 + b*wy4
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderHealthCLPM.fit <- lavaan(LadderHealthCLPM, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderHealthCLPM.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 377.146
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1985.448
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.821
## Tucker-Lewis Index (TLI) 0.732
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3609.415
## Loglikelihood unrestricted model (H1) -3420.842
##
## Akaike (AIC) 7288.830
## Bayesian (BIC) 7435.058
## Sample-size adjusted Bayesian (BIC) 7323.971
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.155
## 90 Percent confidence interval - lower 0.141
## 90 Percent confidence interval - upper 0.169
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.126
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.995 1.000
##
## 1.001 1.000
##
## 1.007 1.000
##
## 0.990 1.000
##
## 0.996 1.000
##
## 0.996 1.000
##
## 0.980 1.000
##
## 0.988 1.000
##
## 0.984 1.000
##
## 0.979 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.041 0.019 -2.137 0.033 -0.079 -0.003
## wy1 0.772 0.033 23.690 0.000 0.708 0.836
## wy3 ~
## wx2 (a) -0.041 0.019 -2.137 0.033 -0.079 -0.003
## wy2 0.782 0.037 21.291 0.000 0.710 0.854
## wy4 ~
## wx3 (a) -0.041 0.019 -2.137 0.033 -0.079 -0.003
## wy3 0.786 0.037 21.188 0.000 0.713 0.859
## wy5 ~
## wx4 (a) -0.041 0.019 -2.137 0.033 -0.079 -0.003
## wy4 0.786 0.038 20.820 0.000 0.712 0.860
## wx2 ~
## wx1 0.495 0.048 10.369 0.000 0.402 0.589
## wy1 (b) -0.139 0.026 -5.353 0.000 -0.189 -0.088
## wx3 ~
## wx2 0.512 0.052 9.774 0.000 0.410 0.615
## wy2 (b) -0.139 0.026 -5.353 0.000 -0.189 -0.088
## wx4 ~
## wx3 0.480 0.053 9.006 0.000 0.376 0.585
## wy3 (b) -0.139 0.026 -5.353 0.000 -0.189 -0.088
## wx5 ~
## wx4 0.563 0.052 10.921 0.000 0.462 0.664
## wy4 (b) -0.139 0.026 -5.353 0.000 -0.189 -0.088
## Std.lv Std.all
##
## -0.042 -0.042
## 0.784 0.784
##
## -0.042 -0.042
## 0.777 0.777
##
## -0.042 -0.042
## 0.788 0.788
##
## -0.042 -0.042
## 0.790 0.790
##
## 0.492 0.492
## -0.138 -0.138
##
## 0.509 0.509
## -0.135 -0.135
##
## 0.488 0.488
## -0.138 -0.138
##
## 0.559 0.559
## -0.137 -0.137
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.338 0.049 -6.877 0.000 -0.435 -0.242
## .wx2 ~~
## .wy2 -0.045 0.028 -1.626 0.104 -0.099 0.009
## .wx3 ~~
## .wy3 -0.037 0.030 -1.210 0.226 -0.096 0.023
## .wx4 ~~
## .wy4 0.029 0.031 0.933 0.351 -0.032 0.090
## .wx5 ~~
## .wy5 0.029 0.030 0.944 0.345 -0.031 0.089
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.342 -0.342
##
## -0.091 -0.091
##
## -0.073 -0.073
##
## 0.059 0.059
##
## 0.062 0.062
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.000 0.047 -0.008 0.994 -0.092 0.091
## .LadderDif.2 -0.009 0.051 -0.179 0.858 -0.110 0.092
## .LadderDif.3 -0.013 0.058 -0.231 0.817 -0.126 0.099
## .LadderDif.4 -0.002 0.061 -0.025 0.980 -0.120 0.117
## .LadderDif.5 -0.006 0.063 -0.102 0.919 -0.129 0.116
## .gHealth.1 0.005 0.046 0.100 0.920 -0.086 0.095
## .gHealth.2 -0.005 0.048 -0.112 0.911 -0.099 0.088
## .gHealth.3 -0.001 0.053 -0.017 0.987 -0.104 0.102
## .gHealth.4 0.005 0.056 0.092 0.926 -0.105 0.115
## .gHealth.5 0.009 0.058 0.150 0.880 -0.106 0.123
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.000 -0.000
## -0.009 -0.009
## -0.013 -0.013
## -0.002 -0.002
## -0.006 -0.006
## 0.005 0.005
## -0.005 -0.005
## -0.001 -0.001
## 0.005 0.005
## 0.009 0.009
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.989 0.066 14.964 0.000 0.860 1.119
## wy1 0.991 0.066 15.128 0.000 0.863 1.120
## .wx2 0.693 0.054 12.901 0.000 0.588 0.799
## .wy2 0.348 0.028 12.508 0.000 0.293 0.402
## .wx3 0.689 0.058 11.785 0.000 0.575 0.804
## .wy3 0.366 0.031 11.790 0.000 0.305 0.427
## .wx4 0.690 0.062 11.198 0.000 0.569 0.811
## .wy4 0.346 0.031 11.171 0.000 0.286 0.407
## .wx5 0.632 0.057 10.999 0.000 0.519 0.744
## .wy5 0.345 0.032 10.914 0.000 0.283 0.407
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.692 0.692
## 0.362 0.362
## 0.680 0.680
## 0.375 0.375
## 0.704 0.704
## 0.358 0.358
## 0.636 0.636
## 0.360 0.360
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000# Same model as above code, but fit with d_black dataset this time
LadderHealthCLPM_b.fit <- lavaan(LadderHealthCLPM, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderHealthCLPM_b.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 259.528
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1202.132
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.802
## Tucker-Lewis Index (TLI) 0.702
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3441.377
## Loglikelihood unrestricted model (H1) -3311.613
##
## Akaike (AIC) 6952.754
## Bayesian (BIC) 7098.982
## Sample-size adjusted Bayesian (BIC) 6987.895
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.126
## 90 Percent confidence interval - lower 0.112
## 90 Percent confidence interval - upper 0.140
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.124
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.989 1.000
##
## 1.010 1.000
##
## 1.006 1.000
##
## 0.997 1.000
##
## 0.994 1.000
##
## 1.004 1.000
##
## 0.991 1.000
##
## 0.994 1.000
##
## 1.003 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.047 0.023 -2.038 0.042 -0.091 -0.002
## wy1 0.734 0.043 16.957 0.000 0.649 0.819
## wy3 ~
## wx2 (a) -0.047 0.023 -2.038 0.042 -0.091 -0.002
## wy2 0.735 0.043 16.928 0.000 0.650 0.820
## wy4 ~
## wx3 (a) -0.047 0.023 -2.038 0.042 -0.091 -0.002
## wy3 0.741 0.046 16.163 0.000 0.651 0.831
## wy5 ~
## wx4 (a) -0.047 0.023 -2.038 0.042 -0.091 -0.002
## wy4 0.763 0.046 16.477 0.000 0.673 0.854
## wx2 ~
## wx1 0.255 0.060 4.237 0.000 0.137 0.374
## wy1 (b) -0.086 0.030 -2.824 0.005 -0.145 -0.026
## wx3 ~
## wx2 0.524 0.060 8.803 0.000 0.407 0.640
## wy2 (b) -0.086 0.030 -2.824 0.005 -0.145 -0.026
## wx4 ~
## wx3 0.463 0.063 7.329 0.000 0.340 0.587
## wy3 (b) -0.086 0.030 -2.824 0.005 -0.145 -0.026
## wx5 ~
## wx4 0.468 0.060 7.744 0.000 0.350 0.586
## wy4 (b) -0.086 0.030 -2.824 0.005 -0.145 -0.026
## Std.lv Std.all
##
## -0.046 -0.046
## 0.726 0.726
##
## -0.046 -0.046
## 0.745 0.745
##
## -0.047 -0.047
## 0.738 0.738
##
## -0.047 -0.047
## 0.756 0.756
##
## 0.258 0.258
## -0.086 -0.086
##
## 0.513 0.513
## -0.085 -0.085
##
## 0.465 0.465
## -0.084 -0.084
##
## 0.472 0.472
## -0.085 -0.085
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.050 0.046 -1.069 0.285 -0.141 0.041
## .wx2 ~~
## .wy2 -0.011 0.041 -0.260 0.795 -0.090 0.069
## .wx3 ~~
## .wy3 0.054 0.038 1.421 0.155 -0.020 0.127
## .wx4 ~~
## .wy4 -0.020 0.041 -0.493 0.622 -0.100 0.060
## .wx5 ~~
## .wy5 0.035 0.040 0.886 0.376 -0.042 0.113
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.050 -0.050
##
## -0.016 -0.016
##
## 0.095 0.095
##
## -0.034 -0.034
##
## 0.062 0.062
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.007 0.047 -0.156 0.876 -0.099 0.085
## .LadderDif.2 0.015 0.058 0.252 0.801 -0.099 0.128
## .LadderDif.3 0.013 0.065 0.204 0.838 -0.114 0.140
## .LadderDif.4 0.027 0.068 0.403 0.687 -0.106 0.161
## .LadderDif.5 0.017 0.069 0.250 0.803 -0.118 0.153
## .gHealth.1 -0.002 0.046 -0.053 0.958 -0.093 0.088
## .gHealth.2 -0.089 0.053 -1.670 0.095 -0.193 0.015
## .gHealth.3 -0.051 0.059 -0.868 0.386 -0.166 0.064
## .gHealth.4 -0.034 0.063 -0.534 0.593 -0.157 0.090
## .gHealth.5 -0.055 0.066 -0.829 0.407 -0.185 0.075
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.007 -0.007
## 0.015 0.015
## 0.013 0.013
## 0.027 0.027
## 0.017 0.017
## -0.002 -0.002
## -0.089 -0.088
## -0.051 -0.051
## -0.034 -0.034
## -0.055 -0.055
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.998 0.066 15.020 0.000 0.867 1.128
## wy1 0.987 0.065 15.240 0.000 0.860 1.114
## .wx2 0.903 0.077 11.728 0.000 0.752 1.054
## .wy2 0.471 0.041 11.471 0.000 0.391 0.552
## .wx3 0.737 0.069 10.647 0.000 0.601 0.872
## .wy3 0.429 0.040 10.685 0.000 0.350 0.507
## .wx4 0.781 0.076 10.326 0.000 0.632 0.929
## .wy4 0.442 0.043 10.293 0.000 0.358 0.527
## .wx5 0.755 0.075 10.094 0.000 0.608 0.901
## .wy5 0.419 0.042 10.090 0.000 0.338 0.501
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.924 0.924
## 0.467 0.467
## 0.722 0.722
## 0.436 0.436
## 0.771 0.771
## 0.447 0.447
## 0.759 0.759
## 0.417 0.417
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000LadderHealthCLPM_2AR <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gHealth.1 + 1*gHealth.2 + 1*gHealth.3 + 1*gHealth.4 + 1*gHealth.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gHealth.1
wy2 =~ 1*gHealth.2
wy3 =~ 1*gHealth.3
wy4 =~ 1*gHealth.4
wy5 =~ 1*gHealth.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2 + wy1
wy4 ~ a*wx3 + wy3 + wy2
wy5 ~ a*wx4 + wy4 + wy3
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2 + wx1
wx4 ~ wx3 + b*wy3 + wx2
wx5 ~ wx4 + b*wy4 + wx3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderHealthCLPM_2AR.fit <- lavaan(LadderHealthCLPM_2AR, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderHealthCLPM_2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 99.933
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1985.448
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.961
## Tucker-Lewis Index (TLI) 0.927
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3470.809
## Loglikelihood unrestricted model (H1) -3420.842
##
## Akaike (AIC) 7023.617
## Bayesian (BIC) 7194.913
## Sample-size adjusted Bayesian (BIC) 7064.782
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.081
## 90 Percent confidence interval - lower 0.065
## 90 Percent confidence interval - upper 0.098
## P-value RMSEA <= 0.05 0.001
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.997 1.000
##
## 0.995 1.000
##
## 0.998 1.000
##
## 0.997 1.000
##
## 0.993 1.000
##
## 0.994 1.000
##
## 0.979 1.000
##
## 0.979 1.000
##
## 0.974 1.000
##
## 0.969 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.010 0.018 -0.568 0.570 -0.046 0.025
## wy1 0.781 0.032 24.067 0.000 0.718 0.845
## wy3 ~
## wx2 (a) -0.010 0.018 -0.568 0.570 -0.046 0.025
## wy2 0.386 0.055 7.011 0.000 0.278 0.494
## wy1 0.491 0.054 9.061 0.000 0.385 0.597
## wy4 ~
## wx3 (a) -0.010 0.018 -0.568 0.570 -0.046 0.025
## wy3 0.588 0.059 9.960 0.000 0.473 0.704
## wy2 0.258 0.059 4.363 0.000 0.142 0.374
## wy5 ~
## wx4 (a) -0.010 0.018 -0.568 0.570 -0.046 0.025
## wy4 0.428 0.058 7.377 0.000 0.315 0.542
## wy3 0.445 0.058 7.658 0.000 0.331 0.559
## wx2 ~
## wx1 0.509 0.047 10.818 0.000 0.417 0.602
## wy1 (b) -0.098 0.024 -3.994 0.000 -0.146 -0.050
## wx3 ~
## wx2 0.284 0.058 4.879 0.000 0.170 0.398
## wy2 (b) -0.098 0.024 -3.994 0.000 -0.146 -0.050
## wx1 0.413 0.057 7.276 0.000 0.302 0.525
## wx4 ~
## wx3 0.291 0.059 4.901 0.000 0.175 0.408
## wy3 (b) -0.098 0.024 -3.994 0.000 -0.146 -0.050
## wx2 0.379 0.062 6.143 0.000 0.258 0.500
## wx5 ~
## wx4 0.333 0.054 6.222 0.000 0.228 0.438
## wy4 (b) -0.098 0.024 -3.994 0.000 -0.146 -0.050
## wx3 0.437 0.053 8.188 0.000 0.332 0.541
## Std.lv Std.all
##
## -0.011 -0.011
## 0.794 0.794
##
## -0.011 -0.011
## 0.386 0.386
## 0.499 0.499
##
## -0.011 -0.011
## 0.592 0.592
## 0.259 0.259
##
## -0.011 -0.011
## 0.430 0.430
## 0.450 0.450
##
## 0.511 0.511
## -0.098 -0.098
##
## 0.283 0.283
## -0.096 -0.096
## 0.413 0.413
##
## 0.292 0.292
## -0.096 -0.096
## 0.378 0.378
##
## 0.335 0.335
## -0.096 -0.096
## 0.438 0.438
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.344 0.049 -6.988 0.000 -0.441 -0.248
## .wx2 ~~
## .wy2 -0.043 0.027 -1.582 0.114 -0.097 0.010
## .wx3 ~~
## .wy3 -0.026 0.025 -1.059 0.290 -0.074 0.022
## .wx4 ~~
## .wy4 0.042 0.028 1.491 0.136 -0.013 0.097
## .wx5 ~~
## .wy5 0.015 0.024 0.610 0.542 -0.033 0.062
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.347 -0.347
##
## -0.088 -0.088
##
## -0.065 -0.065
##
## 0.096 0.096
##
## 0.040 0.040
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.001 0.047 0.017 0.986 -0.091 0.092
## .LadderDif.2 -0.010 0.051 -0.194 0.846 -0.110 0.090
## .LadderDif.3 -0.034 0.055 -0.617 0.538 -0.141 0.074
## .LadderDif.4 -0.012 0.059 -0.213 0.831 -0.127 0.102
## .LadderDif.5 -0.020 0.059 -0.339 0.735 -0.136 0.096
## .gHealth.1 0.005 0.046 0.107 0.915 -0.085 0.095
## .gHealth.2 -0.006 0.048 -0.128 0.898 -0.100 0.087
## .gHealth.3 -0.003 0.050 -0.069 0.945 -0.101 0.094
## .gHealth.4 0.010 0.053 0.187 0.851 -0.094 0.114
## .gHealth.5 0.009 0.054 0.165 0.869 -0.097 0.114
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## 0.001 0.001
## -0.010 -0.010
## -0.034 -0.034
## -0.012 -0.013
## -0.020 -0.020
## 0.005 0.005
## -0.006 -0.006
## -0.003 -0.004
## 0.010 0.010
## 0.009 0.009
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.995 0.067 14.948 0.000 0.864 1.125
## wy1 0.989 0.065 15.195 0.000 0.861 1.116
## .wx2 0.688 0.053 12.943 0.000 0.584 0.792
## .wy2 0.349 0.028 12.560 0.000 0.294 0.403
## .wx3 0.573 0.050 11.543 0.000 0.476 0.670
## .wy3 0.279 0.024 11.511 0.000 0.231 0.326
## .wx4 0.607 0.054 11.198 0.000 0.501 0.713
## .wy4 0.319 0.028 11.182 0.000 0.263 0.374
## .wx5 0.493 0.045 10.998 0.000 0.405 0.581
## .wy5 0.281 0.026 10.946 0.000 0.231 0.331
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.695 0.695
## 0.364 0.364
## 0.575 0.575
## 0.291 0.291
## 0.610 0.610
## 0.336 0.336
## 0.499 0.499
## 0.299 0.299
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderHealthCLPM_2AR.fit, LadderHealthCLPM.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## LadderHealthCLPM_2AR.fit 24 7023.6 7194.9 99.933
## LadderHealthCLPM.fit 30 7288.8 7435.1 377.146 277.21 6 < 2.2e-16
##
## LadderHealthCLPM_2AR.fit
## LadderHealthCLPM.fit ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Same model as above code, but fit with d_black dataset this time
LadderHealthCLPM_b2AR.fit <- lavaan(LadderHealthCLPM_2AR, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderHealthCLPM_b2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 83.702
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1202.132
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.948
## Tucker-Lewis Index (TLI) 0.903
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3353.464
## Loglikelihood unrestricted model (H1) -3311.613
##
## Akaike (AIC) 6788.929
## Bayesian (BIC) 6960.224
## Sample-size adjusted Bayesian (BIC) 6830.094
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.072
## 90 Percent confidence interval - lower 0.055
## 90 Percent confidence interval - upper 0.089
## P-value RMSEA <= 0.05 0.015
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.052
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 0.990 1.000
##
## 1.009 1.000
##
## 1.007 1.000
##
## 1.008 1.000
##
## 0.995 1.000
##
## 1.002 1.000
##
## 0.984 1.000
##
## 0.993 1.000
##
## 1.006 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.030 0.022 -1.414 0.157 -0.073 0.012
## wy1 0.734 0.043 17.029 0.000 0.650 0.819
## wy3 ~
## wx2 (a) -0.030 0.022 -1.414 0.157 -0.073 0.012
## wy2 0.506 0.062 8.171 0.000 0.385 0.628
## wy1 0.310 0.062 4.989 0.000 0.189 0.432
## wy4 ~
## wx3 (a) -0.030 0.022 -1.414 0.157 -0.073 0.012
## wy3 0.456 0.066 6.891 0.000 0.326 0.586
## wy2 0.372 0.066 5.655 0.000 0.243 0.501
## wy5 ~
## wx4 (a) -0.030 0.022 -1.414 0.157 -0.073 0.012
## wy4 0.396 0.058 6.786 0.000 0.282 0.511
## wy3 0.510 0.059 8.636 0.000 0.394 0.626
## wx2 ~
## wx1 0.264 0.060 4.409 0.000 0.147 0.381
## wy1 (b) -0.067 0.029 -2.268 0.023 -0.124 -0.009
## wx3 ~
## wx2 0.478 0.061 7.859 0.000 0.359 0.598
## wy2 (b) -0.067 0.029 -2.268 0.023 -0.124 -0.009
## wx1 0.180 0.062 2.889 0.004 0.058 0.302
## wx4 ~
## wx3 0.327 0.071 4.631 0.000 0.189 0.466
## wy3 (b) -0.067 0.029 -2.268 0.023 -0.124 -0.009
## wx2 0.265 0.071 3.702 0.000 0.125 0.405
## wx5 ~
## wx4 0.281 0.062 4.545 0.000 0.160 0.402
## wy4 (b) -0.067 0.029 -2.268 0.023 -0.124 -0.009
## wx3 0.426 0.064 6.628 0.000 0.300 0.553
## Std.lv Std.all
##
## -0.030 -0.030
## 0.729 0.729
##
## -0.031 -0.031
## 0.516 0.516
## 0.314 0.314
##
## -0.031 -0.031
## 0.452 0.452
## 0.376 0.376
##
## -0.030 -0.030
## 0.391 0.391
## 0.499 0.499
##
## 0.267 0.267
## -0.067 -0.067
##
## 0.469 0.469
## -0.066 -0.066
## 0.178 0.178
##
## 0.328 0.328
## -0.065 -0.065
## 0.260 0.260
##
## 0.281 0.281
## -0.066 -0.066
## 0.427 0.427
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.048 0.047 -1.029 0.303 -0.139 0.043
## .wx2 ~~
## .wy2 -0.014 0.040 -0.356 0.722 -0.094 0.065
## .wx3 ~~
## .wy3 0.055 0.035 1.553 0.121 -0.014 0.124
## .wx4 ~~
## .wy4 -0.049 0.038 -1.307 0.191 -0.123 0.025
## .wx5 ~~
## .wy5 0.027 0.031 0.869 0.385 -0.034 0.087
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.048 -0.048
##
## -0.022 -0.022
##
## 0.105 0.105
##
## -0.092 -0.092
##
## 0.061 0.061
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.009 0.047 -0.183 0.855 -0.101 0.083
## .LadderDif.2 0.013 0.058 0.218 0.828 -0.101 0.126
## .LadderDif.3 0.007 0.064 0.105 0.916 -0.118 0.132
## .LadderDif.4 0.027 0.067 0.406 0.685 -0.104 0.158
## .LadderDif.5 0.029 0.068 0.426 0.670 -0.105 0.163
## .gHealth.1 -0.003 0.046 -0.076 0.940 -0.094 0.087
## .gHealth.2 -0.088 0.053 -1.660 0.097 -0.192 0.016
## .gHealth.3 -0.073 0.056 -1.305 0.192 -0.182 0.037
## .gHealth.4 -0.055 0.059 -0.921 0.357 -0.171 0.062
## .gHealth.5 -0.100 0.061 -1.618 0.106 -0.220 0.021
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.009 -0.009
## 0.013 0.013
## 0.007 0.007
## 0.027 0.027
## 0.029 0.029
## -0.003 -0.004
## -0.088 -0.088
## -0.073 -0.074
## -0.055 -0.055
## -0.100 -0.099
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 1.000 0.067 14.987 0.000 0.869 1.131
## wy1 0.990 0.065 15.236 0.000 0.863 1.117
## .wx2 0.904 0.077 11.720 0.000 0.753 1.055
## .wy2 0.468 0.041 11.529 0.000 0.389 0.548
## .wx3 0.705 0.066 10.610 0.000 0.575 0.835
## .wy3 0.382 0.036 10.540 0.000 0.311 0.453
## .wx4 0.737 0.071 10.322 0.000 0.597 0.876
## .wy4 0.390 0.038 10.280 0.000 0.316 0.464
## .wx5 0.621 0.062 10.090 0.000 0.501 0.742
## .wy5 0.307 0.030 10.096 0.000 0.247 0.366
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.923 0.923
## 0.466 0.466
## 0.692 0.692
## 0.394 0.394
## 0.726 0.726
## 0.395 0.395
## 0.611 0.611
## 0.303 0.303
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderHealthCLPM_b2AR.fit, LadderHealthCLPM_b.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff
## LadderHealthCLPM_b2AR.fit 24 6788.9 6960.2 83.702
## LadderHealthCLPM_b.fit 30 6952.8 7099.0 259.528 175.83 6
## Pr(>Chisq)
## LadderHealthCLPM_b2AR.fit
## LadderHealthCLPM_b.fit < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LadderSleepCLPM <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gSleep.1 + 1*gSleep.2 + 1*gSleep.3 + 1*gSleep.4 + 1*gSleep.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gSleep.1
wy2 =~ 1*gSleep.2
wy3 =~ 1*gSleep.3
wy4 =~ 1*gSleep.4
wy5 =~ 1*gSleep.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2
wy4 ~ a*wx3 + wy3
wy5 ~ a*wx4 + wy4
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2
wx4 ~ wx3 + b*wy3
wx5 ~ wx4 + b*wy4
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderSleepCLPM.fit <- lavaan(LadderSleepCLPM, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderSleepCLPM.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 318.786
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1735.738
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.829
## Tucker-Lewis Index (TLI) 0.744
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3707.374
## Loglikelihood unrestricted model (H1) -3547.982
##
## Akaike (AIC) 7484.749
## Bayesian (BIC) 7630.977
## Sample-size adjusted Bayesian (BIC) 7519.890
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.141
## 90 Percent confidence interval - lower 0.128
## 90 Percent confidence interval - upper 0.156
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.127
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.997 1.000
##
## 0.998 1.000
##
## 1.003 1.000
##
## 1.000 1.000
##
## 0.994 1.000
##
## 0.985 1.000
##
## 0.998 1.000
##
## 0.956 1.000
##
## 1.031 1.000
##
## 1.031 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.061 0.020 -3.028 0.002 -0.101 -0.022
## wy1 0.782 0.034 22.874 0.000 0.715 0.850
## wy3 ~
## wx2 (a) -0.061 0.020 -3.028 0.002 -0.101 -0.022
## wy2 0.686 0.039 17.609 0.000 0.610 0.763
## wy4 ~
## wx3 (a) -0.061 0.020 -3.028 0.002 -0.101 -0.022
## wy3 0.820 0.043 19.282 0.000 0.737 0.904
## wy5 ~
## wx4 (a) -0.061 0.020 -3.028 0.002 -0.101 -0.022
## wy4 0.774 0.040 19.505 0.000 0.696 0.851
## wx2 ~
## wx1 0.524 0.047 11.213 0.000 0.433 0.616
## wy1 (b) -0.099 0.025 -3.873 0.000 -0.148 -0.049
## wx3 ~
## wx2 0.531 0.053 10.115 0.000 0.428 0.634
## wy2 (b) -0.099 0.025 -3.873 0.000 -0.148 -0.049
## wx4 ~
## wx3 0.511 0.054 9.549 0.000 0.406 0.616
## wy3 (b) -0.099 0.025 -3.873 0.000 -0.148 -0.049
## wx5 ~
## wx4 0.558 0.052 10.760 0.000 0.457 0.660
## wy4 (b) -0.099 0.025 -3.873 0.000 -0.148 -0.049
## Std.lv Std.all
##
## -0.061 -0.061
## 0.772 0.772
##
## -0.064 -0.064
## 0.717 0.717
##
## -0.060 -0.060
## 0.761 0.761
##
## -0.059 -0.059
## 0.773 0.773
##
## 0.524 0.524
## -0.097 -0.097
##
## 0.529 0.529
## -0.098 -0.098
##
## 0.513 0.513
## -0.094 -0.094
##
## 0.562 0.562
## -0.102 -0.102
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.241 0.048 -5.027 0.000 -0.335 -0.147
## .wx2 ~~
## .wy2 0.008 0.028 0.268 0.789 -0.048 0.063
## .wx3 ~~
## .wy3 0.008 0.033 0.237 0.813 -0.056 0.072
## .wx4 ~~
## .wy4 -0.003 0.035 -0.100 0.920 -0.072 0.065
## .wx5 ~~
## .wy5 -0.051 0.034 -1.513 0.130 -0.117 0.015
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.246 -0.246
##
## 0.015 0.015
##
## 0.014 0.014
##
## -0.006 -0.006
##
## -0.099 -0.099
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.002 0.047 0.034 0.973 -0.090 0.094
## .LadderDif.2 -0.005 0.051 -0.104 0.917 -0.106 0.095
## .LadderDif.3 -0.011 0.057 -0.192 0.848 -0.124 0.102
## .LadderDif.4 0.000 0.061 0.002 0.999 -0.120 0.120
## .LadderDif.5 -0.006 0.063 -0.098 0.922 -0.129 0.117
## .gSleep.1 2.762 0.046 60.463 0.000 2.673 2.852
## .gSleep.2 2.844 0.049 58.293 0.000 2.748 2.940
## .gSleep.3 2.938 0.052 56.628 0.000 2.836 3.040
## .gSleep.4 2.886 0.060 48.376 0.000 2.770 3.003
## .gSleep.5 2.922 0.062 46.956 0.000 2.800 3.044
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## 0.002 0.002
## -0.005 -0.005
## -0.011 -0.011
## 0.000 0.000
## -0.006 -0.006
## 2.762 2.806
## 2.844 2.849
## 2.938 3.074
## 2.886 2.801
## 2.922 2.834
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.994 0.067 14.919 0.000 0.863 1.124
## wy1 0.969 0.065 14.949 0.000 0.842 1.097
## .wx2 0.688 0.053 12.922 0.000 0.584 0.792
## .wy2 0.376 0.030 12.391 0.000 0.317 0.436
## .wx3 0.694 0.059 11.777 0.000 0.578 0.809
## .wy3 0.423 0.036 11.762 0.000 0.353 0.493
## .wx4 0.711 0.063 11.201 0.000 0.586 0.835
## .wy4 0.427 0.038 11.183 0.000 0.352 0.502
## .wx5 0.646 0.059 10.990 0.000 0.531 0.761
## .wy5 0.407 0.037 10.932 0.000 0.334 0.479
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.691 0.691
## 0.378 0.378
## 0.690 0.690
## 0.463 0.463
## 0.711 0.711
## 0.402 0.402
## 0.654 0.654
## 0.383 0.383
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000# Same model as above code, but fit with d_black dataset this time
LadderSleepCLPM_b.fit <- lavaan(LadderSleepCLPM, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderSleepCLPM_b.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 197.797
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1022.753
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.828
## Tucker-Lewis Index (TLI) 0.743
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3496.830
## Loglikelihood unrestricted model (H1) -3397.931
##
## Akaike (AIC) 7063.659
## Bayesian (BIC) 7209.887
## Sample-size adjusted Bayesian (BIC) 7098.800
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.108
## 90 Percent confidence interval - lower 0.094
## 90 Percent confidence interval - upper 0.122
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.111
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.998 1.000
##
## 1.011 1.000
##
## 1.005 1.000
##
## 0.996 1.000
##
## 0.973 1.000
##
## 0.981 1.000
##
## 0.993 1.000
##
## 1.030 1.000
##
## 0.994 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.051 0.024 -2.116 0.034 -0.098 -0.004
## wy1 0.619 0.046 13.358 0.000 0.528 0.710
## wy3 ~
## wx2 (a) -0.051 0.024 -2.116 0.034 -0.098 -0.004
## wy2 0.679 0.048 14.110 0.000 0.585 0.773
## wy4 ~
## wx3 (a) -0.051 0.024 -2.116 0.034 -0.098 -0.004
## wy3 0.731 0.050 14.674 0.000 0.633 0.829
## wy5 ~
## wx4 (a) -0.051 0.024 -2.116 0.034 -0.098 -0.004
## wy4 0.755 0.042 18.107 0.000 0.674 0.837
## wx2 ~
## wx1 0.260 0.061 4.233 0.000 0.139 0.380
## wy1 (b) -0.021 0.029 -0.710 0.478 -0.079 0.037
## wx3 ~
## wx2 0.543 0.059 9.218 0.000 0.427 0.658
## wy2 (b) -0.021 0.029 -0.710 0.478 -0.079 0.037
## wx4 ~
## wx3 0.462 0.063 7.294 0.000 0.338 0.587
## wy3 (b) -0.021 0.029 -0.710 0.478 -0.079 0.037
## wx5 ~
## wx4 0.482 0.060 8.033 0.000 0.364 0.599
## wy4 (b) -0.021 0.029 -0.710 0.478 -0.079 0.037
## Std.lv Std.all
##
## -0.052 -0.052
## 0.614 0.614
##
## -0.051 -0.051
## 0.670 0.670
##
## -0.050 -0.050
## 0.705 0.705
##
## -0.052 -0.052
## 0.783 0.783
##
## 0.260 0.260
## -0.020 -0.020
##
## 0.536 0.536
## -0.020 -0.020
##
## 0.465 0.465
## -0.021 -0.021
##
## 0.487 0.487
## -0.022 -0.022
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.056 0.046 1.220 0.223 -0.034 0.145
## .wx2 ~~
## .wy2 -0.005 0.046 -0.114 0.909 -0.095 0.084
## .wx3 ~~
## .wy3 -0.045 0.042 -1.082 0.279 -0.127 0.037
## .wx4 ~~
## .wy4 -0.029 0.044 -0.659 0.510 -0.116 0.058
## .wx5 ~~
## .wy5 0.066 0.037 1.764 0.078 -0.007 0.139
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.057 0.057
##
## -0.007 -0.007
##
## -0.072 -0.072
##
## -0.045 -0.045
##
## 0.125 0.125
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.007 0.047 -0.158 0.875 -0.099 0.085
## .LadderDif.2 0.005 0.059 0.087 0.931 -0.110 0.120
## .LadderDif.3 0.003 0.065 0.051 0.959 -0.124 0.130
## .LadderDif.4 0.020 0.068 0.295 0.768 -0.113 0.153
## .LadderDif.5 0.009 0.069 0.132 0.895 -0.126 0.145
## .gSleep.1 3.022 0.045 66.789 0.000 2.933 3.111
## .gSleep.2 3.036 0.054 56.483 0.000 2.930 3.141
## .gSleep.3 3.186 0.061 52.355 0.000 3.067 3.305
## .gSleep.4 3.271 0.067 48.870 0.000 3.140 3.403
## .gSleep.5 3.266 0.067 49.069 0.000 3.136 3.396
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.007 -0.007
## 0.005 0.005
## 0.003 0.003
## 0.020 0.020
## 0.009 0.009
## 3.022 3.107
## 3.036 3.096
## 3.186 3.207
## 3.271 3.175
## 3.266 3.285
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.998 0.066 15.017 0.000 0.868 1.128
## wy1 0.946 0.062 15.171 0.000 0.824 1.068
## .wx2 0.929 0.079 11.793 0.000 0.775 1.083
## .wy2 0.600 0.052 11.571 0.000 0.498 0.701
## .wx3 0.727 0.068 10.679 0.000 0.594 0.860
## .wy3 0.539 0.050 10.700 0.000 0.441 0.638
## .wx4 0.790 0.077 10.328 0.000 0.640 0.940
## .wy4 0.525 0.051 10.310 0.000 0.425 0.624
## .wx5 0.754 0.075 10.095 0.000 0.607 0.900
## .wy5 0.373 0.037 10.092 0.000 0.300 0.445
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.933 0.933
## 0.624 0.624
## 0.712 0.712
## 0.547 0.547
## 0.782 0.782
## 0.494 0.494
## 0.761 0.761
## 0.377 0.377
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000LadderSleepCLPM_2AR <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gSleep.1 + 1*gSleep.2 + 1*gSleep.3 + 1*gSleep.4 + 1*gSleep.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gSleep.1
wy2 =~ 1*gSleep.2
wy3 =~ 1*gSleep.3
wy4 =~ 1*gSleep.4
wy5 =~ 1*gSleep.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2 + wy1
wy4 ~ a*wx3 + wy3 + wy2
wy5 ~ a*wx4 + wy4 + wy3
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2 + wx1
wx4 ~ wx3 + b*wy3 + wx2
wx5 ~ wx4 + b*wy4 + wx3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderSleepCLPM_2AR.fit <- lavaan(LadderSleepCLPM_2AR, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderSleepCLPM_2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 104.752
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1735.738
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.952
## Tucker-Lewis Index (TLI) 0.910
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3600.358
## Loglikelihood unrestricted model (H1) -3547.982
##
## Akaike (AIC) 7282.716
## Bayesian (BIC) 7454.011
## Sample-size adjusted Bayesian (BIC) 7323.881
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.084
## 90 Percent confidence interval - lower 0.068
## 90 Percent confidence interval - upper 0.100
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.997 1.000
##
## 0.996 1.000
##
## 1.005 1.000
##
## 0.991 1.000
##
## 0.988 1.000
##
## 0.997 1.000
##
## 0.944 1.000
##
## 1.023 1.000
##
## 1.023 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.040 0.020 -2.028 0.043 -0.079 -0.001
## wy1 0.785 0.034 23.029 0.000 0.718 0.852
## wy3 ~
## wx2 (a) -0.040 0.020 -2.028 0.043 -0.079 -0.001
## wy2 0.380 0.064 5.976 0.000 0.256 0.505
## wy1 0.382 0.064 5.963 0.000 0.256 0.507
## wy4 ~
## wx3 (a) -0.040 0.020 -2.028 0.043 -0.079 -0.001
## wy3 0.606 0.061 9.955 0.000 0.486 0.725
## wy2 0.280 0.059 4.756 0.000 0.165 0.396
## wy5 ~
## wx4 (a) -0.040 0.020 -2.028 0.043 -0.079 -0.001
## wy4 0.581 0.060 9.634 0.000 0.462 0.699
## wy3 0.272 0.064 4.218 0.000 0.145 0.398
## wx2 ~
## wx1 0.532 0.047 11.434 0.000 0.441 0.623
## wy1 (b) -0.071 0.024 -2.955 0.003 -0.117 -0.024
## wx3 ~
## wx2 0.288 0.058 4.940 0.000 0.174 0.402
## wy2 (b) -0.071 0.024 -2.955 0.003 -0.117 -0.024
## wx1 0.425 0.057 7.516 0.000 0.314 0.535
## wx4 ~
## wx3 0.302 0.060 5.008 0.000 0.184 0.420
## wy3 (b) -0.071 0.024 -2.955 0.003 -0.117 -0.024
## wx2 0.394 0.063 6.292 0.000 0.271 0.516
## wx5 ~
## wx4 0.328 0.054 6.095 0.000 0.223 0.434
## wy4 (b) -0.071 0.024 -2.955 0.003 -0.117 -0.024
## wx3 0.445 0.053 8.331 0.000 0.340 0.549
## Std.lv Std.all
##
## -0.040 -0.040
## 0.778 0.778
##
## -0.042 -0.042
## 0.402 0.402
## 0.400 0.400
##
## -0.039 -0.039
## 0.559 0.559
## 0.273 0.273
##
## -0.039 -0.039
## 0.581 0.581
## 0.251 0.251
##
## 0.533 0.533
## -0.070 -0.070
##
## 0.288 0.288
## -0.071 -0.071
## 0.426 0.426
##
## 0.299 0.299
## -0.066 -0.066
## 0.390 0.390
##
## 0.333 0.333
## -0.073 -0.073
## 0.447 0.447
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.247 0.048 -5.112 0.000 -0.342 -0.152
## .wx2 ~~
## .wy2 0.007 0.028 0.243 0.808 -0.048 0.062
## .wx3 ~~
## .wy3 0.003 0.028 0.110 0.912 -0.052 0.059
## .wx4 ~~
## .wy4 0.039 0.032 1.236 0.217 -0.023 0.102
## .wx5 ~~
## .wy5 -0.032 0.029 -1.120 0.263 -0.088 0.024
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.250 -0.250
##
## 0.014 0.014
##
## 0.007 0.007
##
## 0.080 0.080
##
## -0.074 -0.074
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.003 0.047 0.061 0.951 -0.089 0.095
## .LadderDif.2 -0.008 0.051 -0.147 0.883 -0.108 0.093
## .LadderDif.3 -0.033 0.055 -0.611 0.541 -0.141 0.074
## .LadderDif.4 -0.011 0.059 -0.186 0.853 -0.127 0.105
## .LadderDif.5 -0.020 0.059 -0.332 0.740 -0.136 0.097
## .gSleep.1 2.757 0.046 60.250 0.000 2.668 2.847
## .gSleep.2 2.845 0.049 58.423 0.000 2.750 2.941
## .gSleep.3 2.924 0.050 58.830 0.000 2.827 3.021
## .gSleep.4 2.888 0.057 50.878 0.000 2.776 2.999
## .gSleep.5 2.920 0.059 49.441 0.000 2.804 3.035
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## 0.003 0.003
## -0.008 -0.008
## -0.033 -0.034
## -0.011 -0.011
## -0.020 -0.020
## 2.757 2.791
## 2.845 2.855
## 2.924 3.097
## 2.888 2.823
## 2.920 2.855
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.998 0.067 14.919 0.000 0.867 1.129
## wy1 0.976 0.065 14.952 0.000 0.848 1.104
## .wx2 0.688 0.053 12.943 0.000 0.583 0.792
## .wy2 0.375 0.030 12.451 0.000 0.316 0.434
## .wx3 0.570 0.049 11.534 0.000 0.473 0.667
## .wy3 0.367 0.032 11.488 0.000 0.304 0.430
## .wx4 0.617 0.055 11.199 0.000 0.509 0.725
## .wy4 0.394 0.035 11.153 0.000 0.325 0.464
## .wx5 0.500 0.046 10.986 0.000 0.411 0.589
## .wy5 0.382 0.035 10.908 0.000 0.313 0.450
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.692 0.692
## 0.377 0.377
## 0.574 0.574
## 0.412 0.412
## 0.611 0.611
## 0.377 0.377
## 0.509 0.509
## 0.365 0.365
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderSleepCLPM_2AR.fit, LadderSleepCLPM.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## LadderSleepCLPM_2AR.fit 24 7282.7 7454 104.75
## LadderSleepCLPM.fit 30 7484.7 7631 318.79 214.03 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Same model as above code, but fit with d_black dataset this time
LadderSleepCLPM_b2AR.fit <- lavaan(LadderSleepCLPM_2AR, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderSleepCLPM_b2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 46.666
## Degrees of freedom 24
## P-value (Chi-square) 0.004
##
## Model Test Baseline Model:
##
## Test statistic 1022.753
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.977
## Tucker-Lewis Index (TLI) 0.957
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3421.264
## Loglikelihood unrestricted model (H1) -3397.931
##
## Akaike (AIC) 6924.528
## Bayesian (BIC) 7095.823
## Sample-size adjusted Bayesian (BIC) 6965.693
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.044
## 90 Percent confidence interval - lower 0.025
## 90 Percent confidence interval - upper 0.063
## P-value RMSEA <= 0.05 0.668
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.037
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 0.999 1.000
##
## 1.014 1.000
##
## 1.012 1.000
##
## 1.008 1.000
##
## 0.973 1.000
##
## 0.981 1.000
##
## 0.983 1.000
##
## 1.018 1.000
##
## 0.983 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) -0.059 0.023 -2.568 0.010 -0.104 -0.014
## wy1 0.621 0.046 13.406 0.000 0.530 0.711
## wy3 ~
## wx2 (a) -0.059 0.023 -2.568 0.010 -0.104 -0.014
## wy2 0.504 0.060 8.396 0.000 0.386 0.622
## wy1 0.268 0.060 4.487 0.000 0.151 0.385
## wy4 ~
## wx3 (a) -0.059 0.023 -2.568 0.010 -0.104 -0.014
## wy3 0.481 0.062 7.711 0.000 0.359 0.603
## wy2 0.366 0.061 5.969 0.000 0.246 0.486
## wy5 ~
## wx4 (a) -0.059 0.023 -2.568 0.010 -0.104 -0.014
## wy4 0.504 0.054 9.292 0.000 0.397 0.610
## wy3 0.358 0.056 6.451 0.000 0.249 0.467
## wx2 ~
## wx1 0.267 0.061 4.382 0.000 0.147 0.386
## wy1 (b) -0.008 0.028 -0.274 0.784 -0.064 0.048
## wx3 ~
## wx2 0.504 0.060 8.384 0.000 0.386 0.621
## wy2 (b) -0.008 0.028 -0.274 0.784 -0.064 0.048
## wx1 0.173 0.062 2.782 0.005 0.051 0.296
## wx4 ~
## wx3 0.327 0.071 4.608 0.000 0.188 0.466
## wy3 (b) -0.008 0.028 -0.274 0.784 -0.064 0.048
## wx2 0.273 0.072 3.809 0.000 0.133 0.413
## wx5 ~
## wx4 0.294 0.061 4.804 0.000 0.174 0.414
## wy4 (b) -0.008 0.028 -0.274 0.784 -0.064 0.048
## wx3 0.425 0.064 6.690 0.000 0.301 0.550
## Std.lv Std.all
##
## -0.060 -0.060
## 0.615 0.615
##
## -0.060 -0.060
## 0.503 0.503
## 0.265 0.265
##
## -0.059 -0.059
## 0.464 0.464
## 0.353 0.353
##
## -0.061 -0.061
## 0.522 0.522
## 0.358 0.358
##
## 0.267 0.267
## -0.008 -0.008
##
## 0.496 0.496
## -0.008 -0.008
## 0.171 0.171
##
## 0.327 0.327
## -0.008 -0.008
## 0.269 0.269
##
## 0.295 0.295
## -0.008 -0.008
## 0.428 0.428
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.059 0.046 1.287 0.198 -0.031 0.148
## .wx2 ~~
## .wy2 -0.008 0.046 -0.177 0.859 -0.097 0.081
## .wx3 ~~
## .wy3 -0.038 0.039 -0.971 0.332 -0.115 0.039
## .wx4 ~~
## .wy4 -0.035 0.040 -0.885 0.376 -0.114 0.043
## .wx5 ~~
## .wy5 0.069 0.031 2.219 0.026 0.008 0.131
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.060 0.060
##
## -0.011 -0.011
##
## -0.065 -0.065
##
## -0.061 -0.061
##
## 0.158 0.158
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.009 0.047 -0.182 0.855 -0.101 0.084
## .LadderDif.2 0.005 0.059 0.085 0.932 -0.110 0.120
## .LadderDif.3 -0.001 0.064 -0.014 0.989 -0.126 0.124
## .LadderDif.4 0.019 0.067 0.284 0.776 -0.113 0.151
## .LadderDif.5 0.018 0.068 0.264 0.792 -0.116 0.152
## .gSleep.1 3.020 0.045 66.820 0.000 2.931 3.108
## .gSleep.2 3.036 0.054 56.475 0.000 2.931 3.142
## .gSleep.3 3.183 0.058 54.705 0.000 3.069 3.297
## .gSleep.4 3.261 0.063 51.899 0.000 3.137 3.384
## .gSleep.5 3.250 0.062 52.337 0.000 3.128 3.371
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.009 -0.009
## 0.005 0.005
## -0.001 -0.001
## 0.019 0.019
## 0.018 0.018
## 3.020 3.104
## 3.036 3.094
## 3.183 3.237
## 3.261 3.202
## 3.250 3.306
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 1.000 0.067 14.985 0.000 0.869 1.131
## wy1 0.947 0.062 15.178 0.000 0.824 1.069
## .wx2 0.926 0.079 11.790 0.000 0.772 1.080
## .wy2 0.600 0.052 11.592 0.000 0.498 0.701
## .wx3 0.698 0.066 10.640 0.000 0.569 0.826
## .wy3 0.492 0.046 10.622 0.000 0.402 0.583
## .wx4 0.741 0.072 10.324 0.000 0.601 0.882
## .wy4 0.449 0.044 10.284 0.000 0.364 0.535
## .wx5 0.619 0.061 10.099 0.000 0.499 0.740
## .wy5 0.313 0.031 10.090 0.000 0.252 0.374
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.929 0.929
## 0.622 0.622
## 0.679 0.679
## 0.509 0.509
## 0.724 0.724
## 0.433 0.433
## 0.609 0.609
## 0.324 0.324
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderSleepCLPM_b2AR.fit, LadderSleepCLPM_b.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## LadderSleepCLPM_b2AR.fit 24 6924.5 7095.8 46.666
## LadderSleepCLPM_b.fit 30 7063.7 7209.9 197.797 151.13 6 < 2.2e-16
##
## LadderSleepCLPM_b2AR.fit
## LadderSleepCLPM_b.fit ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1LadderDepCLPM <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*dep.1 + 1*dep.2 + 1*dep.3 + 1*dep.4 + 1*dep.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*dep.1
wy2 =~ 1*dep.2
wy3 =~ 1*dep.3
wy4 =~ 1*dep.4
wy5 =~ 1*dep.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2
wy4 ~ a*wx3 + wy3
wy5 ~ a*wx4 + wy4
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2
wx4 ~ wx3 + b*wy3
wx5 ~ wx4 + b*wy4
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderDepCLPM.fit <- lavaan(LadderDepCLPM, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderDepCLPM.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 39 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 348.208
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1965.104
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.834
## Tucker-Lewis Index (TLI) 0.751
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3605.118
## Loglikelihood unrestricted model (H1) -3431.014
##
## Akaike (AIC) 7280.236
## Bayesian (BIC) 7426.464
## Sample-size adjusted Bayesian (BIC) 7315.377
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.148
## 90 Percent confidence interval - lower 0.135
## 90 Percent confidence interval - upper 0.163
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.124
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.997 1.000
##
## 0.993 1.000
##
## 1.001 1.000
##
## 1.000 1.000
##
## 0.998 1.000
##
## 1.011 1.000
##
## 1.007 1.000
##
## 1.009 1.000
##
## 0.998 1.000
##
## 1.006 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) 0.062 0.019 3.302 0.001 0.025 0.098
## wy1 0.798 0.033 24.193 0.000 0.733 0.862
## wy3 ~
## wx2 (a) 0.062 0.019 3.302 0.001 0.025 0.098
## wy2 0.799 0.036 22.182 0.000 0.729 0.870
## wy4 ~
## wx3 (a) 0.062 0.019 3.302 0.001 0.025 0.098
## wy3 0.776 0.038 20.622 0.000 0.702 0.850
## wy5 ~
## wx4 (a) 0.062 0.019 3.302 0.001 0.025 0.098
## wy4 0.817 0.037 22.153 0.000 0.744 0.889
## wx2 ~
## wx1 0.505 0.047 10.784 0.000 0.413 0.597
## wy1 (b) 0.127 0.026 4.826 0.000 0.075 0.178
## wx3 ~
## wx2 0.513 0.053 9.730 0.000 0.410 0.617
## wy2 (b) 0.127 0.026 4.826 0.000 0.075 0.178
## wx4 ~
## wx3 0.500 0.053 9.384 0.000 0.396 0.605
## wy3 (b) 0.127 0.026 4.826 0.000 0.075 0.178
## wx5 ~
## wx4 0.550 0.051 10.744 0.000 0.450 0.651
## wy4 (b) 0.127 0.026 4.826 0.000 0.075 0.178
## Std.lv Std.all
##
## 0.061 0.061
## 0.801 0.801
##
## 0.061 0.061
## 0.798 0.798
##
## 0.062 0.062
## 0.784 0.784
##
## 0.061 0.061
## 0.810 0.810
##
## 0.507 0.507
## 0.129 0.129
##
## 0.509 0.509
## 0.128 0.128
##
## 0.500 0.500
## 0.128 0.128
##
## 0.551 0.551
## 0.127 0.127
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.301 0.050 6.027 0.000 0.203 0.399
## .wx2 ~~
## .wy2 -0.021 0.026 -0.808 0.419 -0.073 0.030
## .wx3 ~~
## .wy3 -0.021 0.029 -0.706 0.480 -0.078 0.037
## .wx4 ~~
## .wy4 0.044 0.032 1.368 0.171 -0.019 0.107
## .wx5 ~~
## .wy5 0.061 0.029 2.066 0.039 0.003 0.118
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.299 0.299
##
## -0.045 -0.045
##
## -0.042 -0.042
##
## 0.087 0.087
##
## 0.134 0.134
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.002 0.047 0.046 0.963 -0.090 0.094
## .LadderDif.2 -0.004 0.051 -0.076 0.939 -0.104 0.096
## .LadderDif.3 -0.000 0.057 -0.002 0.999 -0.113 0.112
## .LadderDif.4 0.015 0.061 0.248 0.804 -0.105 0.135
## .LadderDif.5 0.011 0.063 0.175 0.861 -0.112 0.134
## .dep.1 0.008 0.047 0.175 0.861 -0.084 0.100
## .dep.2 0.044 0.049 0.911 0.362 -0.051 0.140
## .dep.3 0.066 0.053 1.237 0.216 -0.038 0.170
## .dep.4 0.057 0.056 1.012 0.311 -0.054 0.168
## .dep.5 0.045 0.059 0.749 0.454 -0.072 0.161
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## 0.002 0.002
## -0.004 -0.004
## -0.000 -0.000
## 0.015 0.015
## 0.011 0.011
## 0.008 0.008
## 0.044 0.044
## 0.066 0.065
## 0.057 0.057
## 0.045 0.044
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.994 0.067 14.913 0.000 0.864 1.125
## wy1 1.022 0.068 14.993 0.000 0.889 1.156
## .wx2 0.677 0.052 12.931 0.000 0.575 0.780
## .wy2 0.330 0.027 12.363 0.000 0.277 0.382
## .wx3 0.694 0.059 11.788 0.000 0.579 0.810
## .wy3 0.342 0.029 11.742 0.000 0.285 0.399
## .wx4 0.706 0.063 11.200 0.000 0.583 0.830
## .wy4 0.359 0.032 11.163 0.000 0.296 0.422
## .wx5 0.641 0.058 10.994 0.000 0.527 0.755
## .wy5 0.319 0.029 10.993 0.000 0.262 0.376
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.687 0.687
## 0.325 0.325
## 0.693 0.693
## 0.336 0.336
## 0.706 0.706
## 0.360 0.360
## 0.644 0.644
## 0.315 0.315
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000# Same model as above code, but fit with d_black dataset this time
LadderDepCLPM_b.fit <- lavaan(LadderDepCLPM, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderDepCLPM_b.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 31 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 41
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 210.219
## Degrees of freedom 30
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1194.723
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.843
## Tucker-Lewis Index (TLI) 0.765
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3420.427
## Loglikelihood unrestricted model (H1) -3315.318
##
## Akaike (AIC) 6910.855
## Bayesian (BIC) 7057.083
## Sample-size adjusted Bayesian (BIC) 6945.996
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.112
## 90 Percent confidence interval - lower 0.098
## 90 Percent confidence interval - upper 0.126
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.114
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.997 1.000
##
## 1.011 1.000
##
## 1.008 1.000
##
## 0.996 1.000
##
## 0.998 1.000
##
## 1.006 1.000
##
## 1.005 1.000
##
## 0.991 1.000
##
## 0.994 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) 0.016 0.022 0.730 0.465 -0.027 0.059
## wy1 0.677 0.046 14.595 0.000 0.586 0.768
## wy3 ~
## wx2 (a) 0.016 0.022 0.730 0.465 -0.027 0.059
## wy2 0.773 0.042 18.203 0.000 0.689 0.856
## wy4 ~
## wx3 (a) 0.016 0.022 0.730 0.465 -0.027 0.059
## wy3 0.785 0.040 19.583 0.000 0.706 0.863
## wy5 ~
## wx4 (a) 0.016 0.022 0.730 0.465 -0.027 0.059
## wy4 0.804 0.042 19.314 0.000 0.722 0.885
## wx2 ~
## wx1 0.258 0.061 4.241 0.000 0.139 0.378
## wy1 (b) 0.031 0.030 1.026 0.305 -0.028 0.089
## wx3 ~
## wx2 0.542 0.059 9.194 0.000 0.427 0.658
## wy2 (b) 0.031 0.030 1.026 0.305 -0.028 0.089
## wx4 ~
## wx3 0.467 0.063 7.434 0.000 0.344 0.590
## wy3 (b) 0.031 0.030 1.026 0.305 -0.028 0.089
## wx5 ~
## wx4 0.482 0.060 8.002 0.000 0.364 0.600
## wy4 (b) 0.031 0.030 1.026 0.305 -0.028 0.089
## Std.lv Std.all
##
## 0.016 0.016
## 0.672 0.672
##
## 0.016 0.016
## 0.773 0.773
##
## 0.016 0.016
## 0.796 0.796
##
## 0.016 0.016
## 0.802 0.802
##
## 0.259 0.259
## 0.031 0.031
##
## 0.535 0.535
## 0.031 0.031
##
## 0.468 0.468
## 0.031 0.031
##
## 0.488 0.488
## 0.030 0.030
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.010 0.047 0.203 0.839 -0.082 0.101
## .wx2 ~~
## .wy2 -0.022 0.044 -0.490 0.624 -0.108 0.065
## .wx3 ~~
## .wy3 0.023 0.036 0.646 0.519 -0.047 0.094
## .wx4 ~~
## .wy4 0.070 0.037 1.901 0.057 -0.002 0.142
## .wx5 ~~
## .wy5 0.002 0.036 0.042 0.967 -0.069 0.072
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.010 0.010
##
## -0.030 -0.030
##
## 0.043 0.043
##
## 0.132 0.132
##
## 0.003 0.003
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.007 0.047 -0.158 0.875 -0.099 0.085
## .LadderDif.2 0.006 0.058 0.104 0.917 -0.109 0.121
## .LadderDif.3 0.005 0.065 0.079 0.937 -0.122 0.132
## .LadderDif.4 0.022 0.068 0.329 0.742 -0.111 0.156
## .LadderDif.5 0.011 0.069 0.158 0.875 -0.125 0.147
## .dep.1 0.002 0.046 0.045 0.964 -0.089 0.093
## .dep.2 0.039 0.054 0.713 0.476 -0.068 0.145
## .dep.3 0.054 0.059 0.901 0.368 -0.063 0.170
## .dep.4 0.052 0.062 0.834 0.404 -0.070 0.174
## .dep.5 0.055 0.065 0.840 0.401 -0.073 0.182
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.007 -0.007
## 0.006 0.006
## 0.005 0.005
## 0.022 0.022
## 0.011 0.011
## 0.002 0.002
## 0.039 0.038
## 0.054 0.053
## 0.052 0.052
## 0.055 0.055
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.998 0.066 15.017 0.000 0.868 1.128
## wy1 0.997 0.066 15.133 0.000 0.868 1.126
## .wx2 0.926 0.079 11.780 0.000 0.772 1.080
## .wy2 0.554 0.048 11.436 0.000 0.459 0.649
## .wx3 0.728 0.068 10.677 0.000 0.594 0.862
## .wy3 0.406 0.038 10.700 0.000 0.331 0.480
## .wx4 0.792 0.077 10.325 0.000 0.642 0.942
## .wy4 0.359 0.035 10.305 0.000 0.291 0.427
## .wx5 0.752 0.075 10.092 0.000 0.606 0.898
## .wy5 0.349 0.035 10.062 0.000 0.281 0.417
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.932 0.932
## 0.547 0.547
## 0.713 0.713
## 0.402 0.402
## 0.779 0.779
## 0.365 0.365
## 0.758 0.758
## 0.354 0.354
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000LadderDepCLPM_2AR <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*dep.1 + 1*dep.2 + 1*dep.3 + 1*dep.4 + 1*dep.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*dep.1
wy2 =~ 1*dep.2
wy3 =~ 1*dep.3
wy4 =~ 1*dep.4
wy5 =~ 1*dep.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ a*wx1 + wy1
wy3 ~ a*wx2 + wy2 + wy1
wy4 ~ a*wx3 + wy3 + wy2
wy5 ~ a*wx4 + wy4 + wy3
wx2 ~ wx1 + b*wy1
wx3 ~ wx2 + b*wy2 + wx1
wx4 ~ wx3 + b*wy3 + wx2
wx5 ~ wx4 + b*wy4 + wx3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIx ~~ 0*RIy
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wx3 ~~ wx3
wy3 ~~ wy3
wx4 ~~ wx4
wy4 ~~ wy4
wx5 ~~ wx5
wy5 ~~ wy5
'
LadderDepCLPM_2AR.fit <- lavaan(LadderDepCLPM_2AR, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderDepCLPM_2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 38 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 105.608
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1965.104
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.957
## Tucker-Lewis Index (TLI) 0.920
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3483.818
## Loglikelihood unrestricted model (H1) -3431.014
##
## Akaike (AIC) 7049.636
## Bayesian (BIC) 7220.932
## Sample-size adjusted Bayesian (BIC) 7090.801
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.084
## 90 Percent confidence interval - lower 0.068
## 90 Percent confidence interval - upper 0.101
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.045
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.992 1.000
##
## 0.995 1.000
##
## 1.001 1.000
##
## 0.991 1.000
##
## 1.016 1.000
##
## 1.007 1.000
##
## 1.006 1.000
##
## 0.998 1.000
##
## 1.003 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) 0.042 0.018 2.369 0.018 0.007 0.077
## wy1 0.803 0.033 24.524 0.000 0.739 0.867
## wy3 ~
## wx2 (a) 0.042 0.018 2.369 0.018 0.007 0.077
## wy2 0.520 0.063 8.227 0.000 0.396 0.643
## wy1 0.338 0.063 5.362 0.000 0.215 0.462
## wy4 ~
## wx3 (a) 0.042 0.018 2.369 0.018 0.007 0.077
## wy3 0.528 0.063 8.395 0.000 0.405 0.651
## wy2 0.313 0.064 4.872 0.000 0.187 0.440
## wy5 ~
## wx4 (a) 0.042 0.018 2.369 0.018 0.007 0.077
## wy4 0.482 0.053 9.015 0.000 0.377 0.586
## wy3 0.422 0.052 8.053 0.000 0.319 0.524
## wx2 ~
## wx1 0.524 0.047 11.235 0.000 0.432 0.615
## wy1 (b) 0.080 0.025 3.201 0.001 0.031 0.129
## wx3 ~
## wx2 0.282 0.059 4.803 0.000 0.167 0.397
## wy2 (b) 0.080 0.025 3.201 0.001 0.031 0.129
## wx1 0.415 0.057 7.263 0.000 0.303 0.527
## wx4 ~
## wx3 0.315 0.060 5.254 0.000 0.198 0.433
## wy3 (b) 0.080 0.025 3.201 0.001 0.031 0.129
## wx2 0.370 0.063 5.877 0.000 0.246 0.493
## wx5 ~
## wx4 0.324 0.053 6.078 0.000 0.219 0.428
## wy4 (b) 0.080 0.025 3.201 0.001 0.031 0.129
## wx3 0.443 0.053 8.354 0.000 0.339 0.547
## Std.lv Std.all
##
## 0.042 0.042
## 0.810 0.810
##
## 0.042 0.042
## 0.520 0.520
## 0.341 0.341
##
## 0.042 0.042
## 0.532 0.532
## 0.316 0.316
##
## 0.042 0.042
## 0.479 0.479
## 0.423 0.423
##
## 0.527 0.527
## 0.082 0.082
##
## 0.281 0.281
## 0.081 0.081
## 0.417 0.417
##
## 0.313 0.313
## 0.080 0.080
## 0.366 0.366
##
## 0.327 0.327
## 0.081 0.081
## 0.445 0.445
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.306 0.050 6.075 0.000 0.207 0.404
## .wx2 ~~
## .wy2 -0.021 0.026 -0.799 0.424 -0.072 0.030
## .wx3 ~~
## .wy3 -0.023 0.026 -0.894 0.371 -0.074 0.028
## .wx4 ~~
## .wy4 0.007 0.029 0.260 0.795 -0.049 0.064
## .wx5 ~~
## .wy5 0.043 0.023 1.868 0.062 -0.002 0.088
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.301 0.301
##
## -0.045 -0.045
##
## -0.055 -0.055
##
## 0.017 0.017
##
## 0.122 0.122
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.003 0.047 0.060 0.952 -0.089 0.095
## .LadderDif.2 -0.007 0.051 -0.135 0.893 -0.107 0.093
## .LadderDif.3 -0.026 0.055 -0.469 0.639 -0.133 0.082
## .LadderDif.4 -0.003 0.059 -0.044 0.965 -0.118 0.113
## .LadderDif.5 -0.007 0.059 -0.119 0.905 -0.123 0.109
## .dep.1 0.007 0.047 0.151 0.880 -0.085 0.099
## .dep.2 0.045 0.049 0.932 0.351 -0.050 0.141
## .dep.3 0.071 0.052 1.383 0.167 -0.030 0.172
## .dep.4 0.064 0.054 1.182 0.237 -0.042 0.170
## .dep.5 0.060 0.055 1.089 0.276 -0.048 0.169
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## 0.003 0.003
## -0.007 -0.007
## -0.026 -0.026
## -0.003 -0.003
## -0.007 -0.007
## 0.007 0.007
## 0.045 0.045
## 0.071 0.071
## 0.064 0.064
## 0.060 0.060
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.998 0.067 14.921 0.000 0.867 1.130
## wy1 1.032 0.069 14.983 0.000 0.897 1.167
## .wx2 0.679 0.052 12.937 0.000 0.576 0.782
## .wy2 0.326 0.026 12.467 0.000 0.275 0.378
## .wx3 0.577 0.050 11.539 0.000 0.479 0.675
## .wy3 0.308 0.027 11.542 0.000 0.255 0.360
## .wx4 0.618 0.055 11.200 0.000 0.510 0.726
## .wy4 0.326 0.029 11.170 0.000 0.268 0.383
## .wx5 0.498 0.045 10.993 0.000 0.409 0.587
## .wy5 0.251 0.023 10.981 0.000 0.206 0.296
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.689 0.689
## 0.322 0.322
## 0.583 0.583
## 0.304 0.304
## 0.616 0.616
## 0.327 0.327
## 0.507 0.507
## 0.249 0.249
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderDepCLPM_2AR.fit, LadderDepCLPM.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## LadderDepCLPM_2AR.fit 24 7049.6 7220.9 105.61
## LadderDepCLPM.fit 30 7280.2 7426.5 348.21 242.6 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Same model as above code, but fit with d_black dataset this time
LadderDepCLPM_b2AR.fit <- lavaan(LadderDepCLPM_2AR, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(LadderDepCLPM_b2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 37 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
## Number of equality constraints 6
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 64.280
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1194.723
## Degrees of freedom 45
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.965
## Tucker-Lewis Index (TLI) 0.934
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -3347.458
## Loglikelihood unrestricted model (H1) -3315.318
##
## Akaike (AIC) 6776.915
## Bayesian (BIC) 6948.211
## Sample-size adjusted Bayesian (BIC) 6818.081
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059
## 90 Percent confidence interval - lower 0.042
## 90 Percent confidence interval - upper 0.077
## P-value RMSEA <= 0.05 0.182
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 0.997 1.000
##
## 1.015 1.000
##
## 1.013 1.000
##
## 1.011 1.000
##
## 0.996 1.000
##
## 1.007 1.000
##
## 0.998 1.000
##
## 0.989 1.000
##
## 0.993 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wx1 (a) 0.017 0.021 0.797 0.425 -0.024 0.058
## wy1 0.677 0.047 14.446 0.000 0.585 0.769
## wy3 ~
## wx2 (a) 0.017 0.021 0.797 0.425 -0.024 0.058
## wy2 0.508 0.056 9.025 0.000 0.398 0.618
## wy1 0.376 0.058 6.471 0.000 0.262 0.489
## wy4 ~
## wx3 (a) 0.017 0.021 0.797 0.425 -0.024 0.058
## wy3 0.579 0.062 9.397 0.000 0.459 0.700
## wy2 0.267 0.063 4.274 0.000 0.145 0.390
## wy5 ~
## wx4 (a) 0.017 0.021 0.797 0.425 -0.024 0.058
## wy4 0.486 0.068 7.183 0.000 0.354 0.619
## wy3 0.391 0.068 5.741 0.000 0.257 0.524
## wx2 ~
## wx1 0.266 0.060 4.409 0.000 0.148 0.384
## wy1 (b) 0.026 0.029 0.916 0.360 -0.030 0.083
## wx3 ~
## wx2 0.503 0.060 8.348 0.000 0.385 0.621
## wy2 (b) 0.026 0.029 0.916 0.360 -0.030 0.083
## wx1 0.176 0.062 2.835 0.005 0.054 0.298
## wx4 ~
## wx3 0.340 0.070 4.842 0.000 0.202 0.478
## wy3 (b) 0.026 0.029 0.916 0.360 -0.030 0.083
## wx2 0.257 0.072 3.576 0.000 0.116 0.398
## wx5 ~
## wx4 0.291 0.062 4.718 0.000 0.170 0.411
## wy4 (b) 0.026 0.029 0.916 0.360 -0.030 0.083
## wx3 0.431 0.064 6.720 0.000 0.305 0.557
## Std.lv Std.all
##
## 0.017 0.017
## 0.669 0.669
##
## 0.017 0.017
## 0.512 0.512
## 0.375 0.375
##
## 0.017 0.017
## 0.585 0.585
## 0.272 0.272
##
## 0.017 0.017
## 0.485 0.485
## 0.393 0.393
##
## 0.267 0.267
## 0.026 0.026
##
## 0.494 0.494
## 0.026 0.026
## 0.174 0.174
##
## 0.341 0.341
## 0.026 0.026
## 0.253 0.253
##
## 0.291 0.291
## 0.026 0.026
## 0.433 0.433
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.008 0.047 0.179 0.858 -0.083 0.100
## .wx2 ~~
## .wy2 -0.017 0.044 -0.395 0.693 -0.103 0.069
## .wx3 ~~
## .wy3 0.028 0.033 0.846 0.397 -0.036 0.092
## .wx4 ~~
## .wy4 0.055 0.034 1.586 0.113 -0.013 0.122
## .wx5 ~~
## .wy5 -0.020 0.030 -0.652 0.515 -0.079 0.040
## RIx ~~
## RIy 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.008 0.008
##
## -0.024 -0.024
##
## 0.057 0.057
##
## 0.110 0.110
##
## -0.046 -0.046
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.008 0.047 -0.178 0.859 -0.100 0.084
## .LadderDif.2 0.006 0.058 0.106 0.916 -0.108 0.121
## .LadderDif.3 0.001 0.064 0.018 0.986 -0.124 0.127
## .LadderDif.4 0.022 0.067 0.330 0.741 -0.110 0.154
## .LadderDif.5 0.022 0.068 0.315 0.752 -0.112 0.156
## .dep.1 0.001 0.046 0.026 0.980 -0.089 0.091
## .dep.2 0.039 0.054 0.720 0.472 -0.067 0.145
## .dep.3 0.072 0.056 1.292 0.196 -0.037 0.181
## .dep.4 0.072 0.059 1.226 0.220 -0.043 0.187
## .dep.5 0.090 0.061 1.487 0.137 -0.029 0.209
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## Std.lv Std.all
## -0.008 -0.008
## 0.006 0.006
## 0.001 0.001
## 0.022 0.022
## 0.022 0.021
## 0.001 0.001
## 0.039 0.039
## 0.072 0.072
## 0.072 0.073
## 0.090 0.091
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## wx1 1.000 0.067 14.983 0.000 0.870 1.131
## wy1 0.992 0.065 15.213 0.000 0.864 1.119
## .wx2 0.923 0.078 11.781 0.000 0.769 1.076
## .wy2 0.559 0.049 11.457 0.000 0.463 0.654
## .wx3 0.699 0.066 10.633 0.000 0.570 0.828
## .wy3 0.338 0.032 10.465 0.000 0.274 0.401
## .wx4 0.743 0.072 10.316 0.000 0.602 0.884
## .wy4 0.331 0.032 10.292 0.000 0.268 0.394
## .wx5 0.617 0.061 10.095 0.000 0.497 0.737
## .wy5 0.301 0.030 10.053 0.000 0.242 0.360
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 0.928 0.928
## 0.551 0.551
## 0.679 0.679
## 0.339 0.339
## 0.725 0.725
## 0.339 0.339
## 0.604 0.604
## 0.306 0.306
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(LadderDepCLPM_b2AR.fit, LadderDepCLPM_b.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## LadderDepCLPM_b2AR.fit 24 6776.9 6948.2 64.28
## LadderDepCLPM_b.fit 30 6910.9 7057.1 210.22 145.94 6 < 2.2e-16
##
## LadderDepCLPM_b2AR.fit
## LadderDepCLPM_b.fit ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Adding in a third variable for CLPM models. As with the 2-variable models, adding in second-order autoregressions always improves model fit, as shown by the likelihood ratio tests.
This output includes 95% CIs, allowing for comparison of coefficients for different effects.
(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.073, p = .001
(b1) Positive emotions at time t predict less depression at time t+1, b = -.07, p < .001
c’1 path is still significant, b = .06, p = .008
a2 and b2 paths are also significant, and their CIs overlap with a1 and b1 CIs
PEmoDepCLPM <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*dep.1 + 1*dep.2 + 1*dep.3 + 1*dep.4 + 1*dep.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*dep.1
wy2 =~ 1*dep.2
wy3 =~ 1*dep.3
wy4 =~ 1*dep.4
wy5 =~ 1*dep.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2
wy4 ~ cp1*wx2 + wy3 + b1*wm3
wy5 ~ cp1*wx3 + wy4 + b1*wm4
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2
wx4 ~ wx3 + cp2*wy2 + b2*wm3
wx5 ~ wx4 + cp2*wy3 + b2*wm4
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2
wm4 ~ a1*wx3 + a2*wy3 + wm3
wm5 ~ a1*wx4 + a2*wy4 + wm4
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoDepCLPM_w.fit <- lavaan(PEmoDepCLPM, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoDepCLPM_w.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 79
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 484.846
## Degrees of freedom 72
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2940.253
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.854
## Tucker-Lewis Index (TLI) 0.788
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5392.646
## Loglikelihood unrestricted model (H1) -5150.222
##
## Akaike (AIC) 10911.291
## Bayesian (BIC) 11174.502
## Sample-size adjusted Bayesian (BIC) 10974.545
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.109
## 90 Percent confidence interval - lower 0.100
## 90 Percent confidence interval - upper 0.118
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.104
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 1.004 1.000
##
## 0.999 1.000
##
## 0.993 1.000
##
## 0.987 1.000
##
## 1.014 1.000
##
## 1.006 1.000
##
## 1.007 1.000
##
## 0.992 1.000
##
## 0.997 1.000
##
## 1.008 1.000
##
## 0.984 1.000
##
## 0.983 1.000
##
## 0.968 1.000
##
## 0.987 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.791 0.033 23.755 0.000 0.725 0.856
## wm1 (b1) -0.068 0.019 -3.524 0.000 -0.106 -0.030
## wy3 ~
## wx1 (cp1) 0.063 0.024 2.639 0.008 0.016 0.110
## wy2 0.772 0.037 21.148 0.000 0.701 0.844
## wm2 (b1) -0.068 0.019 -3.524 0.000 -0.106 -0.030
## wy4 ~
## wx2 (cp1) 0.063 0.024 2.639 0.008 0.016 0.110
## wy3 0.743 0.039 19.253 0.000 0.667 0.819
## wm3 (b1) -0.068 0.019 -3.524 0.000 -0.106 -0.030
## wy5 ~
## wx3 (cp1) 0.063 0.024 2.639 0.008 0.016 0.110
## wy4 0.793 0.037 21.323 0.000 0.720 0.866
## wm4 (b1) -0.068 0.019 -3.524 0.000 -0.106 -0.030
## wx2 ~
## wx1 0.542 0.047 11.574 0.000 0.450 0.634
## wm1 (b2) -0.063 0.027 -2.354 0.019 -0.116 -0.011
## wx3 ~
## wx2 0.507 0.053 9.564 0.000 0.403 0.610
## wy1 (cp2) 0.091 0.034 2.665 0.008 0.024 0.159
## wm2 (b2) -0.063 0.027 -2.354 0.019 -0.116 -0.011
## wx4 ~
## wx3 0.488 0.054 9.030 0.000 0.382 0.593
## wy2 (cp2) 0.091 0.034 2.665 0.008 0.024 0.159
## wm3 (b2) -0.063 0.027 -2.354 0.019 -0.116 -0.011
## wx5 ~
## wx4 0.552 0.051 10.772 0.000 0.452 0.653
## wy3 (cp2) 0.091 0.034 2.665 0.008 0.024 0.159
## wm4 (b2) -0.063 0.027 -2.354 0.019 -0.116 -0.011
## wm2 ~
## wx1 (a1) -0.073 0.023 -3.238 0.001 -0.117 -0.029
## wy1 (a2) -0.141 0.024 -5.780 0.000 -0.189 -0.093
## wm1 0.541 0.041 13.187 0.000 0.460 0.621
## wm3 ~
## wx2 (a1) -0.073 0.023 -3.238 0.001 -0.117 -0.029
## wy2 (a2) -0.141 0.024 -5.780 0.000 -0.189 -0.093
## wm2 0.589 0.044 13.481 0.000 0.503 0.674
## wm4 ~
## wx3 (a1) -0.073 0.023 -3.238 0.001 -0.117 -0.029
## wy3 (a2) -0.141 0.024 -5.780 0.000 -0.189 -0.093
## wm3 0.636 0.042 15.044 0.000 0.553 0.719
## wm5 ~
## wx4 (a1) -0.073 0.023 -3.238 0.001 -0.117 -0.029
## wy4 (a2) -0.141 0.024 -5.780 0.000 -0.189 -0.093
## wm4 0.648 0.046 14.212 0.000 0.558 0.737
## Std.lv Std.all
##
## 0.797 0.797
## -0.068 -0.068
##
## 0.062 0.062
## 0.772 0.772
## -0.066 -0.066
##
## 0.064 0.064
## 0.754 0.754
## -0.067 -0.067
##
## 0.063 0.063
## 0.789 0.789
## -0.066 -0.066
##
## 0.540 0.540
## -0.064 -0.064
##
## 0.509 0.509
## 0.093 0.093
## -0.062 -0.062
##
## 0.491 0.491
## 0.093 0.093
## -0.063 -0.063
##
## 0.555 0.555
## 0.093 0.093
## -0.062 -0.062
##
## -0.074 -0.074
## -0.146 -0.146
## 0.554 0.554
##
## -0.074 -0.074
## -0.144 -0.144
## 0.589 0.589
##
## -0.075 -0.075
## -0.147 -0.147
## 0.646 0.646
##
## -0.073 -0.073
## -0.142 -0.142
## 0.635 0.635
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.311 0.050 6.172 0.000 0.213 0.410
## wm1 -0.255 0.049 -5.157 0.000 -0.352 -0.158
## wy1 ~~
## wm1 -0.347 0.051 -6.754 0.000 -0.447 -0.246
## .wx2 ~~
## .wy2 -0.022 0.027 -0.827 0.408 -0.075 0.031
## .wx3 ~~
## .wy3 -0.052 0.030 -1.753 0.080 -0.111 0.006
## .wx4 ~~
## .wy4 0.025 0.033 0.753 0.451 -0.039 0.089
## .wx5 ~~
## .wy5 0.020 0.030 0.648 0.517 -0.040 0.079
## .wx2 ~~
## .wm2 -0.032 0.035 -0.925 0.355 -0.100 0.036
## .wx3 ~~
## .wm3 0.032 0.036 0.884 0.376 -0.039 0.103
## .wx4 ~~
## .wm4 0.013 0.035 0.363 0.716 -0.056 0.081
## .wx5 ~~
## .wm5 -0.001 0.035 -0.017 0.987 -0.070 0.068
## .wy2 ~~
## .wm2 -0.045 0.025 -1.825 0.068 -0.093 0.003
## .wy3 ~~
## .wm3 -0.090 0.026 -3.492 0.000 -0.141 -0.040
## .wy4 ~~
## .wm4 0.032 0.025 1.286 0.199 -0.017 0.081
## .wy5 ~~
## .wm5 -0.054 0.025 -2.155 0.031 -0.104 -0.005
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.307 0.307
## -0.254 -0.254
##
## -0.339 -0.339
##
## -0.047 -0.047
##
## -0.109 -0.109
##
## 0.049 0.049
##
## 0.044 0.044
##
## -0.051 -0.051
##
## 0.053 0.053
##
## 0.023 0.023
##
## -0.001 -0.001
##
## -0.104 -0.104
##
## -0.216 -0.216
##
## 0.082 0.082
##
## -0.140 -0.140
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.002 0.047 0.045 0.964 -0.090 0.094
## .LadderDif.2 -0.011 0.052 -0.207 0.836 -0.112 0.090
## .LadderDif.3 -0.006 0.057 -0.101 0.919 -0.118 0.106
## .LadderDif.4 0.012 0.061 0.191 0.849 -0.108 0.131
## .LadderDif.5 0.009 0.062 0.143 0.887 -0.113 0.131
## .dep.1 0.008 0.047 0.176 0.861 -0.084 0.100
## .dep.2 0.047 0.049 0.955 0.340 -0.049 0.142
## .dep.3 0.063 0.053 1.196 0.232 -0.040 0.167
## .dep.4 0.053 0.056 0.948 0.343 -0.057 0.163
## .dep.5 0.040 0.059 0.685 0.493 -0.075 0.155
## .posEmo.1 -0.004 0.047 -0.079 0.937 -0.096 0.089
## .posEmo.2 -0.017 0.050 -0.341 0.733 -0.114 0.080
## .posEmo.3 -0.018 0.055 -0.335 0.737 -0.126 0.089
## .posEmo.4 -0.026 0.057 -0.450 0.652 -0.137 0.086
## .posEmo.5 -0.012 0.060 -0.196 0.844 -0.129 0.106
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.002 0.002
## -0.011 -0.011
## -0.006 -0.006
## 0.012 0.012
## 0.009 0.009
## 0.008 0.008
## 0.047 0.046
## 0.063 0.063
## 0.053 0.054
## 0.040 0.040
## -0.004 -0.004
## -0.017 -0.017
## -0.018 -0.019
## -0.026 -0.026
## -0.012 -0.012
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.998 0.067 14.883 0.000 0.867 1.130
## wy1 1.028 0.069 14.962 0.000 0.893 1.162
## wm1 1.015 0.069 14.758 0.000 0.880 1.150
## .wx2 0.693 0.054 12.914 0.000 0.588 0.798
## .wy2 0.328 0.026 12.429 0.000 0.276 0.380
## .wm2 0.566 0.045 12.701 0.000 0.478 0.653
## .wx3 0.691 0.059 11.770 0.000 0.576 0.806
## .wy3 0.335 0.029 11.725 0.000 0.279 0.391
## .wm3 0.524 0.045 11.766 0.000 0.437 0.611
## .wx4 0.707 0.063 11.182 0.000 0.583 0.831
## .wy4 0.356 0.032 11.123 0.000 0.293 0.419
## .wm4 0.427 0.038 11.187 0.000 0.352 0.502
## .wx5 0.630 0.057 10.997 0.000 0.518 0.742
## .wy5 0.315 0.029 10.985 0.000 0.259 0.371
## .wm5 0.475 0.043 10.991 0.000 0.390 0.560
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.687 0.687
## 0.324 0.324
## 0.585 0.585
## 0.693 0.693
## 0.330 0.330
## 0.542 0.542
## 0.717 0.717
## 0.362 0.362
## 0.455 0.455
## 0.646 0.646
## 0.317 0.317
## 0.488 0.488
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b1) Positive emotions at time t predict less depression at time t+1, b = -.10, p < .001
# Same model as above code, but fit with d_black dataset this time
PEmoDepCLPM_b.fit <- lavaan(PEmoDepCLPM, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoDepCLPM_b.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 54 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 79
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 379.992
## Degrees of freedom 72
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2054.729
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.842
## Tucker-Lewis Index (TLI) 0.770
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5032.360
## Loglikelihood unrestricted model (H1) -4842.364
##
## Akaike (AIC) 10190.720
## Bayesian (BIC) 10453.931
## Sample-size adjusted Bayesian (BIC) 10253.974
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.094
## 90 Percent confidence interval - lower 0.085
## 90 Percent confidence interval - upper 0.104
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.099
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.998 1.000
##
## 1.009 1.000
##
## 1.008 1.000
##
## 0.996 1.000
##
## 0.998 1.000
##
## 1.004 1.000
##
## 0.994 1.000
##
## 0.977 1.000
##
## 0.987 1.000
##
## 0.997 1.000
##
## 0.992 1.000
##
## 0.977 1.000
##
## 0.984 1.000
##
## 0.998 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.657 0.046 14.198 0.000 0.566 0.747
## wm1 (b1) -0.101 0.023 -4.437 0.000 -0.145 -0.056
## wy3 ~
## wx1 (cp1) 0.037 0.026 1.410 0.158 -0.014 0.088
## wy2 0.741 0.042 17.655 0.000 0.658 0.823
## wm2 (b1) -0.101 0.023 -4.437 0.000 -0.145 -0.056
## wy4 ~
## wx2 (cp1) 0.037 0.026 1.410 0.158 -0.014 0.088
## wy3 0.743 0.041 18.304 0.000 0.664 0.823
## wm3 (b1) -0.101 0.023 -4.437 0.000 -0.145 -0.056
## wy5 ~
## wx3 (cp1) 0.037 0.026 1.410 0.158 -0.014 0.088
## wy4 0.758 0.043 17.804 0.000 0.674 0.841
## wm4 (b1) -0.101 0.023 -4.437 0.000 -0.145 -0.056
## wx2 ~
## wx1 0.253 0.061 4.123 0.000 0.133 0.373
## wm1 (b2) -0.026 0.031 -0.816 0.414 -0.087 0.036
## wx3 ~
## wx2 0.542 0.059 9.179 0.000 0.426 0.658
## wy1 (cp2) -0.002 0.038 -0.052 0.959 -0.076 0.072
## wm2 (b2) -0.026 0.031 -0.816 0.414 -0.087 0.036
## wx4 ~
## wx3 0.468 0.063 7.458 0.000 0.345 0.591
## wy2 (cp2) -0.002 0.038 -0.052 0.959 -0.076 0.072
## wm3 (b2) -0.026 0.031 -0.816 0.414 -0.087 0.036
## wx5 ~
## wx4 0.483 0.060 8.023 0.000 0.365 0.601
## wy3 (cp2) -0.002 0.038 -0.052 0.959 -0.076 0.072
## wm4 (b2) -0.026 0.031 -0.816 0.414 -0.087 0.036
## wm2 ~
## wx1 (a1) -0.030 0.024 -1.277 0.202 -0.077 0.016
## wy1 (a2) -0.133 0.025 -5.288 0.000 -0.182 -0.084
## wm1 0.507 0.051 9.977 0.000 0.407 0.606
## wm3 ~
## wx2 (a1) -0.030 0.024 -1.277 0.202 -0.077 0.016
## wy2 (a2) -0.133 0.025 -5.288 0.000 -0.182 -0.084
## wm2 0.673 0.044 15.165 0.000 0.586 0.760
## wm4 ~
## wx3 (a1) -0.030 0.024 -1.277 0.202 -0.077 0.016
## wy3 (a2) -0.133 0.025 -5.288 0.000 -0.182 -0.084
## wm3 0.693 0.046 15.132 0.000 0.603 0.783
## wm5 ~
## wx4 (a1) -0.030 0.024 -1.277 0.202 -0.077 0.016
## wy4 (a2) -0.133 0.025 -5.288 0.000 -0.182 -0.084
## wm4 0.695 0.048 14.441 0.000 0.600 0.789
## Std.lv Std.all
##
## 0.653 0.653
## -0.100 -0.100
##
## 0.037 0.037
## 0.748 0.748
## -0.101 -0.101
##
## 0.038 0.038
## 0.756 0.756
## -0.101 -0.101
##
## 0.038 0.038
## 0.750 0.750
## -0.100 -0.100
##
## 0.253 0.253
## -0.025 -0.025
##
## 0.536 0.536
## -0.002 -0.002
## -0.025 -0.025
##
## 0.468 0.468
## -0.002 -0.002
## -0.025 -0.025
##
## 0.489 0.489
## -0.002 -0.002
## -0.025 -0.025
##
## -0.030 -0.030
## -0.134 -0.134
## 0.509 0.509
##
## -0.031 -0.031
## -0.136 -0.136
## 0.684 0.684
##
## -0.031 -0.031
## -0.134 -0.134
## 0.688 0.688
##
## -0.031 -0.031
## -0.130 -0.130
## 0.684 0.684
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.009 0.047 0.183 0.855 -0.083 0.100
## wm1 -0.007 0.047 -0.159 0.874 -0.099 0.084
## wy1 ~~
## wm1 -0.226 0.048 -4.748 0.000 -0.319 -0.133
## .wx2 ~~
## .wy2 -0.020 0.044 -0.448 0.654 -0.105 0.066
## .wx3 ~~
## .wy3 0.016 0.036 0.456 0.649 -0.054 0.087
## .wx4 ~~
## .wy4 0.063 0.037 1.713 0.087 -0.009 0.134
## .wx5 ~~
## .wy5 -0.012 0.038 -0.323 0.747 -0.086 0.061
## .wx2 ~~
## .wm2 0.001 0.048 0.012 0.990 -0.094 0.095
## .wx3 ~~
## .wm3 -0.029 0.038 -0.766 0.444 -0.103 0.045
## .wx4 ~~
## .wm4 0.010 0.040 0.246 0.805 -0.068 0.088
## .wx5 ~~
## .wm5 0.024 0.040 0.591 0.555 -0.055 0.103
## .wy2 ~~
## .wm2 0.005 0.037 0.140 0.889 -0.068 0.078
## .wy3 ~~
## .wm3 -0.066 0.028 -2.360 0.018 -0.122 -0.011
## .wy4 ~~
## .wm4 -0.045 0.027 -1.687 0.092 -0.097 0.007
## .wy5 ~~
## .wm5 -0.061 0.028 -2.161 0.031 -0.116 -0.006
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.009 0.009
## -0.007 -0.007
##
## -0.227 -0.227
##
## -0.028 -0.028
##
## 0.031 0.031
##
## 0.120 0.120
##
## -0.024 -0.024
##
## 0.001 0.001
##
## -0.051 -0.051
##
## 0.017 0.017
##
## 0.042 0.042
##
## 0.009 0.009
##
## -0.158 -0.158
##
## -0.117 -0.117
##
## -0.155 -0.155
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.007 0.047 -0.156 0.876 -0.099 0.085
## .LadderDif.2 0.005 0.059 0.078 0.938 -0.110 0.119
## .LadderDif.3 0.002 0.065 0.036 0.972 -0.125 0.129
## .LadderDif.4 0.019 0.068 0.285 0.776 -0.114 0.153
## .LadderDif.5 0.008 0.069 0.115 0.909 -0.128 0.144
## .dep.1 0.003 0.046 0.058 0.954 -0.088 0.093
## .dep.2 0.038 0.054 0.708 0.479 -0.067 0.144
## .dep.3 0.051 0.058 0.878 0.380 -0.063 0.166
## .dep.4 0.049 0.061 0.810 0.418 -0.070 0.169
## .dep.5 0.053 0.064 0.832 0.405 -0.072 0.179
## .posEmo.1 -0.001 0.046 -0.019 0.985 -0.092 0.090
## .posEmo.2 -0.014 0.055 -0.254 0.800 -0.122 0.094
## .posEmo.3 -0.017 0.059 -0.288 0.773 -0.133 0.099
## .posEmo.4 -0.025 0.063 -0.393 0.695 -0.148 0.098
## .posEmo.5 -0.030 0.066 -0.450 0.653 -0.159 0.100
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.007 -0.007
## 0.005 0.005
## 0.002 0.002
## 0.019 0.019
## 0.008 0.008
## 0.003 0.003
## 0.038 0.038
## 0.051 0.052
## 0.049 0.050
## 0.053 0.054
## -0.001 -0.001
## -0.014 -0.014
## -0.017 -0.017
## -0.025 -0.025
## -0.030 -0.030
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.997 0.066 15.025 0.000 0.867 1.127
## wy1 0.996 0.066 15.151 0.000 0.867 1.124
## wm1 0.994 0.066 15.118 0.000 0.865 1.123
## .wx2 0.932 0.079 11.803 0.000 0.777 1.086
## .wy2 0.538 0.047 11.415 0.000 0.446 0.631
## .wm2 0.680 0.059 11.573 0.000 0.565 0.796
## .wx3 0.725 0.068 10.683 0.000 0.592 0.858
## .wy3 0.392 0.037 10.691 0.000 0.320 0.464
## .wm3 0.451 0.042 10.697 0.000 0.368 0.534
## .wx4 0.791 0.077 10.327 0.000 0.641 0.941
## .wy4 0.346 0.034 10.298 0.000 0.280 0.411
## .wm4 0.425 0.041 10.290 0.000 0.344 0.506
## .wx5 0.753 0.075 10.067 0.000 0.606 0.900
## .wy5 0.352 0.035 10.045 0.000 0.283 0.421
## .wm5 0.437 0.043 10.082 0.000 0.352 0.522
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.935 0.935
## 0.534 0.534
## 0.691 0.691
## 0.711 0.711
## 0.397 0.397
## 0.473 0.473
## 0.778 0.778
## 0.362 0.362
## 0.439 0.439
## 0.759 0.759
## 0.361 0.361
## 0.439 0.439
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.07, p = .002
(b1) Positive emotions at time t predict less depression at time t+1, b = -.037, p = .047
C’ path is still significant, b = .04, p = .04
a2 path is also significant, and its CIs overlap with a1 CIs
PEmoDepCLPM_2AR <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*dep.1 + 1*dep.2 + 1*dep.3 + 1*dep.4 + 1*dep.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*dep.1
wy2 =~ 1*dep.2
wy3 =~ 1*dep.3
wy4 =~ 1*dep.4
wy5 =~ 1*dep.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoDepCLPM_w2AR.fit <- lavaan(PEmoDepCLPM_2AR, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoDepCLPM_w2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 163.664
## Degrees of freedom 63
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2940.253
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.964
## Tucker-Lewis Index (TLI) 0.941
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5232.055
## Loglikelihood unrestricted model (H1) -5150.222
##
## Akaike (AIC) 10608.109
## Bayesian (BIC) 10908.921
## Sample-size adjusted Bayesian (BIC) 10680.400
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.068
## P-value RMSEA <= 0.05 0.119
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.049
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.001 1.000
##
## 1.001 1.000
##
## 0.995 1.000
##
## 1.001 1.000
##
## 0.988 1.000
##
## 1.018 1.000
##
## 1.004 1.000
##
## 1.003 1.000
##
## 0.990 1.000
##
## 1.001 1.000
##
## 1.006 1.000
##
## 0.978 1.000
##
## 0.973 1.000
##
## 0.961 1.000
##
## 0.989 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.798 0.033 24.153 0.000 0.734 0.863
## wm1 (b1) -0.037 0.019 -1.989 0.047 -0.074 -0.001
## wy3 ~
## wx1 (cp1) 0.043 0.021 2.057 0.040 0.002 0.084
## wy2 0.520 0.062 8.378 0.000 0.399 0.642
## wm2 (b1) -0.037 0.019 -1.989 0.047 -0.074 -0.001
## wy1 0.319 0.062 5.152 0.000 0.197 0.440
## wy4 ~
## wx2 (cp1) 0.043 0.021 2.057 0.040 0.002 0.084
## wy3 0.509 0.063 8.118 0.000 0.386 0.632
## wm3 (b1) -0.037 0.019 -1.989 0.047 -0.074 -0.001
## wy2 0.315 0.064 4.952 0.000 0.190 0.439
## wy5 ~
## wx3 (cp1) 0.043 0.021 2.057 0.040 0.002 0.084
## wy4 0.489 0.053 9.197 0.000 0.384 0.593
## wm4 (b1) -0.037 0.019 -1.989 0.047 -0.074 -0.001
## wy3 0.402 0.053 7.585 0.000 0.298 0.506
## wx2 ~
## wx1 0.548 0.046 11.792 0.000 0.457 0.639
## wm1 (b2) -0.036 0.025 -1.413 0.158 -0.086 0.014
## wx3 ~
## wx2 0.283 0.059 4.832 0.000 0.168 0.398
## wy1 (cp2) 0.045 0.031 1.447 0.148 -0.016 0.105
## wm2 (b2) -0.036 0.025 -1.413 0.158 -0.086 0.014
## wx1 0.414 0.058 7.197 0.000 0.301 0.527
## wx4 ~
## wx3 0.310 0.060 5.177 0.000 0.193 0.428
## wy2 (cp2) 0.045 0.031 1.447 0.148 -0.016 0.105
## wm3 (b2) -0.036 0.025 -1.413 0.158 -0.086 0.014
## wx2 0.374 0.063 5.984 0.000 0.252 0.497
## wx5 ~
## wx4 0.323 0.053 6.052 0.000 0.218 0.428
## wy3 (cp2) 0.045 0.031 1.447 0.148 -0.016 0.105
## wm4 (b2) -0.036 0.025 -1.413 0.158 -0.086 0.014
## wx3 0.447 0.053 8.375 0.000 0.343 0.552
## wm2 ~
## wx1 (a1) -0.068 0.022 -3.154 0.002 -0.110 -0.026
## wy1 (a2) -0.109 0.024 -4.586 0.000 -0.155 -0.062
## wm1 0.546 0.041 13.206 0.000 0.465 0.627
## wm3 ~
## wx2 (a1) -0.068 0.022 -3.154 0.002 -0.110 -0.026
## wy2 (a2) -0.109 0.024 -4.586 0.000 -0.155 -0.062
## wm2 0.486 0.056 8.672 0.000 0.376 0.596
## wm1 0.180 0.058 3.085 0.002 0.065 0.294
## wm4 ~
## wx3 (a1) -0.068 0.022 -3.154 0.002 -0.110 -0.026
## wy3 (a2) -0.109 0.024 -4.586 0.000 -0.155 -0.062
## wm3 0.492 0.054 9.082 0.000 0.386 0.598
## wm2 0.237 0.055 4.340 0.000 0.130 0.344
## wm5 ~
## wx4 (a1) -0.068 0.022 -3.154 0.002 -0.110 -0.026
## wy4 (a2) -0.109 0.024 -4.586 0.000 -0.155 -0.062
## wm4 0.333 0.057 5.849 0.000 0.222 0.445
## wm3 0.461 0.056 8.216 0.000 0.351 0.571
## Std.lv Std.all
##
## 0.810 0.810
## -0.037 -0.037
##
## 0.043 0.043
## 0.521 0.521
## -0.036 -0.036
## 0.324 0.324
##
## 0.043 0.043
## 0.516 0.516
## -0.036 -0.036
## 0.319 0.319
##
## 0.043 0.043
## 0.483 0.483
## -0.036 -0.036
## 0.403 0.403
##
## 0.547 0.547
## -0.036 -0.036
##
## 0.285 0.285
## 0.046 0.046
## -0.035 -0.035
## 0.417 0.417
##
## 0.308 0.308
## 0.045 0.045
## -0.035 -0.035
## 0.375 0.375
##
## 0.327 0.327
## 0.045 0.045
## -0.035 -0.035
## 0.451 0.451
##
## -0.070 -0.070
## -0.113 -0.113
## 0.562 0.562
##
## -0.070 -0.070
## -0.112 -0.112
## 0.489 0.489
## 0.186 0.186
##
## -0.071 -0.071
## -0.113 -0.113
## 0.498 0.498
## 0.241 0.241
##
## -0.069 -0.069
## -0.109 -0.109
## 0.324 0.324
## 0.454 0.454
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.314 0.051 6.204 0.000 0.215 0.414
## wm1 -0.257 0.049 -5.200 0.000 -0.354 -0.160
## wy1 ~~
## wm1 -0.349 0.052 -6.770 0.000 -0.450 -0.248
## .wx2 ~~
## .wy2 -0.023 0.027 -0.872 0.383 -0.076 0.029
## .wx3 ~~
## .wy3 -0.029 0.026 -1.132 0.258 -0.079 0.021
## .wx4 ~~
## .wy4 0.009 0.028 0.329 0.742 -0.046 0.065
## .wx5 ~~
## .wy5 0.043 0.023 1.864 0.062 -0.002 0.089
## .wx2 ~~
## .wm2 -0.036 0.035 -1.033 0.302 -0.104 0.032
## .wx3 ~~
## .wm3 0.003 0.033 0.097 0.923 -0.061 0.068
## .wx4 ~~
## .wm4 0.018 0.032 0.557 0.578 -0.044 0.079
## .wx5 ~~
## .wm5 0.002 0.028 0.077 0.938 -0.052 0.056
## .wy2 ~~
## .wm2 -0.046 0.025 -1.845 0.065 -0.094 0.003
## .wy3 ~~
## .wm3 -0.077 0.024 -3.174 0.002 -0.125 -0.030
## .wy4 ~~
## .wm4 0.031 0.023 1.346 0.178 -0.014 0.075
## .wy5 ~~
## .wm5 -0.048 0.020 -2.402 0.016 -0.088 -0.009
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.308 0.308
## -0.255 -0.255
##
## -0.341 -0.341
##
## -0.049 -0.049
##
## -0.070 -0.070
##
## 0.021 0.021
##
## 0.122 0.122
##
## -0.057 -0.057
##
## 0.006 0.006
##
## 0.035 0.035
##
## 0.005 0.005
##
## -0.106 -0.106
##
## -0.198 -0.198
##
## 0.086 0.086
##
## -0.157 -0.157
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.003 0.047 0.069 0.945 -0.089 0.095
## .LadderDif.2 -0.011 0.051 -0.221 0.825 -0.112 0.089
## .LadderDif.3 -0.029 0.055 -0.533 0.594 -0.137 0.078
## .LadderDif.4 -0.008 0.059 -0.127 0.899 -0.123 0.108
## .LadderDif.5 -0.012 0.059 -0.202 0.840 -0.128 0.104
## .dep.1 0.007 0.047 0.152 0.879 -0.085 0.099
## .dep.2 0.047 0.049 0.960 0.337 -0.049 0.142
## .dep.3 0.069 0.051 1.351 0.177 -0.031 0.170
## .dep.4 0.063 0.054 1.166 0.243 -0.043 0.168
## .dep.5 0.057 0.055 1.037 0.300 -0.051 0.165
## .posEmo.1 -0.006 0.047 -0.134 0.893 -0.099 0.086
## .posEmo.2 -0.015 0.050 -0.298 0.766 -0.112 0.082
## .posEmo.3 -0.009 0.054 -0.172 0.863 -0.114 0.096
## .posEmo.4 -0.017 0.055 -0.305 0.760 -0.124 0.091
## .posEmo.5 -0.004 0.058 -0.073 0.942 -0.117 0.109
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.003 0.003
## -0.011 -0.011
## -0.029 -0.029
## -0.008 -0.008
## -0.012 -0.012
## 0.007 0.007
## 0.047 0.046
## 0.069 0.069
## 0.063 0.063
## 0.057 0.057
## -0.006 -0.006
## -0.015 -0.015
## -0.009 -0.009
## -0.017 -0.017
## -0.004 -0.004
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 1.002 0.067 14.901 0.000 0.870 1.134
## wy1 1.037 0.069 14.957 0.000 0.901 1.173
## wm1 1.011 0.068 14.796 0.000 0.877 1.145
## .wx2 0.691 0.053 12.944 0.000 0.586 0.795
## .wy2 0.325 0.026 12.507 0.000 0.274 0.376
## .wm2 0.573 0.045 12.634 0.000 0.484 0.662
## .wx3 0.575 0.050 11.528 0.000 0.477 0.673
## .wy3 0.304 0.026 11.538 0.000 0.252 0.355
## .wm3 0.500 0.043 11.678 0.000 0.416 0.584
## .wx4 0.619 0.055 11.179 0.000 0.510 0.727
## .wy4 0.322 0.029 11.155 0.000 0.265 0.378
## .wm4 0.399 0.036 11.197 0.000 0.329 0.469
## .wx5 0.495 0.045 10.997 0.000 0.406 0.583
## .wy5 0.255 0.023 10.984 0.000 0.209 0.300
## .wm5 0.373 0.034 10.964 0.000 0.307 0.440
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.689 0.689
## 0.323 0.323
## 0.599 0.599
## 0.581 0.581
## 0.302 0.302
## 0.528 0.528
## 0.618 0.618
## 0.328 0.328
## 0.432 0.432
## 0.507 0.507
## 0.254 0.254
## 0.382 0.382
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(PEmoDepCLPM_w2AR.fit, PEmoDepCLPM_w.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PEmoDepCLPM_w2AR.fit 63 10608 10909 163.66
## PEmoDepCLPM_w.fit 72 10911 11174 484.85 321.18 9 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b1) Positive emotions at time t predict less depression at time t+1, b = -.09, p < .001
# Same model as above code, but fit with d_black dataset this time
PEmoDepCLPM_b2AR.fit <- lavaan(PEmoDepCLPM_2AR, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoDepCLPM_b2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 154.995
## Degrees of freedom 63
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2054.729
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.953
## Tucker-Lewis Index (TLI) 0.921
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4919.862
## Loglikelihood unrestricted model (H1) -4842.364
##
## Akaike (AIC) 9983.723
## Bayesian (BIC) 10284.535
## Sample-size adjusted Bayesian (BIC) 10056.013
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.055
## 90 Percent confidence interval - lower 0.044
## 90 Percent confidence interval - upper 0.066
## P-value RMSEA <= 0.05 0.214
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.048
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 0.998 1.000
##
## 1.014 1.000
##
## 1.015 1.000
##
## 1.016 1.000
##
## 0.996 1.000
##
## 1.004 1.000
##
## 0.989 1.000
##
## 0.974 1.000
##
## 0.985 1.000
##
## 0.996 1.000
##
## 0.990 1.000
##
## 0.971 1.000
##
## 0.970 1.000
##
## 0.978 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.659 0.047 14.101 0.000 0.567 0.750
## wm1 (b1) -0.087 0.022 -3.984 0.000 -0.129 -0.044
## wy3 ~
## wx1 (cp1) 0.037 0.024 1.587 0.112 -0.009 0.084
## wy2 0.483 0.055 8.837 0.000 0.376 0.590
## wm2 (b1) -0.087 0.022 -3.984 0.000 -0.129 -0.044
## wy1 0.371 0.056 6.653 0.000 0.262 0.480
## wy4 ~
## wx2 (cp1) 0.037 0.024 1.587 0.112 -0.009 0.084
## wy3 0.556 0.061 9.079 0.000 0.436 0.677
## wm3 (b1) -0.087 0.022 -3.984 0.000 -0.129 -0.044
## wy2 0.249 0.061 4.056 0.000 0.129 0.370
## wy5 ~
## wx3 (cp1) 0.037 0.024 1.587 0.112 -0.009 0.084
## wy4 0.472 0.068 6.904 0.000 0.338 0.607
## wm4 (b1) -0.087 0.022 -3.984 0.000 -0.129 -0.044
## wy3 0.364 0.069 5.296 0.000 0.229 0.499
## wx2 ~
## wx1 0.263 0.061 4.323 0.000 0.144 0.382
## wm1 (b2) -0.010 0.030 -0.336 0.737 -0.069 0.049
## wx3 ~
## wx2 0.505 0.060 8.382 0.000 0.387 0.623
## wy1 (cp2) -0.001 0.035 -0.035 0.972 -0.070 0.067
## wm2 (b2) -0.010 0.030 -0.336 0.737 -0.069 0.049
## wx1 0.175 0.062 2.801 0.005 0.052 0.297
## wx4 ~
## wx3 0.337 0.070 4.819 0.000 0.200 0.475
## wy2 (cp2) -0.001 0.035 -0.035 0.972 -0.070 0.067
## wm3 (b2) -0.010 0.030 -0.336 0.737 -0.069 0.049
## wx2 0.268 0.072 3.736 0.000 0.127 0.408
## wx5 ~
## wx4 0.290 0.062 4.706 0.000 0.169 0.411
## wy3 (cp2) -0.001 0.035 -0.035 0.972 -0.070 0.067
## wm4 (b2) -0.010 0.030 -0.336 0.737 -0.069 0.049
## wx3 0.440 0.065 6.778 0.000 0.313 0.567
## wm2 ~
## wx1 (a1) -0.029 0.023 -1.291 0.197 -0.073 0.015
## wy1 (a2) -0.101 0.024 -4.155 0.000 -0.148 -0.053
## wm1 0.510 0.051 9.970 0.000 0.410 0.611
## wm3 ~
## wx2 (a1) -0.029 0.023 -1.291 0.197 -0.073 0.015
## wy2 (a2) -0.101 0.024 -4.155 0.000 -0.148 -0.053
## wm2 0.547 0.051 10.649 0.000 0.446 0.648
## wm1 0.237 0.052 4.563 0.000 0.135 0.340
## wm4 ~
## wx3 (a1) -0.029 0.023 -1.291 0.197 -0.073 0.015
## wy3 (a2) -0.101 0.024 -4.155 0.000 -0.148 -0.053
## wm3 0.478 0.064 7.446 0.000 0.352 0.604
## wm2 0.300 0.063 4.799 0.000 0.178 0.423
## wm5 ~
## wx4 (a1) -0.029 0.023 -1.291 0.197 -0.073 0.015
## wy4 (a2) -0.101 0.024 -4.155 0.000 -0.148 -0.053
## wm4 0.363 0.064 5.636 0.000 0.237 0.490
## wm3 0.444 0.063 7.014 0.000 0.320 0.568
## Std.lv Std.all
##
## 0.654 0.654
## -0.086 -0.086
##
## 0.038 0.038
## 0.490 0.490
## -0.087 -0.087
## 0.373 0.373
##
## 0.038 0.038
## 0.565 0.565
## -0.087 -0.087
## 0.257 0.257
##
## 0.038 0.038
## 0.467 0.467
## -0.085 -0.085
## 0.365 0.365
##
## 0.263 0.263
## -0.010 -0.010
##
## 0.497 0.497
## -0.001 -0.001
## -0.010 -0.010
## 0.172 0.172
##
## 0.337 0.337
## -0.001 -0.001
## -0.010 -0.010
## 0.264 0.264
##
## 0.290 0.290
## -0.001 -0.001
## -0.010 -0.010
## 0.439 0.439
##
## -0.029 -0.029
## -0.101 -0.101
## 0.514 0.514
##
## -0.030 -0.030
## -0.104 -0.104
## 0.558 0.558
## 0.244 0.244
##
## -0.030 -0.030
## -0.103 -0.103
## 0.478 0.478
## 0.306 0.306
##
## -0.030 -0.030
## -0.100 -0.100
## 0.360 0.360
## 0.441 0.441
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.008 0.047 0.166 0.868 -0.084 0.099
## wm1 -0.007 0.047 -0.151 0.880 -0.099 0.085
## wy1 ~~
## wm1 -0.228 0.047 -4.820 0.000 -0.321 -0.136
## .wx2 ~~
## .wy2 -0.016 0.043 -0.378 0.705 -0.102 0.069
## .wx3 ~~
## .wy3 0.027 0.032 0.837 0.403 -0.036 0.090
## .wx4 ~~
## .wy4 0.054 0.034 1.602 0.109 -0.012 0.120
## .wx5 ~~
## .wy5 -0.019 0.031 -0.615 0.539 -0.079 0.041
## .wx2 ~~
## .wm2 -0.003 0.048 -0.069 0.945 -0.098 0.091
## .wx3 ~~
## .wm3 -0.013 0.036 -0.367 0.714 -0.083 0.057
## .wx4 ~~
## .wm4 0.015 0.037 0.405 0.686 -0.057 0.086
## .wx5 ~~
## .wm5 -0.037 0.034 -1.115 0.265 -0.103 0.028
## .wy2 ~~
## .wm2 0.007 0.038 0.181 0.856 -0.067 0.081
## .wy3 ~~
## .wm3 -0.073 0.025 -2.899 0.004 -0.122 -0.024
## .wy4 ~~
## .wm4 -0.035 0.024 -1.451 0.147 -0.083 0.012
## .wy5 ~~
## .wm5 -0.034 0.024 -1.414 0.157 -0.080 0.013
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.008 0.008
## -0.007 -0.007
##
## -0.230 -0.230
##
## -0.023 -0.023
##
## 0.056 0.056
##
## 0.111 0.111
##
## -0.043 -0.043
##
## -0.004 -0.004
##
## -0.024 -0.024
##
## 0.028 0.028
##
## -0.080 -0.080
##
## 0.011 0.011
##
## -0.199 -0.199
##
## -0.101 -0.101
##
## -0.102 -0.102
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.008 0.047 -0.179 0.858 -0.100 0.084
## .LadderDif.2 0.005 0.059 0.081 0.936 -0.110 0.119
## .LadderDif.3 -0.001 0.064 -0.021 0.983 -0.127 0.124
## .LadderDif.4 0.019 0.067 0.280 0.779 -0.113 0.151
## .LadderDif.5 0.018 0.069 0.257 0.797 -0.117 0.152
## .dep.1 0.002 0.046 0.035 0.972 -0.089 0.092
## .dep.2 0.038 0.054 0.713 0.476 -0.067 0.144
## .dep.3 0.070 0.055 1.266 0.205 -0.038 0.177
## .dep.4 0.069 0.058 1.195 0.232 -0.044 0.182
## .dep.5 0.087 0.060 1.447 0.148 -0.031 0.205
## .posEmo.1 -0.002 0.046 -0.053 0.957 -0.093 0.088
## .posEmo.2 -0.012 0.055 -0.213 0.831 -0.120 0.097
## .posEmo.3 -0.019 0.057 -0.329 0.742 -0.130 0.093
## .posEmo.4 -0.028 0.060 -0.463 0.643 -0.144 0.089
## .posEmo.5 -0.036 0.062 -0.591 0.554 -0.157 0.084
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.008 -0.008
## 0.005 0.005
## -0.001 -0.001
## 0.019 0.019
## 0.018 0.017
## 0.002 0.002
## 0.038 0.038
## 0.070 0.070
## 0.069 0.071
## 0.087 0.088
## -0.002 -0.002
## -0.012 -0.012
## -0.019 -0.019
## -0.028 -0.028
## -0.036 -0.037
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.999 0.067 14.998 0.000 0.869 1.130
## wy1 0.991 0.065 15.221 0.000 0.864 1.119
## wm1 0.992 0.066 15.144 0.000 0.864 1.121
## .wx2 0.928 0.079 11.801 0.000 0.774 1.082
## .wy2 0.543 0.048 11.431 0.000 0.450 0.637
## .wm2 0.687 0.059 11.562 0.000 0.571 0.804
## .wx3 0.698 0.066 10.637 0.000 0.569 0.826
## .wy3 0.327 0.031 10.482 0.000 0.266 0.388
## .wm3 0.413 0.039 10.567 0.000 0.336 0.489
## .wx4 0.742 0.072 10.322 0.000 0.601 0.882
## .wy4 0.320 0.031 10.290 0.000 0.259 0.381
## .wm4 0.381 0.037 10.290 0.000 0.309 0.454
## .wx5 0.619 0.061 10.071 0.000 0.499 0.740
## .wy5 0.305 0.030 10.030 0.000 0.246 0.365
## .wm5 0.356 0.035 10.095 0.000 0.287 0.425
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.931 0.931
## 0.539 0.539
## 0.701 0.701
## 0.678 0.678
## 0.334 0.334
## 0.438 0.438
## 0.720 0.720
## 0.338 0.338
## 0.405 0.405
## 0.600 0.600
## 0.315 0.315
## 0.372 0.372
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(PEmoDepCLPM_w2AR.fit, PEmoDepCLPM_w.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PEmoDepCLPM_w2AR.fit 63 10608 10909 163.66
## PEmoDepCLPM_w.fit 72 10911 11174 484.85 321.18 9 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.09, p < .001
(b1) Positive emotions at time t do not predict health at time t+1
PEmoHealthCLPM <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gHealth.1 + 1*gHealth.2 + 1*gHealth.3 + 1*gHealth.4 + 1*gHealth.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gHealth.1
wy2 =~ 1*gHealth.2
wy3 =~ 1*gHealth.3
wy4 =~ 1*gHealth.4
wy5 =~ 1*gHealth.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2
wy4 ~ cp1*wx2 + wy3 + b1*wm3
wy5 ~ cp1*wx3 + wy4 + b1*wm4
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2
wx4 ~ wx3 + cp2*wy2 + b2*wm3
wx5 ~ wx4 + cp2*wy3 + b2*wm4
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2
wm4 ~ a1*wx3 + a2*wy3 + wm3
wm5 ~ a1*wx4 + a2*wy4 + wm4
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoHealthCLPM_w.fit <- lavaan(PEmoHealthCLPM, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoHealthCLPM_w.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 36 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 79
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 522.333
## Degrees of freedom 72
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2897.268
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.839
## Tucker-Lewis Index (TLI) 0.765
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5432.882
## Loglikelihood unrestricted model (H1) -5171.715
##
## Akaike (AIC) 10991.764
## Bayesian (BIC) 11254.974
## Sample-size adjusted Bayesian (BIC) 11055.018
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.114
## 90 Percent confidence interval - lower 0.105
## 90 Percent confidence interval - upper 0.123
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.105
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.997 1.000
##
## 1.001 1.000
##
## 0.994 1.000
##
## 0.991 1.000
##
## 0.997 1.000
##
## 0.996 1.000
##
## 0.975 1.000
##
## 0.984 1.000
##
## 0.981 1.000
##
## 0.981 1.000
##
## 1.004 1.000
##
## 0.990 1.000
##
## 0.992 1.000
##
## 0.979 1.000
##
## 0.991 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.774 0.032 24.347 0.000 0.712 0.837
## wm1 (b1) 0.026 0.018 1.430 0.153 -0.010 0.063
## wy3 ~
## wx1 (cp1) -0.072 0.024 -2.976 0.003 -0.120 -0.025
## wy2 0.767 0.037 20.491 0.000 0.693 0.840
## wm2 (b1) 0.026 0.018 1.430 0.153 -0.010 0.063
## wy4 ~
## wx2 (cp1) -0.072 0.024 -2.976 0.003 -0.120 -0.025
## wy3 0.776 0.037 20.944 0.000 0.703 0.849
## wm3 (b1) 0.026 0.018 1.430 0.153 -0.010 0.063
## wy5 ~
## wx3 (cp1) -0.072 0.024 -2.976 0.003 -0.120 -0.025
## wy4 0.774 0.038 20.462 0.000 0.700 0.848
## wm4 (b1) 0.026 0.018 1.430 0.153 -0.010 0.063
## wx2 ~
## wx1 0.535 0.047 11.473 0.000 0.444 0.626
## wm1 (b2) -0.071 0.025 -2.795 0.005 -0.120 -0.021
## wx3 ~
## wx2 0.493 0.053 9.353 0.000 0.390 0.596
## wy1 (cp2) -0.132 0.032 -4.128 0.000 -0.195 -0.069
## wm2 (b2) -0.071 0.025 -2.795 0.005 -0.120 -0.021
## wx4 ~
## wx3 0.464 0.054 8.550 0.000 0.357 0.570
## wy2 (cp2) -0.132 0.032 -4.128 0.000 -0.195 -0.069
## wm3 (b2) -0.071 0.025 -2.795 0.005 -0.120 -0.021
## wx5 ~
## wx4 0.549 0.052 10.662 0.000 0.448 0.650
## wy3 (cp2) -0.132 0.032 -4.128 0.000 -0.195 -0.069
## wm4 (b2) -0.071 0.025 -2.795 0.005 -0.120 -0.021
## wm2 ~
## wx1 (a1) -0.090 0.023 -3.846 0.000 -0.135 -0.044
## wy1 (a2) 0.041 0.023 1.780 0.075 -0.004 0.086
## wm1 0.580 0.041 14.030 0.000 0.499 0.661
## wm3 ~
## wx2 (a1) -0.090 0.023 -3.846 0.000 -0.135 -0.044
## wy2 (a2) 0.041 0.023 1.780 0.075 -0.004 0.086
## wm2 0.642 0.044 14.653 0.000 0.556 0.728
## wm4 ~
## wx3 (a1) -0.090 0.023 -3.846 0.000 -0.135 -0.044
## wy3 (a2) 0.041 0.023 1.780 0.075 -0.004 0.086
## wm3 0.693 0.042 16.563 0.000 0.611 0.775
## wm5 ~
## wx4 (a1) -0.090 0.023 -3.846 0.000 -0.135 -0.044
## wy4 (a2) 0.041 0.023 1.780 0.075 -0.004 0.086
## wm4 0.692 0.045 15.246 0.000 0.603 0.780
## Std.lv Std.all
##
## 0.791 0.791
## 0.027 0.027
##
## -0.073 -0.073
## 0.760 0.760
## 0.027 0.027
##
## -0.074 -0.074
## 0.778 0.778
## 0.027 0.027
##
## -0.073 -0.073
## 0.774 0.774
## 0.026 0.026
##
## 0.532 0.532
## -0.071 -0.071
##
## 0.497 0.497
## -0.133 -0.133
## -0.071 -0.071
##
## 0.465 0.465
## -0.130 -0.130
## -0.071 -0.071
##
## 0.546 0.546
## -0.131 -0.131
## -0.070 -0.070
##
## -0.090 -0.090
## 0.041 0.041
## 0.588 0.588
##
## -0.090 -0.090
## 0.040 0.040
## 0.640 0.640
##
## -0.091 -0.091
## 0.041 0.041
## 0.702 0.702
##
## -0.090 -0.090
## 0.041 0.041
## 0.683 0.683
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.345 0.050 -6.969 0.000 -0.442 -0.248
## wm1 -0.249 0.049 -5.085 0.000 -0.345 -0.153
## wy1 ~~
## wm1 0.201 0.048 4.171 0.000 0.107 0.295
## .wx2 ~~
## .wy2 -0.045 0.028 -1.640 0.101 -0.099 0.009
## .wx3 ~~
## .wy3 0.004 0.031 0.144 0.885 -0.056 0.065
## .wx4 ~~
## .wy4 0.059 0.032 1.839 0.066 -0.004 0.122
## .wx5 ~~
## .wy5 0.071 0.032 2.230 0.026 0.009 0.134
## .wx2 ~~
## .wm2 -0.048 0.036 -1.353 0.176 -0.118 0.022
## .wx3 ~~
## .wm3 0.024 0.036 0.676 0.499 -0.046 0.095
## .wx4 ~~
## .wm4 0.011 0.036 0.322 0.748 -0.058 0.081
## .wx5 ~~
## .wm5 -0.001 0.035 -0.017 0.986 -0.070 0.069
## .wy2 ~~
## .wm2 0.068 0.026 2.600 0.009 0.017 0.119
## .wy3 ~~
## .wm3 0.027 0.027 0.997 0.319 -0.026 0.079
## .wy4 ~~
## .wm4 0.001 0.025 0.057 0.954 -0.047 0.050
## .wy5 ~~
## .wm5 0.059 0.027 2.208 0.027 0.007 0.112
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.348 -0.348
## -0.249 -0.249
##
## 0.201 0.201
##
## -0.092 -0.092
##
## 0.009 0.009
##
## 0.121 0.121
##
## 0.152 0.152
##
## -0.075 -0.075
##
## 0.041 0.041
##
## 0.020 0.020
##
## -0.001 -0.001
##
## 0.149 0.149
##
## 0.061 0.061
##
## 0.004 0.004
##
## 0.144 0.144
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.000 0.047 0.003 0.997 -0.092 0.092
## .LadderDif.2 -0.010 0.051 -0.191 0.848 -0.111 0.091
## .LadderDif.3 -0.012 0.057 -0.215 0.830 -0.124 0.099
## .LadderDif.4 -0.003 0.060 -0.054 0.957 -0.122 0.115
## .LadderDif.5 -0.008 0.062 -0.131 0.896 -0.130 0.114
## .gHealth.1 0.005 0.046 0.103 0.918 -0.086 0.095
## .gHealth.2 -0.007 0.047 -0.141 0.888 -0.100 0.086
## .gHealth.3 0.001 0.052 0.023 0.981 -0.101 0.104
## .gHealth.4 0.008 0.056 0.136 0.892 -0.101 0.117
## .gHealth.5 0.011 0.058 0.186 0.852 -0.103 0.125
## .posEmo.1 -0.003 0.047 -0.062 0.950 -0.095 0.089
## .posEmo.2 -0.010 0.050 -0.196 0.844 -0.108 0.088
## .posEmo.3 -0.002 0.055 -0.031 0.975 -0.110 0.107
## .posEmo.4 -0.005 0.058 -0.084 0.933 -0.118 0.108
## .posEmo.5 0.011 0.061 0.181 0.857 -0.108 0.130
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.000 0.000
## -0.010 -0.010
## -0.012 -0.012
## -0.003 -0.003
## -0.008 -0.008
## 0.005 0.005
## -0.007 -0.007
## 0.001 0.001
## 0.008 0.008
## 0.011 0.011
## -0.003 -0.003
## -0.010 -0.010
## -0.002 -0.002
## -0.005 -0.005
## 0.011 0.011
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.993 0.067 14.934 0.000 0.863 1.124
## wy1 0.993 0.066 15.114 0.000 0.864 1.121
## wm1 1.008 0.068 14.817 0.000 0.875 1.141
## .wx2 0.695 0.054 12.902 0.000 0.589 0.800
## .wy2 0.346 0.028 12.510 0.000 0.292 0.401
## .wm2 0.594 0.047 12.578 0.000 0.502 0.687
## .wx3 0.677 0.058 11.761 0.000 0.564 0.790
## .wy3 0.363 0.031 11.778 0.000 0.302 0.423
## .wm3 0.532 0.045 11.752 0.000 0.443 0.621
## .wx4 0.707 0.063 11.189 0.000 0.583 0.830
## .wy4 0.341 0.030 11.192 0.000 0.281 0.401
## .wm4 0.442 0.040 11.167 0.000 0.365 0.520
## .wx5 0.629 0.057 10.982 0.000 0.517 0.742
## .wy5 0.350 0.032 10.974 0.000 0.288 0.413
## .wm5 0.482 0.044 10.996 0.000 0.396 0.568
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.693 0.693
## 0.364 0.364
## 0.606 0.606
## 0.685 0.685
## 0.375 0.375
## 0.540 0.540
## 0.719 0.719
## 0.354 0.354
## 0.461 0.461
## 0.633 0.633
## 0.364 0.364
## 0.491 0.491
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b1) Positive emotions at time t do not predict health at time t+1
# Same model as above code, but fit with d_black dataset this time
PEmoHealthCLPM_b.fit <- lavaan(PEmoHealthCLPM, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoHealthCLPM_b.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 34 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 79
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 444.357
## Degrees of freedom 72
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2035.348
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.807
## Tucker-Lewis Index (TLI) 0.719
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5074.233
## Loglikelihood unrestricted model (H1) -4852.055
##
## Akaike (AIC) 10274.466
## Bayesian (BIC) 10537.676
## Sample-size adjusted Bayesian (BIC) 10337.720
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.104
## 90 Percent confidence interval - lower 0.094
## 90 Percent confidence interval - upper 0.113
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.112
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.998 1.000
##
## 1.008 1.000
##
## 1.005 1.000
##
## 0.995 1.000
##
## 0.994 1.000
##
## 1.002 1.000
##
## 0.997 1.000
##
## 0.989 1.000
##
## 1.004 1.000
##
## 0.997 1.000
##
## 0.994 1.000
##
## 0.994 1.000
##
## 0.983 1.000
##
## 0.999 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.725 0.044 16.331 0.000 0.638 0.812
## wm1 (b1) 0.031 0.023 1.360 0.174 -0.014 0.076
## wy3 ~
## wx1 (cp1) -0.023 0.028 -0.809 0.419 -0.079 0.033
## wy2 0.744 0.043 17.386 0.000 0.660 0.828
## wm2 (b1) 0.031 0.023 1.360 0.174 -0.014 0.076
## wy4 ~
## wx2 (cp1) -0.023 0.028 -0.809 0.419 -0.079 0.033
## wy3 0.724 0.047 15.548 0.000 0.632 0.815
## wm3 (b1) 0.031 0.023 1.360 0.174 -0.014 0.076
## wy5 ~
## wx3 (cp1) -0.023 0.028 -0.809 0.419 -0.079 0.033
## wy4 0.766 0.047 16.384 0.000 0.675 0.858
## wm4 (b1) 0.031 0.023 1.360 0.174 -0.014 0.076
## wx2 ~
## wx1 0.252 0.061 4.106 0.000 0.132 0.373
## wm1 (b2) -0.023 0.030 -0.741 0.458 -0.082 0.037
## wx3 ~
## wx2 0.540 0.059 9.081 0.000 0.423 0.656
## wy1 (cp2) -0.001 0.038 -0.034 0.973 -0.075 0.072
## wm2 (b2) -0.023 0.030 -0.741 0.458 -0.082 0.037
## wx4 ~
## wx3 0.462 0.064 7.197 0.000 0.336 0.588
## wy2 (cp2) -0.001 0.038 -0.034 0.973 -0.075 0.072
## wm3 (b2) -0.023 0.030 -0.741 0.458 -0.082 0.037
## wx5 ~
## wx4 0.484 0.060 8.040 0.000 0.366 0.601
## wy3 (cp2) -0.001 0.038 -0.034 0.973 -0.075 0.072
## wm4 (b2) -0.023 0.030 -0.741 0.458 -0.082 0.037
## wm2 ~
## wx1 (a1) -0.025 0.024 -1.049 0.294 -0.073 0.022
## wy1 (a2) 0.055 0.025 2.227 0.026 0.007 0.104
## wm1 0.522 0.051 10.189 0.000 0.422 0.623
## wm3 ~
## wx2 (a1) -0.025 0.024 -1.049 0.294 -0.073 0.022
## wy2 (a2) 0.055 0.025 2.227 0.026 0.007 0.104
## wm2 0.712 0.045 15.879 0.000 0.624 0.800
## wm4 ~
## wx3 (a1) -0.025 0.024 -1.049 0.294 -0.073 0.022
## wy3 (a2) 0.055 0.025 2.227 0.026 0.007 0.104
## wm3 0.728 0.045 16.291 0.000 0.641 0.816
## wm5 ~
## wx4 (a1) -0.025 0.024 -1.049 0.294 -0.073 0.022
## wy4 (a2) 0.055 0.025 2.227 0.026 0.007 0.104
## wm4 0.728 0.049 14.939 0.000 0.632 0.823
## Std.lv Std.all
##
## 0.719 0.719
## 0.031 0.031
##
## -0.023 -0.023
## 0.747 0.747
## 0.031 0.031
##
## -0.023 -0.023
## 0.729 0.729
## 0.031 0.031
##
## -0.023 -0.023
## 0.755 0.755
## 0.031 0.031
##
## 0.252 0.252
## -0.023 -0.023
##
## 0.534 0.534
## -0.001 -0.001
## -0.022 -0.022
##
## 0.463 0.463
## -0.001 -0.001
## -0.022 -0.022
##
## 0.488 0.488
## -0.001 -0.001
## -0.022 -0.022
##
## -0.025 -0.025
## 0.055 0.055
## 0.524 0.524
##
## -0.025 -0.025
## 0.056 0.056
## 0.712 0.712
##
## -0.026 -0.026
## 0.056 0.056
## 0.737 0.737
##
## -0.025 -0.025
## 0.055 0.055
## 0.716 0.716
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.051 0.046 -1.093 0.274 -0.142 0.040
## wm1 -0.007 0.047 -0.160 0.873 -0.099 0.084
## wy1 ~~
## wm1 0.266 0.048 5.570 0.000 0.173 0.360
## .wx2 ~~
## .wy2 -0.017 0.042 -0.412 0.680 -0.099 0.065
## .wx3 ~~
## .wy3 0.054 0.038 1.440 0.150 -0.020 0.128
## .wx4 ~~
## .wy4 -0.016 0.042 -0.372 0.710 -0.099 0.067
## .wx5 ~~
## .wy5 0.043 0.042 1.044 0.297 -0.038 0.125
## .wx2 ~~
## .wm2 -0.001 0.049 -0.022 0.982 -0.097 0.095
## .wx3 ~~
## .wm3 -0.025 0.039 -0.646 0.518 -0.101 0.051
## .wx4 ~~
## .wm4 0.016 0.039 0.409 0.683 -0.061 0.093
## .wx5 ~~
## .wm5 0.020 0.041 0.476 0.634 -0.061 0.101
## .wy2 ~~
## .wm2 0.013 0.035 0.357 0.721 -0.057 0.082
## .wy3 ~~
## .wm3 0.084 0.030 2.782 0.005 0.025 0.143
## .wy4 ~~
## .wm4 0.057 0.030 1.898 0.058 -0.002 0.116
## .wy5 ~~
## .wm5 0.051 0.031 1.630 0.103 -0.010 0.112
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.051 -0.051
## -0.007 -0.007
##
## 0.269 0.269
##
## -0.026 -0.026
##
## 0.097 0.097
##
## -0.027 -0.027
##
## 0.077 0.077
##
## -0.001 -0.001
##
## -0.043 -0.043
##
## 0.028 0.028
##
## 0.033 0.033
##
## 0.022 0.022
##
## 0.188 0.188
##
## 0.133 0.133
##
## 0.116 0.116
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.007 0.047 -0.147 0.883 -0.099 0.085
## .LadderDif.2 0.004 0.059 0.076 0.939 -0.110 0.119
## .LadderDif.3 0.003 0.065 0.039 0.969 -0.124 0.129
## .LadderDif.4 0.019 0.068 0.279 0.780 -0.114 0.152
## .LadderDif.5 0.008 0.069 0.115 0.908 -0.128 0.144
## .gHealth.1 -0.004 0.046 -0.080 0.936 -0.094 0.086
## .gHealth.2 -0.087 0.053 -1.631 0.103 -0.191 0.017
## .gHealth.3 -0.049 0.059 -0.832 0.405 -0.164 0.066
## .gHealth.4 -0.030 0.063 -0.478 0.633 -0.153 0.093
## .gHealth.5 -0.053 0.067 -0.793 0.428 -0.183 0.078
## .posEmo.1 -0.001 0.046 -0.026 0.980 -0.092 0.090
## .posEmo.2 -0.013 0.056 -0.236 0.813 -0.122 0.096
## .posEmo.3 -0.011 0.061 -0.188 0.851 -0.130 0.107
## .posEmo.4 -0.015 0.063 -0.238 0.812 -0.139 0.109
## .posEmo.5 -0.019 0.067 -0.280 0.779 -0.150 0.112
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.007 -0.007
## 0.004 0.004
## 0.003 0.003
## 0.019 0.019
## 0.008 0.008
## -0.004 -0.004
## -0.087 -0.086
## -0.049 -0.049
## -0.030 -0.030
## -0.053 -0.053
## -0.001 -0.001
## -0.013 -0.013
## -0.011 -0.011
## -0.015 -0.015
## -0.019 -0.019
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.997 0.066 15.023 0.000 0.867 1.128
## wy1 0.988 0.065 15.236 0.000 0.861 1.115
## wm1 0.995 0.066 15.108 0.000 0.866 1.124
## .wx2 0.932 0.079 11.803 0.000 0.777 1.086
## .wy2 0.472 0.041 11.480 0.000 0.392 0.553
## .wm2 0.698 0.060 11.594 0.000 0.580 0.816
## .wx3 0.725 0.068 10.690 0.000 0.592 0.858
## .wy3 0.428 0.040 10.688 0.000 0.349 0.506
## .wm3 0.469 0.044 10.692 0.000 0.383 0.555
## .wx4 0.791 0.077 10.325 0.000 0.641 0.941
## .wy4 0.444 0.043 10.288 0.000 0.360 0.529
## .wm4 0.417 0.040 10.318 0.000 0.337 0.496
## .wx5 0.753 0.075 10.090 0.000 0.607 0.899
## .wy5 0.421 0.042 10.075 0.000 0.339 0.503
## .wm5 0.461 0.046 10.081 0.000 0.371 0.550
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.936 0.936
## 0.470 0.470
## 0.706 0.706
## 0.713 0.713
## 0.430 0.430
## 0.475 0.475
## 0.783 0.783
## 0.454 0.454
## 0.431 0.431
## 0.760 0.760
## 0.417 0.417
## 0.462 0.462
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.08, p < .001
(b1) Positive emotions at time t do not predict health at time t+1
PEmoHealthCLPM_2AR <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gHealth.1 + 1*gHealth.2 + 1*gHealth.3 + 1*gHealth.4 + 1*gHealth.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gHealth.1
wy2 =~ 1*gHealth.2
wy3 =~ 1*gHealth.3
wy4 =~ 1*gHealth.4
wy5 =~ 1*gHealth.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoHealthCLPM_w2AR.fit <- lavaan(PEmoHealthCLPM_2AR, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoHealthCLPM_w2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 40 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 165.856
## Degrees of freedom 63
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2897.268
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.963
## Tucker-Lewis Index (TLI) 0.939
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5254.643
## Loglikelihood unrestricted model (H1) -5171.715
##
## Akaike (AIC) 10653.286
## Bayesian (BIC) 10954.098
## Sample-size adjusted Bayesian (BIC) 10725.577
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.058
## 90 Percent confidence interval - lower 0.047
## 90 Percent confidence interval - upper 0.069
## P-value RMSEA <= 0.05 0.101
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.048
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.999 1.000
##
## 0.994 1.000
##
## 1.002 1.000
##
## 0.988 1.000
##
## 0.994 1.000
##
## 0.974 1.000
##
## 0.974 1.000
##
## 0.971 1.000
##
## 0.967 1.000
##
## 1.002 1.000
##
## 0.988 1.000
##
## 0.987 1.000
##
## 0.977 1.000
##
## 0.991 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.774 0.032 24.313 0.000 0.712 0.837
## wm1 (b1) 0.025 0.017 1.450 0.147 -0.009 0.060
## wy3 ~
## wx1 (cp1) 0.004 0.022 0.174 0.861 -0.038 0.046
## wy2 0.375 0.056 6.761 0.000 0.267 0.484
## wm2 (b1) 0.025 0.017 1.450 0.147 -0.009 0.060
## wy1 0.496 0.055 9.040 0.000 0.389 0.604
## wy4 ~
## wx2 (cp1) 0.004 0.022 0.174 0.861 -0.038 0.046
## wy3 0.593 0.059 9.979 0.000 0.476 0.709
## wm3 (b1) 0.025 0.017 1.450 0.147 -0.009 0.060
## wy2 0.255 0.059 4.305 0.000 0.139 0.371
## wy5 ~
## wx3 (cp1) 0.004 0.022 0.174 0.861 -0.038 0.046
## wy4 0.433 0.058 7.423 0.000 0.319 0.548
## wm4 (b1) 0.025 0.017 1.450 0.147 -0.009 0.060
## wy3 0.437 0.058 7.477 0.000 0.322 0.551
## wx2 ~
## wx1 0.541 0.046 11.685 0.000 0.450 0.631
## wm1 (b2) -0.041 0.024 -1.723 0.085 -0.089 0.006
## wx3 ~
## wx2 0.287 0.058 4.924 0.000 0.173 0.401
## wy1 (cp2) -0.047 0.029 -1.620 0.105 -0.104 0.010
## wm2 (b2) -0.041 0.024 -1.723 0.085 -0.089 0.006
## wx1 0.411 0.058 7.114 0.000 0.298 0.525
## wx4 ~
## wx3 0.297 0.060 4.949 0.000 0.180 0.415
## wy2 (cp2) -0.047 0.029 -1.620 0.105 -0.104 0.010
## wm3 (b2) -0.041 0.024 -1.723 0.085 -0.089 0.006
## wx2 0.384 0.063 6.133 0.000 0.261 0.506
## wx5 ~
## wx4 0.336 0.054 6.204 0.000 0.230 0.442
## wy3 (cp2) -0.047 0.029 -1.620 0.105 -0.104 0.010
## wm4 (b2) -0.041 0.024 -1.723 0.085 -0.089 0.006
## wx3 0.432 0.054 8.025 0.000 0.327 0.538
## wm2 ~
## wx1 (a1) -0.081 0.022 -3.661 0.000 -0.125 -0.038
## wy1 (a2) 0.033 0.022 1.516 0.130 -0.010 0.077
## wm1 0.579 0.042 13.868 0.000 0.497 0.660
## wm3 ~
## wx2 (a1) -0.081 0.022 -3.661 0.000 -0.125 -0.038
## wy2 (a2) 0.033 0.022 1.516 0.130 -0.010 0.077
## wm2 0.508 0.057 8.926 0.000 0.396 0.619
## wm1 0.214 0.060 3.575 0.000 0.097 0.331
## wm4 ~
## wx3 (a1) -0.081 0.022 -3.661 0.000 -0.125 -0.038
## wy3 (a2) 0.033 0.022 1.516 0.130 -0.010 0.077
## wm3 0.521 0.055 9.511 0.000 0.413 0.628
## wm2 0.258 0.056 4.634 0.000 0.149 0.367
## wm5 ~
## wx4 (a1) -0.081 0.022 -3.661 0.000 -0.125 -0.038
## wy4 (a2) 0.033 0.022 1.516 0.130 -0.010 0.077
## wm4 0.352 0.057 6.137 0.000 0.240 0.465
## wm3 0.472 0.056 8.395 0.000 0.362 0.583
## Std.lv Std.all
##
## 0.790 0.790
## 0.026 0.026
##
## 0.004 0.004
## 0.375 0.375
## 0.026 0.026
## 0.507 0.507
##
## 0.004 0.004
## 0.594 0.594
## 0.026 0.026
## 0.255 0.255
##
## 0.004 0.004
## 0.435 0.435
## 0.026 0.026
## 0.440 0.440
##
## 0.541 0.541
## -0.042 -0.042
##
## 0.288 0.288
## -0.047 -0.047
## -0.041 -0.041
## 0.413 0.413
##
## 0.295 0.295
## -0.046 -0.046
## -0.041 -0.041
## 0.382 0.382
##
## 0.341 0.341
## -0.047 -0.047
## -0.041 -0.041
## 0.435 0.435
##
## -0.082 -0.082
## 0.034 0.034
## 0.587 0.587
##
## -0.082 -0.082
## 0.033 0.033
## 0.508 0.508
## 0.217 0.217
##
## -0.083 -0.083
## 0.033 0.033
## 0.526 0.526
## 0.261 0.261
##
## -0.082 -0.082
## 0.033 0.033
## 0.347 0.347
## 0.470 0.470
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.349 0.049 -7.071 0.000 -0.446 -0.252
## wm1 -0.251 0.049 -5.138 0.000 -0.347 -0.155
## wy1 ~~
## wm1 0.201 0.048 4.208 0.000 0.107 0.295
## .wx2 ~~
## .wy2 -0.042 0.027 -1.535 0.125 -0.096 0.012
## .wx3 ~~
## .wy3 -0.023 0.025 -0.938 0.348 -0.071 0.025
## .wx4 ~~
## .wy4 0.042 0.029 1.476 0.140 -0.014 0.098
## .wx5 ~~
## .wy5 0.015 0.025 0.613 0.540 -0.033 0.063
## .wx2 ~~
## .wm2 -0.049 0.036 -1.363 0.173 -0.119 0.021
## .wx3 ~~
## .wm3 0.004 0.033 0.109 0.913 -0.061 0.068
## .wx4 ~~
## .wm4 0.017 0.032 0.519 0.604 -0.046 0.079
## .wx5 ~~
## .wm5 0.005 0.028 0.166 0.868 -0.050 0.059
## .wy2 ~~
## .wm2 0.069 0.026 2.625 0.009 0.017 0.120
## .wy3 ~~
## .wm3 -0.005 0.023 -0.199 0.842 -0.051 0.041
## .wy4 ~~
## .wm4 0.010 0.023 0.442 0.658 -0.035 0.055
## .wy5 ~~
## .wm5 0.037 0.021 1.727 0.084 -0.005 0.078
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.352 -0.352
## -0.251 -0.251
##
## 0.202 0.202
##
## -0.086 -0.086
##
## -0.058 -0.058
##
## 0.095 0.095
##
## 0.040 0.040
##
## -0.076 -0.076
##
## 0.007 0.007
##
## 0.033 0.033
##
## 0.011 0.011
##
## 0.150 0.150
##
## -0.012 -0.012
##
## 0.028 0.028
##
## 0.113 0.113
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.002 0.047 0.038 0.970 -0.090 0.093
## .LadderDif.2 -0.011 0.051 -0.212 0.832 -0.111 0.090
## .LadderDif.3 -0.033 0.055 -0.608 0.543 -0.141 0.074
## .LadderDif.4 -0.014 0.059 -0.232 0.817 -0.129 0.102
## .LadderDif.5 -0.020 0.059 -0.334 0.738 -0.136 0.096
## .gHealth.1 0.005 0.046 0.100 0.920 -0.085 0.095
## .gHealth.2 -0.007 0.047 -0.139 0.889 -0.100 0.086
## .gHealth.3 -0.004 0.049 -0.085 0.932 -0.101 0.093
## .gHealth.4 0.009 0.053 0.169 0.866 -0.095 0.112
## .gHealth.5 0.008 0.054 0.152 0.879 -0.097 0.114
## .posEmo.1 -0.006 0.047 -0.131 0.896 -0.098 0.086
## .posEmo.2 -0.009 0.050 -0.182 0.855 -0.107 0.089
## .posEmo.3 0.004 0.054 0.081 0.935 -0.102 0.111
## .posEmo.4 -0.000 0.056 -0.005 0.996 -0.110 0.109
## .posEmo.5 0.015 0.058 0.266 0.790 -0.098 0.129
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.002 0.002
## -0.011 -0.011
## -0.033 -0.033
## -0.014 -0.014
## -0.020 -0.020
## 0.005 0.005
## -0.007 -0.007
## -0.004 -0.004
## 0.009 0.009
## 0.008 0.008
## -0.006 -0.006
## -0.009 -0.009
## 0.004 0.004
## -0.000 -0.000
## 0.015 0.016
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.998 0.067 14.935 0.000 0.867 1.129
## wy1 0.989 0.065 15.192 0.000 0.861 1.116
## wm1 1.004 0.068 14.863 0.000 0.872 1.136
## .wx2 0.692 0.054 12.936 0.000 0.587 0.797
## .wy2 0.348 0.028 12.553 0.000 0.294 0.403
## .wm2 0.600 0.048 12.551 0.000 0.506 0.694
## .wx3 0.572 0.050 11.539 0.000 0.475 0.669
## .wy3 0.277 0.024 11.503 0.000 0.230 0.324
## .wm3 0.503 0.043 11.641 0.000 0.419 0.588
## .wx4 0.619 0.055 11.184 0.000 0.511 0.728
## .wy4 0.317 0.028 11.194 0.000 0.261 0.372
## .wm4 0.408 0.036 11.188 0.000 0.337 0.480
## .wx5 0.496 0.045 10.999 0.000 0.407 0.584
## .wy5 0.283 0.026 10.971 0.000 0.232 0.333
## .wm5 0.371 0.034 10.988 0.000 0.305 0.437
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.694 0.694
## 0.367 0.367
## 0.614 0.614
## 0.578 0.578
## 0.292 0.292
## 0.516 0.516
## 0.617 0.617
## 0.336 0.336
## 0.427 0.427
## 0.508 0.508
## 0.302 0.302
## 0.378 0.378
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(PEmoHealthCLPM_w2AR.fit, PEmoHealthCLPM_w.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PEmoHealthCLPM_w2AR.fit 63 10653 10954 165.86
## PEmoHealthCLPM_w.fit 72 10992 11255 522.33 356.48 9 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b1) Positive emotions at time t do not predict health at time t+1
# Same model as above code, but fit with d_black dataset this time
PEmoHealthCLPM_b2AR.fit <- lavaan(PEmoHealthCLPM_2AR, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoHealthCLPM_b2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 173.057
## Degrees of freedom 63
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2035.348
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.943
## Tucker-Lewis Index (TLI) 0.905
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4938.583
## Loglikelihood unrestricted model (H1) -4852.055
##
## Akaike (AIC) 10021.167
## Bayesian (BIC) 10321.979
## Sample-size adjusted Bayesian (BIC) 10093.457
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.060
## 90 Percent confidence interval - lower 0.050
## 90 Percent confidence interval - upper 0.071
## P-value RMSEA <= 0.05 0.057
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.061
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 0.998 1.000
##
## 1.009 1.000
##
## 1.011 1.000
##
## 1.013 1.000
##
## 0.995 1.000
##
## 1.000 1.000
##
## 0.992 1.000
##
## 0.986 1.000
##
## 1.008 1.000
##
## 0.996 1.000
##
## 0.994 1.000
##
## 0.987 1.000
##
## 0.967 1.000
##
## 0.987 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.727 0.044 16.514 0.000 0.641 0.814
## wm1 (b1) 0.019 0.022 0.873 0.383 -0.023 0.061
## wy3 ~
## wx1 (cp1) -0.024 0.025 -0.942 0.346 -0.073 0.025
## wy2 0.510 0.061 8.404 0.000 0.391 0.629
## wm2 (b1) 0.019 0.022 0.873 0.383 -0.023 0.061
## wy1 0.319 0.061 5.267 0.000 0.200 0.438
## wy4 ~
## wx2 (cp1) -0.024 0.025 -0.942 0.346 -0.073 0.025
## wy3 0.418 0.067 6.271 0.000 0.288 0.549
## wm3 (b1) 0.019 0.022 0.873 0.383 -0.023 0.061
## wy2 0.393 0.066 5.995 0.000 0.265 0.522
## wy5 ~
## wx3 (cp1) -0.024 0.025 -0.942 0.346 -0.073 0.025
## wy4 0.400 0.059 6.802 0.000 0.285 0.515
## wm4 (b1) 0.019 0.022 0.873 0.383 -0.023 0.061
## wy3 0.510 0.059 8.649 0.000 0.395 0.626
## wx2 ~
## wx1 0.261 0.061 4.291 0.000 0.142 0.381
## wm1 (b2) -0.007 0.029 -0.254 0.800 -0.065 0.050
## wx3 ~
## wx2 0.497 0.061 8.183 0.000 0.378 0.616
## wy1 (cp2) 0.010 0.034 0.286 0.775 -0.058 0.077
## wm2 (b2) -0.007 0.029 -0.254 0.800 -0.065 0.050
## wx1 0.173 0.062 2.793 0.005 0.052 0.295
## wx4 ~
## wx3 0.315 0.071 4.420 0.000 0.175 0.454
## wy2 (cp2) 0.010 0.034 0.286 0.775 -0.058 0.077
## wm3 (b2) -0.007 0.029 -0.254 0.800 -0.065 0.050
## wx2 0.287 0.072 3.975 0.000 0.146 0.429
## wx5 ~
## wx4 0.288 0.062 4.676 0.000 0.167 0.409
## wy3 (cp2) 0.010 0.034 0.286 0.775 -0.058 0.077
## wm4 (b2) -0.007 0.029 -0.254 0.800 -0.065 0.050
## wx3 0.443 0.065 6.827 0.000 0.316 0.570
## wm2 ~
## wx1 (a1) -0.026 0.023 -1.132 0.258 -0.071 0.019
## wy1 (a2) 0.041 0.024 1.719 0.086 -0.006 0.087
## wm1 0.523 0.052 10.138 0.000 0.422 0.624
## wm3 ~
## wx2 (a1) -0.026 0.023 -1.132 0.258 -0.071 0.019
## wy2 (a2) 0.041 0.024 1.719 0.086 -0.006 0.087
## wm2 0.586 0.053 11.121 0.000 0.483 0.690
## wm1 0.228 0.054 4.245 0.000 0.123 0.333
## wm4 ~
## wx3 (a1) -0.026 0.023 -1.132 0.258 -0.071 0.019
## wy3 (a2) 0.041 0.024 1.719 0.086 -0.006 0.087
## wm3 0.488 0.063 7.736 0.000 0.364 0.612
## wm2 0.313 0.061 5.099 0.000 0.192 0.433
## wm5 ~
## wx4 (a1) -0.026 0.023 -1.132 0.258 -0.071 0.019
## wy4 (a2) 0.041 0.024 1.719 0.086 -0.006 0.087
## wm4 0.370 0.065 5.672 0.000 0.242 0.498
## wm3 0.471 0.064 7.390 0.000 0.346 0.595
## Std.lv Std.all
##
## 0.724 0.724
## 0.019 0.019
##
## -0.024 -0.024
## 0.514 0.514
## 0.019 0.019
## 0.320 0.320
##
## -0.024 -0.024
## 0.421 0.421
## 0.019 0.019
## 0.399 0.399
##
## -0.024 -0.024
## 0.391 0.391
## 0.018 0.018
## 0.502 0.502
##
## 0.262 0.262
## -0.007 -0.007
##
## 0.492 0.492
## 0.010 0.010
## -0.007 -0.007
## 0.172 0.172
##
## 0.314 0.314
## 0.010 0.010
## -0.007 -0.007
## 0.283 0.283
##
## 0.288 0.288
## 0.010 0.010
## -0.007 -0.007
## 0.441 0.441
##
## -0.026 -0.026
## 0.041 0.041
## 0.524 0.524
##
## -0.026 -0.026
## 0.041 0.041
## 0.590 0.590
## 0.230 0.230
##
## -0.027 -0.027
## 0.042 0.042
## 0.498 0.498
## 0.321 0.321
##
## -0.026 -0.026
## 0.041 0.041
## 0.363 0.363
## 0.471 0.471
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.049 0.047 -1.061 0.289 -0.141 0.042
## wm1 -0.007 0.047 -0.148 0.883 -0.098 0.085
## wy1 ~~
## wm1 0.265 0.048 5.557 0.000 0.172 0.359
## .wx2 ~~
## .wy2 -0.018 0.041 -0.445 0.657 -0.100 0.063
## .wx3 ~~
## .wy3 0.052 0.035 1.496 0.135 -0.016 0.120
## .wx4 ~~
## .wy4 -0.053 0.038 -1.382 0.167 -0.128 0.022
## .wx5 ~~
## .wy5 0.027 0.031 0.879 0.380 -0.033 0.087
## .wx2 ~~
## .wm2 -0.004 0.049 -0.092 0.927 -0.101 0.092
## .wx3 ~~
## .wm3 -0.012 0.036 -0.331 0.741 -0.083 0.059
## .wx4 ~~
## .wm4 0.020 0.036 0.537 0.591 -0.052 0.091
## .wx5 ~~
## .wm5 -0.043 0.034 -1.266 0.205 -0.110 0.024
## .wy2 ~~
## .wm2 0.014 0.035 0.402 0.688 -0.055 0.083
## .wy3 ~~
## .wm3 0.074 0.028 2.669 0.008 0.020 0.128
## .wy4 ~~
## .wm4 0.068 0.027 2.497 0.013 0.015 0.122
## .wy5 ~~
## .wm5 0.035 0.024 1.457 0.145 -0.012 0.081
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.050 -0.050
## -0.007 -0.007
##
## 0.268 0.268
##
## -0.028 -0.028
##
## 0.101 0.101
##
## -0.098 -0.098
##
## 0.062 0.062
##
## -0.006 -0.006
##
## -0.022 -0.022
##
## 0.037 0.037
##
## -0.091 -0.091
##
## 0.025 0.025
##
## 0.183 0.183
##
## 0.178 0.178
##
## 0.104 0.104
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.008 0.047 -0.170 0.865 -0.100 0.084
## .LadderDif.2 0.005 0.059 0.079 0.937 -0.110 0.119
## .LadderDif.3 -0.003 0.064 -0.041 0.967 -0.128 0.122
## .LadderDif.4 0.017 0.067 0.254 0.799 -0.115 0.149
## .LadderDif.5 0.016 0.069 0.227 0.820 -0.119 0.150
## .gHealth.1 -0.004 0.046 -0.090 0.929 -0.094 0.086
## .gHealth.2 -0.087 0.053 -1.634 0.102 -0.190 0.017
## .gHealth.3 -0.073 0.056 -1.296 0.195 -0.183 0.037
## .gHealth.4 -0.052 0.059 -0.888 0.375 -0.168 0.063
## .gHealth.5 -0.099 0.061 -1.614 0.106 -0.220 0.021
## .posEmo.1 -0.003 0.046 -0.059 0.953 -0.094 0.088
## .posEmo.2 -0.011 0.056 -0.195 0.846 -0.120 0.098
## .posEmo.3 -0.014 0.058 -0.239 0.811 -0.128 0.100
## .posEmo.4 -0.020 0.060 -0.329 0.742 -0.137 0.097
## .posEmo.5 -0.027 0.063 -0.439 0.661 -0.150 0.095
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.008 -0.008
## 0.005 0.005
## -0.003 -0.003
## 0.017 0.017
## 0.016 0.015
## -0.004 -0.004
## -0.087 -0.087
## -0.073 -0.073
## -0.052 -0.053
## -0.099 -0.098
## -0.003 -0.003
## -0.011 -0.011
## -0.014 -0.014
## -0.020 -0.020
## -0.027 -0.028
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.999 0.067 14.998 0.000 0.869 1.130
## wy1 0.991 0.065 15.234 0.000 0.863 1.118
## wm1 0.992 0.065 15.145 0.000 0.864 1.120
## .wx2 0.928 0.079 11.801 0.000 0.774 1.082
## .wy2 0.469 0.041 11.536 0.000 0.389 0.549
## .wm2 0.703 0.061 11.587 0.000 0.584 0.822
## .wx3 0.697 0.065 10.647 0.000 0.569 0.826
## .wy3 0.379 0.036 10.560 0.000 0.309 0.450
## .wm3 0.428 0.041 10.541 0.000 0.348 0.507
## .wx4 0.741 0.072 10.319 0.000 0.601 0.882
## .wy4 0.392 0.038 10.261 0.000 0.317 0.467
## .wm4 0.374 0.036 10.298 0.000 0.303 0.446
## .wx5 0.619 0.061 10.092 0.000 0.499 0.740
## .wy5 0.307 0.030 10.086 0.000 0.247 0.367
## .wm5 0.366 0.036 10.076 0.000 0.295 0.437
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.931 0.931
## 0.469 0.469
## 0.711 0.711
## 0.685 0.685
## 0.385 0.385
## 0.439 0.439
## 0.725 0.725
## 0.403 0.403
## 0.400 0.400
## 0.603 0.603
## 0.302 0.302
## 0.376 0.376
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(PEmoHealthCLPM_b2AR.fit, PEmoHealthCLPM_b.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PEmoHealthCLPM_b2AR.fit 63 10021 10322 173.06
## PEmoHealthCLPM_b.fit 72 10274 10538 444.36 271.3 9 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.09, p < .001
(b1) Positive emotions at time t predict sleep at time t+1, b = .056, p = .006
PEmoSleepCLPM <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gSleep.1 + 1*gSleep.2 + 1*gSleep.3 + 1*gSleep.4 + 1*gSleep.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gSleep.1
wy2 =~ 1*gSleep.2
wy3 =~ 1*gSleep.3
wy4 =~ 1*gSleep.4
wy5 =~ 1*gSleep.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2
wy4 ~ cp1*wx2 + wy3 + b1*wm3
wy5 ~ cp1*wx3 + wy4 + b1*wm4
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2
wx4 ~ wx3 + cp2*wy2 + b2*wm3
wx5 ~ wx4 + cp2*wy3 + b2*wm4
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2
wm4 ~ a1*wx3 + a2*wy3 + wm3
wm5 ~ a1*wx4 + a2*wy4 + wm4
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoSleepCLPM_w.fit <- lavaan(PEmoSleepCLPM, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoSleepCLPM_w.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 44 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 79
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 450.816
## Degrees of freedom 72
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2666.889
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.852
## Tucker-Lewis Index (TLI) 0.784
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5514.597
## Loglikelihood unrestricted model (H1) -5289.189
##
## Akaike (AIC) 11155.194
## Bayesian (BIC) 11418.404
## Sample-size adjusted Bayesian (BIC) 11218.448
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.104
## 90 Percent confidence interval - lower 0.095
## 90 Percent confidence interval - upper 0.114
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.104
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.998 1.000
##
## 1.004 1.000
##
## 1.000 1.000
##
## 0.994 1.000
##
## 0.988 1.000
##
## 0.987 1.000
##
## 0.995 1.000
##
## 0.957 1.000
##
## 1.029 1.000
##
## 1.031 1.000
##
## 1.006 1.000
##
## 0.988 1.000
##
## 0.991 1.000
##
## 0.983 1.000
##
## 0.994 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.782 0.034 22.948 0.000 0.715 0.849
## wm1 (b1) 0.056 0.020 2.757 0.006 0.016 0.096
## wy3 ~
## wx1 (cp1) -0.081 0.026 -3.095 0.002 -0.132 -0.030
## wy2 0.670 0.039 17.205 0.000 0.593 0.746
## wm2 (b1) 0.056 0.020 2.757 0.006 0.016 0.096
## wy4 ~
## wx2 (cp1) -0.081 0.026 -3.095 0.002 -0.132 -0.030
## wy3 0.801 0.042 18.874 0.000 0.717 0.884
## wm3 (b1) 0.056 0.020 2.757 0.006 0.016 0.096
## wy5 ~
## wx3 (cp1) -0.081 0.026 -3.095 0.002 -0.132 -0.030
## wy4 0.751 0.040 18.558 0.000 0.672 0.830
## wm4 (b1) 0.056 0.020 2.757 0.006 0.016 0.096
## wx2 ~
## wx1 0.539 0.047 11.518 0.000 0.447 0.630
## wm1 (b2) -0.074 0.026 -2.838 0.005 -0.125 -0.023
## wx3 ~
## wx2 0.512 0.053 9.694 0.000 0.408 0.615
## wy1 (cp2) -0.081 0.032 -2.514 0.012 -0.145 -0.018
## wm2 (b2) -0.074 0.026 -2.838 0.005 -0.125 -0.023
## wx4 ~
## wx3 0.488 0.054 9.003 0.000 0.382 0.595
## wy2 (cp2) -0.081 0.032 -2.514 0.012 -0.145 -0.018
## wm3 (b2) -0.074 0.026 -2.838 0.005 -0.125 -0.023
## wx5 ~
## wx4 0.557 0.052 10.744 0.000 0.455 0.658
## wy3 (cp2) -0.081 0.032 -2.514 0.012 -0.145 -0.018
## wm4 (b2) -0.074 0.026 -2.838 0.005 -0.125 -0.023
## wm2 ~
## wx1 (a1) -0.092 0.023 -4.080 0.000 -0.136 -0.048
## wy1 (a2) 0.089 0.023 3.889 0.000 0.044 0.134
## wm1 0.565 0.041 13.646 0.000 0.484 0.646
## wm3 ~
## wx2 (a1) -0.092 0.023 -4.080 0.000 -0.136 -0.048
## wy2 (a2) 0.089 0.023 3.889 0.000 0.044 0.134
## wm2 0.629 0.043 14.570 0.000 0.545 0.714
## wm4 ~
## wx3 (a1) -0.092 0.023 -4.080 0.000 -0.136 -0.048
## wy3 (a2) 0.089 0.023 3.889 0.000 0.044 0.134
## wm3 0.671 0.043 15.766 0.000 0.588 0.755
## wm5 ~
## wx4 (a1) -0.092 0.023 -4.080 0.000 -0.136 -0.048
## wy4 (a2) 0.089 0.023 3.889 0.000 0.044 0.134
## wm4 0.674 0.045 14.876 0.000 0.585 0.763
## Std.lv Std.all
##
## 0.776 0.776
## 0.057 0.057
##
## -0.084 -0.084
## 0.696 0.696
## 0.058 0.058
##
## -0.079 -0.079
## 0.745 0.745
## 0.054 0.054
##
## -0.078 -0.078
## 0.749 0.749
## 0.053 0.053
##
## 0.535 0.535
## -0.074 -0.074
##
## 0.514 0.514
## -0.080 -0.080
## -0.073 -0.073
##
## 0.491 0.491
## -0.081 -0.081
## -0.074 -0.074
##
## 0.560 0.560
## -0.079 -0.079
## -0.073 -0.073
##
## -0.093 -0.093
## 0.089 0.089
## 0.575 0.575
##
## -0.093 -0.093
## 0.090 0.090
## 0.627 0.627
##
## -0.094 -0.094
## 0.087 0.087
## 0.677 0.677
##
## -0.092 -0.092
## 0.092 0.092
## 0.667 0.667
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.249 0.048 -5.143 0.000 -0.343 -0.154
## wm1 -0.250 0.049 -5.090 0.000 -0.347 -0.154
## wy1 ~~
## wm1 0.254 0.049 5.201 0.000 0.158 0.349
## .wx2 ~~
## .wy2 0.011 0.028 0.405 0.685 -0.044 0.067
## .wx3 ~~
## .wy3 0.043 0.034 1.273 0.203 -0.023 0.108
## .wx4 ~~
## .wy4 0.027 0.035 0.760 0.447 -0.042 0.096
## .wx5 ~~
## .wy5 -0.013 0.035 -0.386 0.700 -0.081 0.054
## .wx2 ~~
## .wm2 -0.043 0.035 -1.236 0.217 -0.112 0.025
## .wx3 ~~
## .wm3 0.032 0.036 0.874 0.382 -0.039 0.103
## .wx4 ~~
## .wm4 0.006 0.036 0.156 0.876 -0.065 0.076
## .wx5 ~~
## .wm5 0.002 0.035 0.070 0.944 -0.066 0.071
## .wy2 ~~
## .wm2 -0.004 0.026 -0.156 0.876 -0.056 0.047
## .wy3 ~~
## .wm3 0.098 0.029 3.383 0.001 0.041 0.155
## .wy4 ~~
## .wm4 0.029 0.027 1.071 0.284 -0.024 0.083
## .wy5 ~~
## .wm5 0.020 0.028 0.711 0.477 -0.036 0.076
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.253 -0.253
## -0.250 -0.250
##
## 0.255 0.255
##
## 0.023 0.023
##
## 0.079 0.079
##
## 0.049 0.049
##
## -0.026 -0.026
##
## -0.069 -0.069
##
## 0.053 0.053
##
## 0.010 0.010
##
## 0.005 0.005
##
## -0.009 -0.009
##
## 0.209 0.209
##
## 0.068 0.068
##
## 0.046 0.046
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.004 0.047 0.087 0.931 -0.088 0.096
## .LadderDif.2 -0.011 0.052 -0.212 0.832 -0.112 0.090
## .LadderDif.3 -0.010 0.057 -0.174 0.862 -0.122 0.102
## .LadderDif.4 -0.000 0.061 -0.003 0.998 -0.119 0.119
## .LadderDif.5 -0.006 0.062 -0.104 0.918 -0.128 0.115
## .gSleep.1 2.762 0.046 60.329 0.000 2.672 2.852
## .gSleep.2 2.842 0.049 58.504 0.000 2.747 2.937
## .gSleep.3 2.940 0.052 56.711 0.000 2.838 3.041
## .gSleep.4 2.888 0.059 48.732 0.000 2.772 3.005
## .gSleep.5 2.923 0.062 47.241 0.000 2.802 3.045
## .posEmo.1 -0.004 0.047 -0.081 0.935 -0.096 0.089
## .posEmo.2 -0.014 0.050 -0.283 0.777 -0.112 0.084
## .posEmo.3 -0.005 0.055 -0.090 0.928 -0.113 0.103
## .posEmo.4 -0.007 0.058 -0.123 0.902 -0.121 0.106
## .posEmo.5 0.010 0.061 0.172 0.863 -0.109 0.130
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.004 0.004
## -0.011 -0.011
## -0.010 -0.010
## -0.000 -0.000
## -0.006 -0.007
## 2.762 2.798
## 2.842 2.856
## 2.940 3.071
## 2.888 2.807
## 2.923 2.834
## -0.004 -0.004
## -0.014 -0.014
## -0.005 -0.005
## -0.007 -0.007
## 0.010 0.011
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.995 0.067 14.911 0.000 0.864 1.126
## wy1 0.974 0.065 14.929 0.000 0.846 1.102
## wm1 1.012 0.068 14.790 0.000 0.878 1.146
## .wx2 0.693 0.054 12.908 0.000 0.588 0.799
## .wy2 0.369 0.030 12.430 0.000 0.311 0.427
## .wm2 0.581 0.046 12.617 0.000 0.491 0.671
## .wx3 0.691 0.059 11.770 0.000 0.576 0.806
## .wy3 0.421 0.036 11.732 0.000 0.350 0.491
## .wm3 0.526 0.045 11.772 0.000 0.438 0.613
## .wx4 0.712 0.064 11.188 0.000 0.587 0.837
## .wy4 0.413 0.037 11.191 0.000 0.341 0.486
## .wm4 0.449 0.040 11.184 0.000 0.370 0.528
## .wx5 0.631 0.057 10.996 0.000 0.518 0.743
## .wy5 0.412 0.037 10.992 0.000 0.339 0.486
## .wm5 0.469 0.043 10.988 0.000 0.385 0.552
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.688 0.688
## 0.373 0.373
## 0.595 0.595
## 0.691 0.691
## 0.459 0.459
## 0.535 0.535
## 0.721 0.721
## 0.390 0.390
## 0.464 0.464
## 0.645 0.645
## 0.387 0.387
## 0.475 0.475
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b1) Positive emotions at time t predict sleep at time t+1, b = .087, p < .001
# Same model as above code, but fit with d_black dataset this time
PEmoSleepCLPM_b.fit <- lavaan(PEmoSleepCLPM, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoSleepCLPM_b.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 41 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 79
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 371.547
## Degrees of freedom 72
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1880.801
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.831
## Tucker-Lewis Index (TLI) 0.754
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5111.730
## Loglikelihood unrestricted model (H1) -4925.956
##
## Akaike (AIC) 10349.460
## Bayesian (BIC) 10612.670
## Sample-size adjusted Bayesian (BIC) 10412.714
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.093
## 90 Percent confidence interval - lower 0.084
## 90 Percent confidence interval - upper 0.102
## P-value RMSEA <= 0.05 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.099
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.999 1.000
##
## 0.998 1.000
##
## 1.008 1.000
##
## 1.006 1.000
##
## 0.999 1.000
##
## 0.973 1.000
##
## 0.980 1.000
##
## 1.001 1.000
##
## 1.021 1.000
##
## 0.989 1.000
##
## 0.997 1.000
##
## 0.996 1.000
##
## 0.996 1.000
##
## 0.986 1.000
##
## 0.999 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.591 0.046 12.792 0.000 0.501 0.682
## wm1 (b1) 0.087 0.025 3.528 0.000 0.039 0.136
## wy3 ~
## wx1 (cp1) -0.034 0.030 -1.163 0.245 -0.092 0.024
## wy2 0.667 0.048 13.926 0.000 0.573 0.761
## wm2 (b1) 0.087 0.025 3.528 0.000 0.039 0.136
## wy4 ~
## wx2 (cp1) -0.034 0.030 -1.163 0.245 -0.092 0.024
## wy3 0.695 0.050 13.948 0.000 0.597 0.793
## wm3 (b1) 0.087 0.025 3.528 0.000 0.039 0.136
## wy5 ~
## wx3 (cp1) -0.034 0.030 -1.163 0.245 -0.092 0.024
## wy4 0.727 0.043 17.034 0.000 0.643 0.810
## wm4 (b1) 0.087 0.025 3.528 0.000 0.039 0.136
## wx2 ~
## wx1 0.256 0.061 4.175 0.000 0.136 0.376
## wm1 (b2) -0.020 0.030 -0.670 0.503 -0.080 0.039
## wx3 ~
## wx2 0.538 0.059 9.115 0.000 0.422 0.654
## wy1 (cp2) -0.016 0.037 -0.428 0.668 -0.087 0.056
## wm2 (b2) -0.020 0.030 -0.670 0.503 -0.080 0.039
## wx4 ~
## wx3 0.463 0.064 7.248 0.000 0.338 0.588
## wy2 (cp2) -0.016 0.037 -0.428 0.668 -0.087 0.056
## wm3 (b2) -0.020 0.030 -0.670 0.503 -0.080 0.039
## wx5 ~
## wx4 0.490 0.060 8.238 0.000 0.374 0.607
## wy3 (cp2) -0.016 0.037 -0.428 0.668 -0.087 0.056
## wm4 (b2) -0.020 0.030 -0.670 0.503 -0.080 0.039
## wm2 ~
## wx1 (a1) -0.027 0.024 -1.147 0.251 -0.074 0.019
## wy1 (a2) 0.043 0.025 1.742 0.082 -0.005 0.091
## wm1 0.525 0.051 10.254 0.000 0.425 0.626
## wm3 ~
## wx2 (a1) -0.027 0.024 -1.147 0.251 -0.074 0.019
## wy2 (a2) 0.043 0.025 1.742 0.082 -0.005 0.091
## wm2 0.711 0.045 15.844 0.000 0.623 0.799
## wm4 ~
## wx3 (a1) -0.027 0.024 -1.147 0.251 -0.074 0.019
## wy3 (a2) 0.043 0.025 1.742 0.082 -0.005 0.091
## wm3 0.732 0.045 16.278 0.000 0.643 0.820
## wm5 ~
## wx4 (a1) -0.027 0.024 -1.147 0.251 -0.074 0.019
## wy4 (a2) 0.043 0.025 1.742 0.082 -0.005 0.091
## wm4 0.727 0.049 14.819 0.000 0.631 0.824
## Std.lv Std.all
##
## 0.587 0.587
## 0.089 0.089
##
## -0.034 -0.034
## 0.653 0.653
## 0.087 0.087
##
## -0.034 -0.034
## 0.681 0.681
## 0.085 0.085
##
## -0.035 -0.035
## 0.750 0.750
## 0.087 0.087
##
## 0.256 0.256
## -0.020 -0.020
##
## 0.533 0.533
## -0.015 -0.015
## -0.020 -0.020
##
## 0.464 0.464
## -0.015 -0.015
## -0.020 -0.020
##
## 0.494 0.494
## -0.016 -0.016
## -0.020 -0.020
##
## -0.028 -0.028
## 0.042 0.042
## 0.526 0.526
##
## -0.028 -0.028
## 0.042 0.042
## 0.711 0.711
##
## -0.028 -0.028
## 0.043 0.043
## 0.739 0.739
##
## -0.028 -0.028
## 0.044 0.044
## 0.718 0.718
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.055 0.046 1.203 0.229 -0.034 0.144
## wm1 -0.007 0.047 -0.157 0.875 -0.099 0.084
## wy1 ~~
## wm1 0.304 0.047 6.404 0.000 0.211 0.397
## .wx2 ~~
## .wy2 -0.006 0.045 -0.143 0.886 -0.095 0.082
## .wx3 ~~
## .wy3 -0.038 0.042 -0.898 0.369 -0.121 0.045
## .wx4 ~~
## .wy4 -0.019 0.045 -0.414 0.679 -0.107 0.070
## .wx5 ~~
## .wy5 0.084 0.039 2.120 0.034 0.006 0.161
## .wx2 ~~
## .wm2 -0.011 0.049 -0.216 0.829 -0.107 0.085
## .wx3 ~~
## .wm3 -0.024 0.039 -0.628 0.530 -0.101 0.052
## .wx4 ~~
## .wm4 0.013 0.039 0.326 0.744 -0.064 0.090
## .wx5 ~~
## .wm5 0.020 0.041 0.488 0.626 -0.061 0.101
## .wy2 ~~
## .wm2 0.091 0.040 2.296 0.022 0.013 0.169
## .wy3 ~~
## .wm3 0.110 0.034 3.229 0.001 0.043 0.177
## .wy4 ~~
## .wm4 0.052 0.032 1.621 0.105 -0.011 0.115
## .wy5 ~~
## .wm5 0.030 0.029 1.019 0.308 -0.028 0.088
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.056 0.056
## -0.007 -0.007
##
## 0.313 0.313
##
## -0.009 -0.009
##
## -0.061 -0.061
##
## -0.029 -0.029
##
## 0.157 0.157
##
## -0.013 -0.013
##
## -0.042 -0.042
##
## 0.022 0.022
##
## 0.034 0.034
##
## 0.142 0.142
##
## 0.219 0.219
##
## 0.113 0.113
##
## 0.072 0.072
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.007 0.047 -0.157 0.875 -0.099 0.085
## .LadderDif.2 0.005 0.059 0.078 0.938 -0.110 0.119
## .LadderDif.3 0.003 0.065 0.045 0.964 -0.124 0.129
## .LadderDif.4 0.020 0.068 0.292 0.770 -0.113 0.153
## .LadderDif.5 0.009 0.069 0.134 0.893 -0.127 0.145
## .gSleep.1 3.022 0.045 66.811 0.000 2.933 3.110
## .gSleep.2 3.036 0.054 56.636 0.000 2.931 3.141
## .gSleep.3 3.188 0.061 52.180 0.000 3.068 3.307
## .gSleep.4 3.275 0.066 49.577 0.000 3.146 3.405
## .gSleep.5 3.269 0.066 49.597 0.000 3.140 3.398
## .posEmo.1 -0.002 0.046 -0.039 0.969 -0.093 0.089
## .posEmo.2 -0.008 0.056 -0.141 0.888 -0.117 0.101
## .posEmo.3 -0.006 0.061 -0.092 0.926 -0.124 0.113
## .posEmo.4 -0.010 0.063 -0.165 0.869 -0.135 0.114
## .posEmo.5 -0.014 0.067 -0.212 0.832 -0.145 0.117
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.007 -0.007
## 0.005 0.005
## 0.003 0.003
## 0.020 0.020
## 0.009 0.009
## 3.022 3.107
## 3.036 3.098
## 3.188 3.184
## 3.275 3.206
## 3.269 3.305
## -0.002 -0.002
## -0.008 -0.008
## -0.006 -0.006
## -0.010 -0.011
## -0.014 -0.014
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.998 0.066 15.019 0.000 0.868 1.128
## wy1 0.946 0.062 15.174 0.000 0.824 1.068
## wm1 0.995 0.066 15.110 0.000 0.866 1.124
## .wx2 0.931 0.079 11.802 0.000 0.776 1.085
## .wy2 0.591 0.051 11.590 0.000 0.491 0.691
## .wm2 0.702 0.061 11.599 0.000 0.583 0.821
## .wx3 0.726 0.068 10.689 0.000 0.593 0.859
## .wy3 0.538 0.050 10.692 0.000 0.439 0.636
## .wm3 0.471 0.044 10.686 0.000 0.385 0.557
## .wx4 0.791 0.077 10.327 0.000 0.641 0.941
## .wy4 0.511 0.050 10.298 0.000 0.413 0.608
## .wm4 0.416 0.040 10.308 0.000 0.337 0.496
## .wx5 0.752 0.075 10.087 0.000 0.606 0.898
## .wy5 0.376 0.037 10.068 0.000 0.302 0.449
## .wm5 0.459 0.046 10.087 0.000 0.370 0.549
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.934 0.934
## 0.616 0.616
## 0.707 0.707
## 0.715 0.715
## 0.537 0.537
## 0.475 0.475
## 0.782 0.782
## 0.489 0.489
## 0.428 0.428
## 0.754 0.754
## 0.384 0.384
## 0.460 0.460
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.08, p < .001
(b1) Positive emotions at time t predict sleep at time t+1, b = .047, p = .017
C’ path is still significant, b = -.05, p = .037
a2 path is also significant, and its CIs overlap with a1 CIs
PEmoSleepCLPM_2AR <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gSleep.1 + 1*gSleep.2 + 1*gSleep.3 + 1*gSleep.4 + 1*gSleep.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gSleep.1
wy2 =~ 1*gSleep.2
wy3 =~ 1*gSleep.3
wy4 =~ 1*gSleep.4
wy5 =~ 1*gSleep.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoSleepCLPM_w2AR.fit <- lavaan(PEmoSleepCLPM_2AR, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoSleepCLPM_w2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 157.477
## Degrees of freedom 63
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2666.889
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.963
## Tucker-Lewis Index (TLI) 0.939
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5367.928
## Loglikelihood unrestricted model (H1) -5289.189
##
## Akaike (AIC) 10879.855
## Bayesian (BIC) 11180.667
## Sample-size adjusted Bayesian (BIC) 10952.146
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056
## 90 Percent confidence interval - lower 0.045
## 90 Percent confidence interval - upper 0.067
## P-value RMSEA <= 0.05 0.183
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 1.000 1.000
##
## 0.996 1.000
##
## 1.002 1.000
##
## 0.986 1.000
##
## 0.989 1.000
##
## 0.994 1.000
##
## 0.945 1.000
##
## 1.018 1.000
##
## 1.024 1.000
##
## 1.004 1.000
##
## 0.986 1.000
##
## 0.986 1.000
##
## 0.980 1.000
##
## 0.991 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.783 0.034 23.015 0.000 0.716 0.850
## wm1 (b1) 0.047 0.020 2.384 0.017 0.008 0.086
## wy3 ~
## wx1 (cp1) -0.049 0.024 -2.088 0.037 -0.096 -0.003
## wy2 0.389 0.063 6.133 0.000 0.265 0.514
## wm2 (b1) 0.047 0.020 2.384 0.017 0.008 0.086
## wy1 0.352 0.064 5.527 0.000 0.227 0.477
## wy4 ~
## wx2 (cp1) -0.049 0.024 -2.088 0.037 -0.096 -0.003
## wy3 0.599 0.060 9.913 0.000 0.480 0.717
## wm3 (b1) 0.047 0.020 2.384 0.017 0.008 0.086
## wy2 0.270 0.058 4.643 0.000 0.156 0.384
## wy5 ~
## wx3 (cp1) -0.049 0.024 -2.088 0.037 -0.096 -0.003
## wy4 0.570 0.061 9.400 0.000 0.451 0.689
## wm4 (b1) 0.047 0.020 2.384 0.017 0.008 0.086
## wy3 0.264 0.065 4.047 0.000 0.136 0.391
## wx2 ~
## wx1 0.544 0.046 11.715 0.000 0.453 0.635
## wm1 (b2) -0.041 0.025 -1.689 0.091 -0.089 0.007
## wx3 ~
## wx2 0.286 0.058 4.904 0.000 0.172 0.401
## wy1 (cp2) -0.042 0.029 -1.443 0.149 -0.099 0.015
## wm2 (b2) -0.041 0.025 -1.689 0.091 -0.089 0.007
## wx1 0.417 0.057 7.292 0.000 0.305 0.530
## wx4 ~
## wx3 0.299 0.060 4.966 0.000 0.181 0.417
## wy2 (cp2) -0.042 0.029 -1.443 0.149 -0.099 0.015
## wm3 (b2) -0.041 0.025 -1.689 0.091 -0.089 0.007
## wx2 0.385 0.062 6.167 0.000 0.263 0.507
## wx5 ~
## wx4 0.326 0.054 6.022 0.000 0.220 0.432
## wy3 (cp2) -0.042 0.029 -1.443 0.149 -0.099 0.015
## wm4 (b2) -0.041 0.025 -1.689 0.091 -0.089 0.007
## wx3 0.443 0.053 8.285 0.000 0.338 0.548
## wm2 ~
## wx1 (a1) -0.084 0.022 -3.883 0.000 -0.127 -0.042
## wy1 (a2) 0.067 0.022 3.024 0.002 0.023 0.110
## wm1 0.568 0.042 13.589 0.000 0.486 0.650
## wm3 ~
## wx2 (a1) -0.084 0.022 -3.883 0.000 -0.127 -0.042
## wy2 (a2) 0.067 0.022 3.024 0.002 0.023 0.110
## wm2 0.519 0.056 9.322 0.000 0.410 0.628
## wm1 0.187 0.057 3.261 0.001 0.075 0.299
## wm4 ~
## wx3 (a1) -0.084 0.022 -3.883 0.000 -0.127 -0.042
## wy3 (a2) 0.067 0.022 3.024 0.002 0.023 0.110
## wm3 0.505 0.055 9.149 0.000 0.397 0.613
## wm2 0.258 0.055 4.643 0.000 0.149 0.366
## wm5 ~
## wx4 (a1) -0.084 0.022 -3.883 0.000 -0.127 -0.042
## wy4 (a2) 0.067 0.022 3.024 0.002 0.023 0.110
## wm4 0.341 0.057 5.996 0.000 0.230 0.453
## wm3 0.467 0.056 8.312 0.000 0.357 0.577
## Std.lv Std.all
##
## 0.779 0.779
## 0.048 0.048
##
## -0.052 -0.052
## 0.410 0.410
## 0.049 0.049
## 0.369 0.369
##
## -0.049 -0.049
## 0.556 0.556
## 0.046 0.046
## 0.264 0.264
##
## -0.048 -0.048
## 0.566 0.566
## 0.045 0.045
## 0.243 0.243
##
## 0.544 0.544
## -0.042 -0.042
##
## 0.288 0.288
## -0.042 -0.042
## -0.041 -0.041
## 0.419 0.419
##
## 0.297 0.297
## -0.042 -0.042
## -0.041 -0.041
## 0.384 0.384
##
## 0.331 0.331
## -0.040 -0.040
## -0.041 -0.041
## 0.447 0.447
##
## -0.085 -0.085
## 0.067 0.067
## 0.579 0.579
##
## -0.085 -0.085
## 0.067 0.067
## 0.518 0.518
## 0.190 0.190
##
## -0.085 -0.085
## 0.064 0.064
## 0.508 0.508
## 0.259 0.259
##
## -0.085 -0.085
## 0.068 0.068
## 0.337 0.337
## 0.465 0.465
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.253 0.048 -5.222 0.000 -0.348 -0.158
## wm1 -0.254 0.049 -5.159 0.000 -0.351 -0.158
## wy1 ~~
## wm1 0.253 0.049 5.195 0.000 0.158 0.349
## .wx2 ~~
## .wy2 0.011 0.028 0.392 0.695 -0.044 0.066
## .wx3 ~~
## .wy3 0.009 0.029 0.317 0.751 -0.047 0.065
## .wx4 ~~
## .wy4 0.037 0.031 1.190 0.234 -0.024 0.098
## .wx5 ~~
## .wy5 -0.032 0.029 -1.110 0.267 -0.088 0.024
## .wx2 ~~
## .wm2 -0.046 0.035 -1.301 0.193 -0.115 0.023
## .wx3 ~~
## .wm3 0.009 0.033 0.268 0.789 -0.056 0.073
## .wx4 ~~
## .wm4 0.016 0.032 0.500 0.617 -0.047 0.079
## .wx5 ~~
## .wm5 0.003 0.027 0.125 0.901 -0.050 0.057
## .wy2 ~~
## .wm2 0.001 0.027 0.053 0.958 -0.051 0.053
## .wy3 ~~
## .wm3 0.084 0.027 3.123 0.002 0.031 0.137
## .wy4 ~~
## .wm4 0.014 0.025 0.564 0.572 -0.035 0.064
## .wy5 ~~
## .wm5 0.010 0.025 0.414 0.679 -0.038 0.058
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.256 -0.256
## -0.253 -0.253
##
## 0.255 0.255
##
## 0.022 0.022
##
## 0.020 0.020
##
## 0.076 0.076
##
## -0.073 -0.073
##
## -0.072 -0.072
##
## 0.016 0.016
##
## 0.032 0.032
##
## 0.008 0.008
##
## 0.003 0.003
##
## 0.195 0.195
##
## 0.036 0.036
##
## 0.027 0.027
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.005 0.047 0.107 0.915 -0.087 0.097
## .LadderDif.2 -0.012 0.051 -0.228 0.820 -0.112 0.089
## .LadderDif.3 -0.032 0.055 -0.591 0.555 -0.140 0.075
## .LadderDif.4 -0.013 0.059 -0.227 0.820 -0.129 0.102
## .LadderDif.5 -0.019 0.059 -0.326 0.744 -0.135 0.096
## .gSleep.1 2.758 0.046 60.202 0.000 2.668 2.847
## .gSleep.2 2.843 0.049 58.587 0.000 2.748 2.939
## .gSleep.3 2.926 0.050 58.766 0.000 2.828 3.023
## .gSleep.4 2.888 0.056 51.185 0.000 2.777 2.998
## .gSleep.5 2.921 0.059 49.442 0.000 2.805 3.037
## .posEmo.1 -0.006 0.047 -0.133 0.894 -0.099 0.086
## .posEmo.2 -0.012 0.050 -0.247 0.805 -0.110 0.086
## .posEmo.3 0.001 0.054 0.024 0.981 -0.105 0.107
## .posEmo.4 -0.003 0.056 -0.055 0.956 -0.113 0.107
## .posEmo.5 0.014 0.058 0.240 0.811 -0.099 0.127
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.005 0.005
## -0.012 -0.012
## -0.032 -0.033
## -0.013 -0.013
## -0.019 -0.020
## 2.758 2.788
## 2.843 2.860
## 2.926 3.098
## 2.888 2.838
## 2.921 2.853
## -0.006 -0.006
## -0.012 -0.013
## 0.001 0.001
## -0.003 -0.003
## 0.014 0.014
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 1.000 0.067 14.907 0.000 0.869 1.132
## wy1 0.978 0.065 14.946 0.000 0.850 1.106
## wm1 1.009 0.068 14.822 0.000 0.876 1.142
## .wx2 0.692 0.053 12.938 0.000 0.587 0.796
## .wy2 0.368 0.029 12.477 0.000 0.310 0.426
## .wm2 0.588 0.047 12.571 0.000 0.496 0.680
## .wx3 0.573 0.050 11.532 0.000 0.475 0.670
## .wy3 0.370 0.032 11.479 0.000 0.307 0.433
## .wm3 0.501 0.043 11.673 0.000 0.417 0.585
## .wx4 0.620 0.055 11.182 0.000 0.511 0.728
## .wy4 0.384 0.034 11.180 0.000 0.317 0.452
## .wm4 0.413 0.037 11.193 0.000 0.340 0.485
## .wx5 0.495 0.045 10.997 0.000 0.407 0.583
## .wy5 0.386 0.035 10.966 0.000 0.317 0.455
## .wm5 0.366 0.033 10.991 0.000 0.301 0.431
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.691 0.691
## 0.372 0.372
## 0.605 0.605
## 0.578 0.578
## 0.414 0.414
## 0.515 0.515
## 0.618 0.618
## 0.371 0.371
## 0.430 0.430
## 0.509 0.509
## 0.368 0.368
## 0.373 0.373
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(PEmoSleepCLPM_w2AR.fit, PEmoSleepCLPM_w.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PEmoSleepCLPM_w2AR.fit 63 10880 11181 157.48
## PEmoSleepCLPM_w.fit 72 11155 11418 450.82 293.34 9 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b1) Positive emotions at time t predict sleep at time t+1, b = .07, p = .001
# Same model as above code, but fit with d_black dataset this time
PEmoSleepCLPM_b2AR.fit <- lavaan(PEmoSleepCLPM_2AR, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoSleepCLPM_b2AR.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 42 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 88
## Number of equality constraints 16
##
## Number of observations 482
## Number of missing patterns 11
##
## Model Test User Model:
##
## Test statistic 140.842
## Degrees of freedom 63
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1880.801
## Degrees of freedom 105
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956
## Tucker-Lewis Index (TLI) 0.927
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4996.378
## Loglikelihood unrestricted model (H1) -4925.956
##
## Akaike (AIC) 10136.755
## Bayesian (BIC) 10437.567
## Sample-size adjusted Bayesian (BIC) 10209.046
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.051
## 90 Percent confidence interval - lower 0.039
## 90 Percent confidence interval - upper 0.062
## P-value RMSEA <= 0.05 0.446
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 1.000 1.000
##
## 0.999 1.000
##
## 1.011 1.000
##
## 1.011 1.000
##
## 1.014 1.000
##
## 0.973 1.000
##
## 0.978 1.000
##
## 0.986 1.000
##
## 1.007 1.000
##
## 0.976 1.000
##
## 0.996 1.000
##
## 0.996 1.000
##
## 0.986 1.000
##
## 0.974 1.000
##
## 0.986 1.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wy2 ~
## wy1 0.592 0.046 12.847 0.000 0.502 0.682
## wm1 (b1) 0.077 0.024 3.263 0.001 0.031 0.123
## wy3 ~
## wx1 (cp1) -0.031 0.027 -1.150 0.250 -0.083 0.022
## wy2 0.505 0.060 8.426 0.000 0.388 0.623
## wm2 (b1) 0.077 0.024 3.263 0.001 0.031 0.123
## wy1 0.251 0.059 4.221 0.000 0.134 0.367
## wy4 ~
## wx2 (cp1) -0.031 0.027 -1.150 0.250 -0.083 0.022
## wy3 0.457 0.062 7.372 0.000 0.336 0.579
## wm3 (b1) 0.077 0.024 3.263 0.001 0.031 0.123
## wy2 0.353 0.061 5.835 0.000 0.235 0.472
## wy5 ~
## wx3 (cp1) -0.031 0.027 -1.150 0.250 -0.083 0.022
## wy4 0.483 0.056 8.684 0.000 0.374 0.593
## wm4 (b1) 0.077 0.024 3.263 0.001 0.031 0.123
## wy3 0.349 0.057 6.160 0.000 0.238 0.461
## wx2 ~
## wx1 0.265 0.061 4.368 0.000 0.146 0.384
## wm1 (b2) -0.006 0.029 -0.220 0.826 -0.064 0.051
## wx3 ~
## wx2 0.499 0.060 8.301 0.000 0.381 0.617
## wy1 (cp2) -0.005 0.034 -0.145 0.885 -0.071 0.061
## wm2 (b2) -0.006 0.029 -0.220 0.826 -0.064 0.051
## wx1 0.175 0.062 2.810 0.005 0.053 0.297
## wx4 ~
## wx3 0.321 0.072 4.472 0.000 0.180 0.462
## wy2 (cp2) -0.005 0.034 -0.145 0.885 -0.071 0.061
## wm3 (b2) -0.006 0.029 -0.220 0.826 -0.064 0.051
## wx2 0.279 0.072 3.870 0.000 0.138 0.421
## wx5 ~
## wx4 0.303 0.061 4.947 0.000 0.183 0.423
## wy3 (cp2) -0.005 0.034 -0.145 0.885 -0.071 0.061
## wm4 (b2) -0.006 0.029 -0.220 0.826 -0.064 0.051
## wx3 0.430 0.065 6.658 0.000 0.303 0.556
## wm2 ~
## wx1 (a1) -0.028 0.023 -1.213 0.225 -0.072 0.017
## wy1 (a2) 0.023 0.023 0.997 0.319 -0.023 0.069
## wm1 0.527 0.051 10.248 0.000 0.426 0.628
## wm3 ~
## wx2 (a1) -0.028 0.023 -1.213 0.225 -0.072 0.017
## wy2 (a2) 0.023 0.023 0.997 0.319 -0.023 0.069
## wm2 0.579 0.053 10.987 0.000 0.475 0.682
## wm1 0.237 0.054 4.420 0.000 0.132 0.342
## wm4 ~
## wx3 (a1) -0.028 0.023 -1.213 0.225 -0.072 0.017
## wy3 (a2) 0.023 0.023 0.997 0.319 -0.023 0.069
## wm3 0.501 0.064 7.856 0.000 0.376 0.626
## wm2 0.311 0.062 4.998 0.000 0.189 0.433
## wm5 ~
## wx4 (a1) -0.028 0.023 -1.213 0.225 -0.072 0.017
## wy4 (a2) 0.023 0.023 0.997 0.319 -0.023 0.069
## wm4 0.367 0.066 5.595 0.000 0.239 0.496
## wm3 0.473 0.064 7.373 0.000 0.347 0.599
## Std.lv Std.all
##
## 0.589 0.589
## 0.078 0.078
##
## -0.031 -0.031
## 0.501 0.501
## 0.078 0.078
## 0.247 0.247
##
## -0.030 -0.030
## 0.448 0.448
## 0.075 0.075
## 0.343 0.343
##
## -0.032 -0.032
## 0.499 0.499
## 0.077 0.077
## 0.353 0.353
##
## 0.266 0.266
## -0.006 -0.006
##
## 0.493 0.493
## -0.005 -0.005
## -0.006 -0.006
## 0.173 0.173
##
## 0.321 0.321
## -0.005 -0.005
## -0.006 -0.006
## 0.276 0.276
##
## 0.302 0.302
## -0.005 -0.005
## -0.006 -0.006
## 0.429 0.429
##
## -0.028 -0.028
## 0.023 0.023
## 0.527 0.527
##
## -0.028 -0.028
## 0.023 0.023
## 0.584 0.584
## 0.239 0.239
##
## -0.029 -0.029
## 0.024 0.024
## 0.507 0.507
## 0.318 0.318
##
## -0.028 -0.028
## 0.024 0.024
## 0.363 0.363
## 0.473 0.473
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.058 0.046 1.260 0.208 -0.032 0.147
## wm1 -0.006 0.047 -0.137 0.891 -0.098 0.085
## wy1 ~~
## wm1 0.302 0.047 6.394 0.000 0.210 0.395
## .wx2 ~~
## .wy2 -0.008 0.045 -0.167 0.867 -0.096 0.081
## .wx3 ~~
## .wy3 -0.037 0.039 -0.959 0.338 -0.114 0.039
## .wx4 ~~
## .wy4 -0.037 0.040 -0.907 0.364 -0.115 0.042
## .wx5 ~~
## .wy5 0.071 0.032 2.247 0.025 0.009 0.134
## .wx2 ~~
## .wm2 -0.012 0.049 -0.244 0.807 -0.108 0.084
## .wx3 ~~
## .wm3 -0.012 0.036 -0.320 0.749 -0.083 0.060
## .wx4 ~~
## .wm4 0.017 0.036 0.456 0.648 -0.055 0.088
## .wx5 ~~
## .wm5 -0.043 0.034 -1.260 0.208 -0.109 0.024
## .wy2 ~~
## .wm2 0.094 0.040 2.362 0.018 0.016 0.172
## .wy3 ~~
## .wm3 0.079 0.031 2.508 0.012 0.017 0.140
## .wy4 ~~
## .wm4 0.040 0.029 1.388 0.165 -0.016 0.096
## .wy5 ~~
## .wm5 -0.009 0.024 -0.369 0.712 -0.057 0.039
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.059 0.059
## -0.006 -0.006
##
## 0.312 0.312
##
## -0.010 -0.010
##
## -0.064 -0.064
##
## -0.064 -0.064
##
## 0.161 0.161
##
## -0.015 -0.015
##
## -0.021 -0.021
##
## 0.031 0.031
##
## -0.090 -0.090
##
## 0.146 0.146
##
## 0.172 0.172
##
## 0.097 0.097
##
## -0.026 -0.026
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.009 0.047 -0.182 0.855 -0.101 0.084
## .LadderDif.2 0.005 0.059 0.081 0.935 -0.110 0.120
## .LadderDif.3 -0.001 0.064 -0.017 0.986 -0.126 0.124
## .LadderDif.4 0.019 0.067 0.277 0.782 -0.113 0.150
## .LadderDif.5 0.018 0.068 0.263 0.792 -0.116 0.152
## .gSleep.1 3.020 0.045 66.852 0.000 2.932 3.109
## .gSleep.2 3.037 0.053 56.772 0.000 2.932 3.142
## .gSleep.3 3.184 0.058 54.617 0.000 3.070 3.299
## .gSleep.4 3.264 0.062 52.606 0.000 3.143 3.386
## .gSleep.5 3.252 0.062 52.842 0.000 3.132 3.373
## .posEmo.1 -0.003 0.046 -0.075 0.941 -0.094 0.087
## .posEmo.2 -0.007 0.056 -0.118 0.906 -0.116 0.103
## .posEmo.3 -0.010 0.058 -0.172 0.863 -0.124 0.104
## .posEmo.4 -0.015 0.060 -0.252 0.801 -0.133 0.102
## .posEmo.5 -0.022 0.062 -0.351 0.725 -0.144 0.100
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.009 -0.009
## 0.005 0.005
## -0.001 -0.001
## 0.019 0.018
## 0.018 0.018
## 3.020 3.105
## 3.037 3.107
## 3.184 3.230
## 3.264 3.242
## 3.252 3.333
## -0.003 -0.003
## -0.007 -0.007
## -0.010 -0.010
## -0.015 -0.016
## -0.022 -0.022
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 1.000 0.067 14.987 0.000 0.869 1.131
## wy1 0.946 0.062 15.182 0.000 0.824 1.068
## wm1 0.992 0.065 15.147 0.000 0.863 1.120
## .wx2 0.927 0.079 11.801 0.000 0.773 1.081
## .wy2 0.591 0.051 11.604 0.000 0.491 0.691
## .wm2 0.707 0.061 11.595 0.000 0.587 0.826
## .wx3 0.697 0.065 10.646 0.000 0.569 0.826
## .wy3 0.490 0.046 10.609 0.000 0.399 0.581
## .wm3 0.427 0.041 10.540 0.000 0.348 0.507
## .wx4 0.742 0.072 10.321 0.000 0.601 0.883
## .wy4 0.442 0.043 10.283 0.000 0.358 0.527
## .wm4 0.376 0.037 10.295 0.000 0.304 0.447
## .wx5 0.619 0.061 10.089 0.000 0.499 0.739
## .wy5 0.318 0.032 10.073 0.000 0.257 0.380
## .wm5 0.365 0.036 10.080 0.000 0.294 0.435
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.929 0.929
## 0.618 0.618
## 0.713 0.713
## 0.682 0.682
## 0.504 0.504
## 0.440 0.440
## 0.725 0.725
## 0.436 0.436
## 0.396 0.396
## 0.602 0.602
## 0.334 0.334
## 0.375 0.375
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Likelihood ratio test suggests the model with additional autoregressive paths has better fit
lavTestLRT(PEmoSleepCLPM_b2AR.fit, PEmoSleepCLPM_b.fit)## Chi-Squared Difference Test
##
## Df AIC BIC Chisq Chisq diff Df diff Pr(>Chisq)
## PEmoSleepCLPM_b2AR.fit 63 10137 10438 140.84
## PEmoSleepCLPM_b.fit 72 10350 10613 371.55 230.7 9 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Adding in income, age, gender, and education as time-invariant predictors to control for their effects. Because these models have control variables, there’s a lot of output and a lot to scroll past before you get to the results of interest. Just a heads up to scroll down until you start seeing “b1” and “cp1” for each model.
(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.053, p = .03
(b1) Positive emotions at time t predict less depression at time t+1, b = -.04, p = .05
c’1 path is nonsignificant
a2 path is also significant, and its CIs overlap with a1 CIs
PEmoDepCLPM_2AR_controls <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*dep.1 + 1*dep.2 + 1*dep.3 + 1*dep.4 + 1*dep.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*dep.1
wy2 =~ 1*dep.2
wy3 =~ 1*dep.3
wy4 =~ 1*dep.4
wy5 =~ 1*dep.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Regression of observed variables on controls (constrained).
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Gen1*GenderBinary
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Gen2*GenderBinary
dep.1 + dep.2 + dep.3 + dep.4 + dep.5 ~ Gen3*GenderBinary
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Edu1*Edu
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Edu2*Edu
dep.1 + dep.2 + dep.3 + dep.4 + dep.5 ~ Edu3*Edu
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Inc1*Income
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Inc2*Income
dep.1 + dep.2 + dep.3 + dep.4 + dep.5 ~ Inc3*Income
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Age1*Age
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Age2*Age
dep.1 + dep.2 + dep.3 + dep.4 + dep.5 ~ Age3*Age
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
options(max.print=1000000)
PEmoDepCLPM_w2AR_controls.fit <- lavaan(PEmoDepCLPM_2AR_controls, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoDepCLPM_w2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 62 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 433 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 202.401
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2760.968
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.965
## Tucker-Lewis Index (TLI) 0.948
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4680.018
## Loglikelihood unrestricted model (H1) -4578.817
##
## Akaike (AIC) 9528.035
## Bayesian (BIC) 9869.977
## Sample-size adjusted Bayesian (BIC) 9603.408
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.044
## 90 Percent confidence interval - lower 0.034
## 90 Percent confidence interval - upper 0.053
## P-value RMSEA <= 0.05 0.863
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.041
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.956 0.956
##
## 0.973 0.958
##
## 0.951 0.956
##
## 0.949 0.956
##
## 0.916 0.953
##
## 0.945 0.953
##
## 0.943 0.953
##
## 0.923 0.951
##
## 0.922 0.951
##
## 0.944 0.953
##
## 0.977 0.980
##
## 0.946 0.979
##
## 0.949 0.979
##
## 0.957 0.979
##
## 0.966 0.980
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) 0.317 0.077 4.122 0.000 0.166 0.467
## LadderDif.2 ~
## GndrBnr (Gen1) 0.317 0.077 4.122 0.000 0.166 0.467
## LadderDif.3 ~
## GndrBnr (Gen1) 0.317 0.077 4.122 0.000 0.166 0.467
## LadderDif.4 ~
## GndrBnr (Gen1) 0.317 0.077 4.122 0.000 0.166 0.467
## LadderDif.5 ~
## GndrBnr (Gen1) 0.317 0.077 4.122 0.000 0.166 0.467
## posEmo.1 ~
## GndrBnr (Gen2) -0.155 0.081 -1.914 0.056 -0.314 0.004
## posEmo.2 ~
## GndrBnr (Gen2) -0.155 0.081 -1.914 0.056 -0.314 0.004
## posEmo.3 ~
## GndrBnr (Gen2) -0.155 0.081 -1.914 0.056 -0.314 0.004
## posEmo.4 ~
## GndrBnr (Gen2) -0.155 0.081 -1.914 0.056 -0.314 0.004
## posEmo.5 ~
## GndrBnr (Gen2) -0.155 0.081 -1.914 0.056 -0.314 0.004
## dep.1 ~
## GndrBnr (Gen3) 0.069 0.084 0.830 0.406 -0.095 0.234
## dep.2 ~
## GndrBnr (Gen3) 0.069 0.084 0.830 0.406 -0.095 0.234
## dep.3 ~
## GndrBnr (Gen3) 0.069 0.084 0.830 0.406 -0.095 0.234
## dep.4 ~
## GndrBnr (Gen3) 0.069 0.084 0.830 0.406 -0.095 0.234
## dep.5 ~
## GndrBnr (Gen3) 0.069 0.084 0.830 0.406 -0.095 0.234
## LadderDif.1 ~
## Edu (Edu1) 0.019 0.041 0.476 0.634 -0.061 0.099
## LadderDif.2 ~
## Edu (Edu1) 0.019 0.041 0.476 0.634 -0.061 0.099
## LadderDif.3 ~
## Edu (Edu1) 0.019 0.041 0.476 0.634 -0.061 0.099
## LadderDif.4 ~
## Edu (Edu1) 0.019 0.041 0.476 0.634 -0.061 0.099
## LadderDif.5 ~
## Edu (Edu1) 0.019 0.041 0.476 0.634 -0.061 0.099
## posEmo.1 ~
## Edu (Edu2) -0.064 0.043 -1.467 0.142 -0.148 0.021
## posEmo.2 ~
## Edu (Edu2) -0.064 0.043 -1.467 0.142 -0.148 0.021
## posEmo.3 ~
## Edu (Edu2) -0.064 0.043 -1.467 0.142 -0.148 0.021
## posEmo.4 ~
## Edu (Edu2) -0.064 0.043 -1.467 0.142 -0.148 0.021
## posEmo.5 ~
## Edu (Edu2) -0.064 0.043 -1.467 0.142 -0.148 0.021
## dep.1 ~
## Edu (Edu3) -0.134 0.045 -3.013 0.003 -0.222 -0.047
## dep.2 ~
## Edu (Edu3) -0.134 0.045 -3.013 0.003 -0.222 -0.047
## dep.3 ~
## Edu (Edu3) -0.134 0.045 -3.013 0.003 -0.222 -0.047
## dep.4 ~
## Edu (Edu3) -0.134 0.045 -3.013 0.003 -0.222 -0.047
## dep.5 ~
## Edu (Edu3) -0.134 0.045 -3.013 0.003 -0.222 -0.047
## LadderDif.1 ~
## Income (Inc1) -0.248 0.041 -6.106 0.000 -0.328 -0.169
## LadderDif.2 ~
## Income (Inc1) -0.248 0.041 -6.106 0.000 -0.328 -0.169
## LadderDif.3 ~
## Income (Inc1) -0.248 0.041 -6.106 0.000 -0.328 -0.169
## LadderDif.4 ~
## Income (Inc1) -0.248 0.041 -6.106 0.000 -0.328 -0.169
## LadderDif.5 ~
## Income (Inc1) -0.248 0.041 -6.106 0.000 -0.328 -0.169
## posEmo.1 ~
## Income (Inc2) 0.157 0.043 3.651 0.000 0.073 0.241
## posEmo.2 ~
## Income (Inc2) 0.157 0.043 3.651 0.000 0.073 0.241
## posEmo.3 ~
## Income (Inc2) 0.157 0.043 3.651 0.000 0.073 0.241
## posEmo.4 ~
## Income (Inc2) 0.157 0.043 3.651 0.000 0.073 0.241
## posEmo.5 ~
## Income (Inc2) 0.157 0.043 3.651 0.000 0.073 0.241
## dep.1 ~
## Income (Inc3) -0.163 0.044 -3.674 0.000 -0.250 -0.076
## dep.2 ~
## Income (Inc3) -0.163 0.044 -3.674 0.000 -0.250 -0.076
## dep.3 ~
## Income (Inc3) -0.163 0.044 -3.674 0.000 -0.250 -0.076
## dep.4 ~
## Income (Inc3) -0.163 0.044 -3.674 0.000 -0.250 -0.076
## dep.5 ~
## Income (Inc3) -0.163 0.044 -3.674 0.000 -0.250 -0.076
## LadderDif.1 ~
## Age (Age1) -0.014 0.039 -0.352 0.725 -0.090 0.062
## LadderDif.2 ~
## Age (Age1) -0.014 0.039 -0.352 0.725 -0.090 0.062
## LadderDif.3 ~
## Age (Age1) -0.014 0.039 -0.352 0.725 -0.090 0.062
## LadderDif.4 ~
## Age (Age1) -0.014 0.039 -0.352 0.725 -0.090 0.062
## LadderDif.5 ~
## Age (Age1) -0.014 0.039 -0.352 0.725 -0.090 0.062
## posEmo.1 ~
## Age (Age2) 0.097 0.041 2.378 0.017 0.017 0.177
## posEmo.2 ~
## Age (Age2) 0.097 0.041 2.378 0.017 0.017 0.177
## posEmo.3 ~
## Age (Age2) 0.097 0.041 2.378 0.017 0.017 0.177
## posEmo.4 ~
## Age (Age2) 0.097 0.041 2.378 0.017 0.017 0.177
## posEmo.5 ~
## Age (Age2) 0.097 0.041 2.378 0.017 0.017 0.177
## dep.1 ~
## Age (Age3) -0.136 0.042 -3.242 0.001 -0.219 -0.054
## dep.2 ~
## Age (Age3) -0.136 0.042 -3.242 0.001 -0.219 -0.054
## dep.3 ~
## Age (Age3) -0.136 0.042 -3.242 0.001 -0.219 -0.054
## dep.4 ~
## Age (Age3) -0.136 0.042 -3.242 0.001 -0.219 -0.054
## dep.5 ~
## Age (Age3) -0.136 0.042 -3.242 0.001 -0.219 -0.054
## wy2 ~
## wy1 0.782 0.037 21.155 0.000 0.709 0.854
## wm1 (b1) -0.038 0.019 -1.962 0.050 -0.076 -0.000
## wy3 ~
## wx1 (cp1) 0.038 0.022 1.691 0.091 -0.006 0.082
## wy2 0.510 0.063 8.151 0.000 0.387 0.633
## wm2 (b1) -0.038 0.019 -1.962 0.050 -0.076 -0.000
## wy1 0.296 0.063 4.705 0.000 0.173 0.420
## wy4 ~
## wx2 (cp1) 0.038 0.022 1.691 0.091 -0.006 0.082
## wy3 0.469 0.069 6.763 0.000 0.333 0.604
## wm3 (b1) -0.038 0.019 -1.962 0.050 -0.076 -0.000
## wy2 0.329 0.068 4.819 0.000 0.195 0.463
## wy5 ~
## wx3 (cp1) 0.038 0.022 1.691 0.091 -0.006 0.082
## wy4 0.535 0.056 9.593 0.000 0.426 0.645
## wm4 (b1) -0.038 0.019 -1.962 0.050 -0.076 -0.000
## wy3 0.366 0.057 6.471 0.000 0.255 0.477
## wx2 ~
## wx1 0.517 0.051 10.232 0.000 0.418 0.616
## wm1 (b2) -0.033 0.027 -1.228 0.219 -0.085 0.020
## wx3 ~
## wx2 0.282 0.062 4.577 0.000 0.161 0.402
## wy1 (cp2) 0.007 0.034 0.219 0.827 -0.059 0.073
## wm2 (b2) -0.033 0.027 -1.228 0.219 -0.085 0.020
## wx1 0.403 0.059 6.840 0.000 0.287 0.518
## wx4 ~
## wx3 0.257 0.064 3.999 0.000 0.131 0.382
## wy2 (cp2) 0.007 0.034 0.219 0.827 -0.059 0.073
## wm3 (b2) -0.033 0.027 -1.228 0.219 -0.085 0.020
## wx2 0.375 0.068 5.534 0.000 0.242 0.507
## wx5 ~
## wx4 0.270 0.057 4.749 0.000 0.159 0.382
## wy3 (cp2) 0.007 0.034 0.219 0.827 -0.059 0.073
## wm4 (b2) -0.033 0.027 -1.228 0.219 -0.085 0.020
## wx3 0.444 0.056 7.977 0.000 0.335 0.554
## wm2 ~
## wx1 (a1) -0.053 0.023 -2.239 0.025 -0.099 -0.007
## wy1 (a2) -0.121 0.026 -4.698 0.000 -0.171 -0.070
## wm1 0.530 0.043 12.252 0.000 0.445 0.614
## wm3 ~
## wx2 (a1) -0.053 0.023 -2.239 0.025 -0.099 -0.007
## wy2 (a2) -0.121 0.026 -4.698 0.000 -0.171 -0.070
## wm2 0.518 0.057 9.007 0.000 0.405 0.630
## wm1 0.163 0.056 2.891 0.004 0.052 0.273
## wm4 ~
## wx3 (a1) -0.053 0.023 -2.239 0.025 -0.099 -0.007
## wy3 (a2) -0.121 0.026 -4.698 0.000 -0.171 -0.070
## wm3 0.494 0.062 7.949 0.000 0.373 0.616
## wm2 0.240 0.063 3.813 0.000 0.117 0.364
## wm5 ~
## wx4 (a1) -0.053 0.023 -2.239 0.025 -0.099 -0.007
## wy4 (a2) -0.121 0.026 -4.698 0.000 -0.171 -0.070
## wm4 0.359 0.058 6.144 0.000 0.244 0.473
## wm3 0.443 0.059 7.554 0.000 0.328 0.558
## Std.lv Std.all
##
## 0.317 0.158
##
## 0.317 0.156
##
## 0.317 0.159
##
## 0.317 0.159
##
## 0.317 0.165
##
## -0.155 -0.078
##
## -0.155 -0.080
##
## -0.155 -0.080
##
## -0.155 -0.079
##
## -0.155 -0.079
##
## 0.069 0.035
##
## 0.069 0.035
##
## 0.069 0.036
##
## 0.069 0.036
##
## 0.069 0.035
##
## 0.019 0.019
##
## 0.019 0.019
##
## 0.019 0.019
##
## 0.019 0.020
##
## 0.019 0.020
##
## -0.064 -0.064
##
## -0.064 -0.066
##
## -0.064 -0.065
##
## -0.064 -0.065
##
## -0.064 -0.064
##
## -0.134 -0.135
##
## -0.134 -0.136
##
## -0.134 -0.138
##
## -0.134 -0.139
##
## -0.134 -0.136
##
## -0.248 -0.249
##
## -0.248 -0.245
##
## -0.248 -0.250
##
## -0.248 -0.250
##
## -0.248 -0.258
##
## 0.157 0.157
##
## 0.157 0.162
##
## 0.157 0.162
##
## 0.157 0.161
##
## 0.157 0.159
##
## -0.163 -0.164
##
## -0.163 -0.165
##
## -0.163 -0.168
##
## -0.163 -0.168
##
## -0.163 -0.165
##
## -0.014 -0.014
##
## -0.014 -0.013
##
## -0.014 -0.014
##
## -0.014 -0.014
##
## -0.014 -0.014
##
## 0.097 0.097
##
## 0.097 0.100
##
## 0.097 0.100
##
## 0.097 0.099
##
## 0.097 0.098
##
## -0.136 -0.137
##
## -0.136 -0.137
##
## -0.136 -0.140
##
## -0.136 -0.140
##
## -0.136 -0.137
##
## 0.784 0.784
## -0.040 -0.040
##
## 0.039 0.039
## 0.521 0.521
## -0.039 -0.039
## 0.303 0.303
##
## 0.040 0.040
## 0.469 0.469
## -0.039 -0.039
## 0.337 0.337
##
## 0.038 0.038
## 0.523 0.523
## -0.039 -0.039
## 0.358 0.358
##
## 0.508 0.508
## -0.033 -0.033
##
## 0.288 0.288
## 0.007 0.007
## -0.033 -0.033
## 0.405 0.405
##
## 0.257 0.257
## 0.007 0.007
## -0.033 -0.033
## 0.384 0.384
##
## 0.280 0.280
## 0.007 0.007
## -0.034 -0.034
## 0.461 0.461
##
## -0.053 -0.053
## -0.120 -0.120
## 0.547 0.547
##
## -0.054 -0.054
## -0.120 -0.120
## 0.516 0.516
## 0.168 0.168
##
## -0.052 -0.052
## -0.116 -0.116
## 0.491 0.491
## 0.238 0.238
##
## -0.052 -0.052
## -0.115 -0.115
## 0.356 0.356
## 0.435 0.435
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.224 0.045 5.000 0.000 0.136 0.312
## wm1 -0.182 0.046 -3.972 0.000 -0.272 -0.092
## wy1 ~~
## wm1 -0.260 0.046 -5.642 0.000 -0.351 -0.170
## .wx2 ~~
## .wy2 -0.031 0.028 -1.121 0.262 -0.086 0.023
## .wx3 ~~
## .wy3 -0.026 0.026 -0.992 0.321 -0.078 0.026
## .wx4 ~~
## .wy4 0.016 0.030 0.513 0.608 -0.044 0.075
## .wx5 ~~
## .wy5 0.052 0.025 2.098 0.036 0.003 0.100
## .wx2 ~~
## .wm2 -0.031 0.036 -0.861 0.389 -0.102 0.040
## .wx3 ~~
## .wm3 0.007 0.034 0.212 0.832 -0.059 0.073
## .wx4 ~~
## .wm4 0.001 0.035 0.036 0.971 -0.066 0.069
## .wx5 ~~
## .wm5 -0.013 0.029 -0.453 0.651 -0.070 0.044
## .wy2 ~~
## .wm2 -0.035 0.025 -1.401 0.161 -0.084 0.014
## .wy3 ~~
## .wm3 -0.074 0.025 -2.975 0.003 -0.123 -0.025
## .wy4 ~~
## .wm4 0.041 0.025 1.598 0.110 -0.009 0.091
## .wy5 ~~
## .wm5 -0.046 0.021 -2.184 0.029 -0.087 -0.005
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.248 0.248
## -0.195 -0.195
##
## -0.282 -0.282
##
## -0.065 -0.065
##
## -0.064 -0.064
##
## 0.035 0.035
##
## 0.147 0.147
##
## -0.050 -0.050
##
## 0.014 0.014
##
## 0.002 0.002
##
## -0.031 -0.031
##
## -0.081 -0.081
##
## -0.197 -0.197
##
## 0.109 0.109
##
## -0.152 -0.152
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.153 0.061 -2.534 0.011 -0.272 -0.035
## .LadderDif.2 -0.160 0.067 -2.412 0.016 -0.291 -0.030
## .LadderDif.3 -0.193 0.069 -2.800 0.005 -0.328 -0.058
## .LadderDif.4 -0.173 0.072 -2.403 0.016 -0.315 -0.032
## .LadderDif.5 -0.196 0.071 -2.748 0.006 -0.336 -0.056
## .dep.1 -0.036 0.063 -0.578 0.563 -0.159 0.086
## .dep.2 -0.001 0.065 -0.022 0.982 -0.129 0.126
## .dep.3 0.029 0.067 0.441 0.659 -0.101 0.160
## .dep.4 -0.000 0.069 -0.002 0.998 -0.136 0.135
## .dep.5 0.027 0.071 0.384 0.701 -0.112 0.166
## .posEmo.1 0.079 0.063 1.262 0.207 -0.044 0.202
## .posEmo.2 0.070 0.066 1.065 0.287 -0.059 0.200
## .posEmo.3 0.092 0.070 1.328 0.184 -0.044 0.229
## .posEmo.4 0.084 0.072 1.168 0.243 -0.057 0.225
## .posEmo.5 0.095 0.073 1.301 0.193 -0.048 0.238
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.153 -0.154
## -0.160 -0.158
## -0.193 -0.194
## -0.173 -0.175
## -0.196 -0.204
## -0.036 -0.036
## -0.001 -0.001
## 0.029 0.030
## -0.000 -0.000
## 0.027 0.027
## 0.079 0.079
## 0.070 0.073
## 0.092 0.095
## 0.084 0.086
## 0.095 0.097
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.914 0.062 14.668 0.000 0.792 1.036
## wy1 0.893 0.061 14.693 0.000 0.774 1.012
## wm1 0.954 0.065 14.704 0.000 0.827 1.081
## .wx2 0.695 0.057 12.261 0.000 0.584 0.806
## .wy2 0.326 0.027 12.271 0.000 0.274 0.378
## .wm2 0.566 0.046 12.282 0.000 0.476 0.657
## .wx3 0.562 0.051 10.990 0.000 0.461 0.662
## .wy3 0.298 0.027 10.945 0.000 0.244 0.351
## .wm3 0.476 0.043 10.986 0.000 0.391 0.561
## .wx4 0.611 0.058 10.465 0.000 0.497 0.726
## .wy4 0.329 0.031 10.434 0.000 0.267 0.390
## .wm4 0.423 0.040 10.473 0.000 0.344 0.503
## .wx5 0.488 0.047 10.291 0.000 0.395 0.581
## .wy5 0.253 0.025 10.310 0.000 0.205 0.302
## .wm5 0.359 0.035 10.301 0.000 0.291 0.428
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.734 0.734
## 0.367 0.367
## 0.632 0.632
## 0.621 0.621
## 0.349 0.349
## 0.528 0.528
## 0.679 0.679
## 0.387 0.387
## 0.463 0.463
## 0.581 0.581
## 0.284 0.284
## 0.385 0.385
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b2) Positive emotions at time t predict less depression at time t+1, b = -.09, p < .001
# Same model as above code, but fit with d_black dataset this time
PEmoDepCLPM_b2AR_controls.fit <- lavaan(PEmoDepCLPM_2AR_controls, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoDepCLPM_b2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 53 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 451 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 200.305
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1938.603
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.950
## Tucker-Lewis Index (TLI) 0.925
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4533.102
## Loglikelihood unrestricted model (H1) -4432.949
##
## Akaike (AIC) 9234.204
## Bayesian (BIC) 9579.567
## Sample-size adjusted Bayesian (BIC) 9312.982
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.042
## 90 Percent confidence interval - lower 0.033
## 90 Percent confidence interval - upper 0.052
## P-value RMSEA <= 0.05 0.914
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.049
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## dep.1 1.000 1.000 1.000
## dep.2 1.000 1.000 1.000
## dep.3 1.000 1.000 1.000
## dep.4 1.000 1.000 1.000
## dep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## dep.1 1.000 1.000 1.000
## wy2 =~
## dep.2 1.000 1.000 1.000
## wy3 =~
## dep.3 1.000 1.000 1.000
## wy4 =~
## dep.4 1.000 1.000 1.000
## wy5 =~
## dep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.994 0.985
##
## 0.968 0.984
##
## 0.971 0.984
##
## 0.978 0.984
##
## 0.953 0.984
##
## 0.973 0.978
##
## 0.992 0.979
##
## 0.992 0.979
##
## 0.972 0.978
##
## 0.996 0.979
##
## 0.997 0.994
##
## 0.985 0.993
##
## 0.966 0.993
##
## 0.946 0.993
##
## 0.957 0.993
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) -0.127 0.075 -1.686 0.092 -0.275 0.021
## LadderDif.2 ~
## GndrBnr (Gen1) -0.127 0.075 -1.686 0.092 -0.275 0.021
## LadderDif.3 ~
## GndrBnr (Gen1) -0.127 0.075 -1.686 0.092 -0.275 0.021
## LadderDif.4 ~
## GndrBnr (Gen1) -0.127 0.075 -1.686 0.092 -0.275 0.021
## LadderDif.5 ~
## GndrBnr (Gen1) -0.127 0.075 -1.686 0.092 -0.275 0.021
## posEmo.1 ~
## GndrBnr (Gen2) -0.209 0.085 -2.468 0.014 -0.375 -0.043
## posEmo.2 ~
## GndrBnr (Gen2) -0.209 0.085 -2.468 0.014 -0.375 -0.043
## posEmo.3 ~
## GndrBnr (Gen2) -0.209 0.085 -2.468 0.014 -0.375 -0.043
## posEmo.4 ~
## GndrBnr (Gen2) -0.209 0.085 -2.468 0.014 -0.375 -0.043
## posEmo.5 ~
## GndrBnr (Gen2) -0.209 0.085 -2.468 0.014 -0.375 -0.043
## dep.1 ~
## GndrBnr (Gen3) -0.049 0.088 -0.557 0.578 -0.221 0.123
## dep.2 ~
## GndrBnr (Gen3) -0.049 0.088 -0.557 0.578 -0.221 0.123
## dep.3 ~
## GndrBnr (Gen3) -0.049 0.088 -0.557 0.578 -0.221 0.123
## dep.4 ~
## GndrBnr (Gen3) -0.049 0.088 -0.557 0.578 -0.221 0.123
## dep.5 ~
## GndrBnr (Gen3) -0.049 0.088 -0.557 0.578 -0.221 0.123
## LadderDif.1 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.2 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.3 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.4 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.5 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## posEmo.1 ~
## Edu (Edu2) -0.005 0.044 -0.113 0.910 -0.092 0.082
## posEmo.2 ~
## Edu (Edu2) -0.005 0.044 -0.113 0.910 -0.092 0.082
## posEmo.3 ~
## Edu (Edu2) -0.005 0.044 -0.113 0.910 -0.092 0.082
## posEmo.4 ~
## Edu (Edu2) -0.005 0.044 -0.113 0.910 -0.092 0.082
## posEmo.5 ~
## Edu (Edu2) -0.005 0.044 -0.113 0.910 -0.092 0.082
## dep.1 ~
## Edu (Edu3) -0.075 0.046 -1.639 0.101 -0.165 0.015
## dep.2 ~
## Edu (Edu3) -0.075 0.046 -1.639 0.101 -0.165 0.015
## dep.3 ~
## Edu (Edu3) -0.075 0.046 -1.639 0.101 -0.165 0.015
## dep.4 ~
## Edu (Edu3) -0.075 0.046 -1.639 0.101 -0.165 0.015
## dep.5 ~
## Edu (Edu3) -0.075 0.046 -1.639 0.101 -0.165 0.015
## LadderDif.1 ~
## Income (Inc1) -0.112 0.039 -2.914 0.004 -0.188 -0.037
## LadderDif.2 ~
## Income (Inc1) -0.112 0.039 -2.914 0.004 -0.188 -0.037
## LadderDif.3 ~
## Income (Inc1) -0.112 0.039 -2.914 0.004 -0.188 -0.037
## LadderDif.4 ~
## Income (Inc1) -0.112 0.039 -2.914 0.004 -0.188 -0.037
## LadderDif.5 ~
## Income (Inc1) -0.112 0.039 -2.914 0.004 -0.188 -0.037
## posEmo.1 ~
## Income (Inc2) 0.044 0.043 1.003 0.316 -0.042 0.129
## posEmo.2 ~
## Income (Inc2) 0.044 0.043 1.003 0.316 -0.042 0.129
## posEmo.3 ~
## Income (Inc2) 0.044 0.043 1.003 0.316 -0.042 0.129
## posEmo.4 ~
## Income (Inc2) 0.044 0.043 1.003 0.316 -0.042 0.129
## posEmo.5 ~
## Income (Inc2) 0.044 0.043 1.003 0.316 -0.042 0.129
## dep.1 ~
## Income (Inc3) -0.092 0.045 -2.052 0.040 -0.179 -0.004
## dep.2 ~
## Income (Inc3) -0.092 0.045 -2.052 0.040 -0.179 -0.004
## dep.3 ~
## Income (Inc3) -0.092 0.045 -2.052 0.040 -0.179 -0.004
## dep.4 ~
## Income (Inc3) -0.092 0.045 -2.052 0.040 -0.179 -0.004
## dep.5 ~
## Income (Inc3) -0.092 0.045 -2.052 0.040 -0.179 -0.004
## LadderDif.1 ~
## Age (Age1) 0.044 0.038 1.172 0.241 -0.030 0.118
## LadderDif.2 ~
## Age (Age1) 0.044 0.038 1.172 0.241 -0.030 0.118
## LadderDif.3 ~
## Age (Age1) 0.044 0.038 1.172 0.241 -0.030 0.118
## LadderDif.4 ~
## Age (Age1) 0.044 0.038 1.172 0.241 -0.030 0.118
## LadderDif.5 ~
## Age (Age1) 0.044 0.038 1.172 0.241 -0.030 0.118
## posEmo.1 ~
## Age (Age2) 0.039 0.042 0.913 0.361 -0.044 0.122
## posEmo.2 ~
## Age (Age2) 0.039 0.042 0.913 0.361 -0.044 0.122
## posEmo.3 ~
## Age (Age2) 0.039 0.042 0.913 0.361 -0.044 0.122
## posEmo.4 ~
## Age (Age2) 0.039 0.042 0.913 0.361 -0.044 0.122
## posEmo.5 ~
## Age (Age2) 0.039 0.042 0.913 0.361 -0.044 0.122
## dep.1 ~
## Age (Age3) -0.126 0.044 -2.874 0.004 -0.211 -0.040
## dep.2 ~
## Age (Age3) -0.126 0.044 -2.874 0.004 -0.211 -0.040
## dep.3 ~
## Age (Age3) -0.126 0.044 -2.874 0.004 -0.211 -0.040
## dep.4 ~
## Age (Age3) -0.126 0.044 -2.874 0.004 -0.211 -0.040
## dep.5 ~
## Age (Age3) -0.126 0.044 -2.874 0.004 -0.211 -0.040
## wy2 ~
## wy1 0.658 0.049 13.515 0.000 0.563 0.754
## wm1 (b1) -0.086 0.023 -3.722 0.000 -0.132 -0.041
## wy3 ~
## wx1 (cp1) 0.051 0.026 1.955 0.051 -0.000 0.103
## wy2 0.502 0.058 8.641 0.000 0.388 0.616
## wm2 (b1) -0.086 0.023 -3.722 0.000 -0.132 -0.041
## wy1 0.368 0.058 6.345 0.000 0.254 0.482
## wy4 ~
## wx2 (cp1) 0.051 0.026 1.955 0.051 -0.000 0.103
## wy3 0.566 0.066 8.624 0.000 0.437 0.695
## wm3 (b1) -0.086 0.023 -3.722 0.000 -0.132 -0.041
## wy2 0.233 0.066 3.505 0.000 0.102 0.363
## wy5 ~
## wx3 (cp1) 0.051 0.026 1.955 0.051 -0.000 0.103
## wy4 0.555 0.073 7.605 0.000 0.412 0.699
## wm4 (b1) -0.086 0.023 -3.722 0.000 -0.132 -0.041
## wy3 0.293 0.073 4.012 0.000 0.150 0.436
## wx2 ~
## wx1 0.216 0.061 3.528 0.000 0.096 0.336
## wm1 (b2) -0.045 0.032 -1.406 0.160 -0.108 0.018
## wx3 ~
## wx2 0.440 0.066 6.674 0.000 0.311 0.569
## wy1 (cp2) -0.017 0.037 -0.449 0.654 -0.090 0.056
## wm2 (b2) -0.045 0.032 -1.406 0.160 -0.108 0.018
## wx1 0.166 0.064 2.612 0.009 0.042 0.291
## wx4 ~
## wx3 0.291 0.077 3.799 0.000 0.141 0.441
## wy2 (cp2) -0.017 0.037 -0.449 0.654 -0.090 0.056
## wm3 (b2) -0.045 0.032 -1.406 0.160 -0.108 0.018
## wx2 0.230 0.078 2.940 0.003 0.077 0.384
## wx5 ~
## wx4 0.243 0.065 3.764 0.000 0.116 0.369
## wy3 (cp2) -0.017 0.037 -0.449 0.654 -0.090 0.056
## wm4 (b2) -0.045 0.032 -1.406 0.160 -0.108 0.018
## wx3 0.417 0.070 5.992 0.000 0.281 0.553
## wm2 ~
## wx1 (a1) -0.039 0.025 -1.544 0.122 -0.088 0.010
## wy1 (a2) -0.106 0.026 -4.094 0.000 -0.156 -0.055
## wm1 0.505 0.052 9.670 0.000 0.403 0.608
## wm3 ~
## wx2 (a1) -0.039 0.025 -1.544 0.122 -0.088 0.010
## wy2 (a2) -0.106 0.026 -4.094 0.000 -0.156 -0.055
## wm2 0.560 0.053 10.509 0.000 0.456 0.665
## wm1 0.223 0.052 4.302 0.000 0.121 0.325
## wm4 ~
## wx3 (a1) -0.039 0.025 -1.544 0.122 -0.088 0.010
## wy3 (a2) -0.106 0.026 -4.094 0.000 -0.156 -0.055
## wm3 0.420 0.071 5.948 0.000 0.282 0.558
## wm2 0.317 0.068 4.672 0.000 0.184 0.450
## wm5 ~
## wx4 (a1) -0.039 0.025 -1.544 0.122 -0.088 0.010
## wy4 (a2) -0.106 0.026 -4.094 0.000 -0.156 -0.055
## wm4 0.330 0.069 4.754 0.000 0.194 0.466
## wm3 0.444 0.067 6.622 0.000 0.313 0.576
## Std.lv Std.all
##
## -0.127 -0.063
##
## -0.127 -0.065
##
## -0.127 -0.064
##
## -0.127 -0.064
##
## -0.127 -0.065
##
## -0.209 -0.104
##
## -0.209 -0.105
##
## -0.209 -0.108
##
## -0.209 -0.110
##
## -0.209 -0.108
##
## -0.049 -0.025
##
## -0.049 -0.024
##
## -0.049 -0.024
##
## -0.049 -0.025
##
## -0.049 -0.024
##
## -0.074 -0.073
##
## -0.074 -0.075
##
## -0.074 -0.075
##
## -0.074 -0.074
##
## -0.074 -0.076
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## -0.075 -0.076
##
## -0.075 -0.074
##
## -0.075 -0.074
##
## -0.075 -0.076
##
## -0.075 -0.074
##
## -0.112 -0.111
##
## -0.112 -0.114
##
## -0.112 -0.114
##
## -0.112 -0.113
##
## -0.112 -0.116
##
## 0.044 0.043
##
## 0.044 0.044
##
## 0.044 0.045
##
## 0.044 0.046
##
## 0.044 0.045
##
## -0.092 -0.092
##
## -0.092 -0.091
##
## -0.092 -0.091
##
## -0.092 -0.092
##
## -0.092 -0.090
##
## 0.044 0.044
##
## 0.044 0.045
##
## 0.044 0.045
##
## 0.044 0.044
##
## 0.044 0.045
##
## 0.039 0.038
##
## 0.039 0.039
##
## 0.039 0.040
##
## 0.039 0.041
##
## 0.039 0.040
##
## -0.126 -0.126
##
## -0.126 -0.124
##
## -0.126 -0.124
##
## -0.126 -0.126
##
## -0.126 -0.123
##
## 0.646 0.646
## -0.087 -0.087
##
## 0.051 0.051
## 0.502 0.502
## -0.086 -0.086
## 0.361 0.361
##
## 0.051 0.051
## 0.577 0.577
## -0.086 -0.086
## 0.237 0.237
##
## 0.050 0.050
## 0.543 0.543
## -0.082 -0.082
## 0.292 0.292
##
## 0.222 0.222
## -0.047 -0.047
##
## 0.438 0.438
## -0.017 -0.017
## -0.046 -0.046
## 0.170 0.170
##
## 0.289 0.289
## -0.017 -0.017
## -0.045 -0.045
## 0.228 0.228
##
## 0.249 0.249
## -0.017 -0.017
## -0.045 -0.045
## 0.425 0.425
##
## -0.039 -0.039
## -0.104 -0.104
## 0.512 0.512
##
## -0.039 -0.039
## -0.108 -0.108
## 0.571 0.571
## 0.230 0.230
##
## -0.040 -0.040
## -0.111 -0.111
## 0.429 0.429
## 0.330 0.330
##
## -0.039 -0.039
## -0.107 -0.107
## 0.326 0.326
## 0.448 0.448
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.008 0.046 -0.186 0.853 -0.098 0.081
## wm1 -0.012 0.047 -0.256 0.798 -0.104 0.080
## wy1 ~~
## wm1 -0.223 0.047 -4.747 0.000 -0.315 -0.131
## .wx2 ~~
## .wy2 -0.026 0.044 -0.600 0.548 -0.112 0.060
## .wx3 ~~
## .wy3 0.042 0.034 1.227 0.220 -0.025 0.108
## .wx4 ~~
## .wy4 0.063 0.037 1.710 0.087 -0.009 0.135
## .wx5 ~~
## .wy5 -0.040 0.032 -1.225 0.221 -0.103 0.024
## .wx2 ~~
## .wm2 -0.028 0.049 -0.567 0.571 -0.124 0.068
## .wx3 ~~
## .wm3 -0.015 0.037 -0.389 0.697 -0.088 0.059
## .wx4 ~~
## .wm4 0.010 0.040 0.251 0.802 -0.068 0.088
## .wx5 ~~
## .wm5 -0.052 0.036 -1.424 0.155 -0.123 0.020
## .wy2 ~~
## .wm2 -0.003 0.039 -0.071 0.943 -0.078 0.073
## .wy3 ~~
## .wm3 -0.067 0.026 -2.588 0.010 -0.118 -0.016
## .wy4 ~~
## .wm4 -0.021 0.026 -0.814 0.416 -0.073 0.030
## .wy5 ~~
## .wm5 -0.037 0.026 -1.437 0.151 -0.087 0.013
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.009 -0.009
## -0.012 -0.012
##
## -0.230 -0.230
##
## -0.038 -0.038
##
## 0.087 0.087
##
## 0.127 0.127
##
## -0.093 -0.093
##
## -0.036 -0.036
##
## -0.028 -0.028
##
## 0.019 0.019
##
## -0.109 -0.109
##
## -0.005 -0.005
##
## -0.186 -0.186
##
## -0.060 -0.060
##
## -0.110 -0.110
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.067 0.061 1.092 0.275 -0.053 0.187
## .LadderDif.2 0.125 0.073 1.718 0.086 -0.018 0.267
## .LadderDif.3 0.105 0.077 1.358 0.175 -0.046 0.256
## .LadderDif.4 0.146 0.081 1.804 0.071 -0.013 0.305
## .LadderDif.5 0.122 0.081 1.519 0.129 -0.036 0.280
## .dep.1 0.027 0.065 0.420 0.674 -0.100 0.154
## .dep.2 0.070 0.073 0.962 0.336 -0.072 0.212
## .dep.3 0.108 0.074 1.459 0.145 -0.037 0.253
## .dep.4 0.116 0.077 1.518 0.129 -0.034 0.267
## .dep.5 0.131 0.080 1.648 0.099 -0.025 0.288
## .posEmo.1 0.109 0.065 1.691 0.091 -0.017 0.236
## .posEmo.2 0.102 0.073 1.394 0.163 -0.041 0.245
## .posEmo.3 0.102 0.075 1.366 0.172 -0.044 0.248
## .posEmo.4 0.080 0.077 1.049 0.294 -0.070 0.230
## .posEmo.5 0.073 0.079 0.930 0.352 -0.081 0.228
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.067 0.066
## 0.125 0.127
## 0.105 0.106
## 0.146 0.147
## 0.122 0.126
## 0.027 0.027
## 0.070 0.069
## 0.108 0.107
## 0.116 0.117
## 0.131 0.129
## 0.109 0.109
## 0.102 0.103
## 0.102 0.105
## 0.080 0.084
## 0.073 0.076
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.989 0.066 14.941 0.000 0.859 1.119
## wy1 0.946 0.063 15.005 0.000 0.823 1.070
## wm1 0.994 0.066 14.999 0.000 0.865 1.124
## .wx2 0.888 0.079 11.180 0.000 0.732 1.043
## .wy2 0.541 0.048 11.217 0.000 0.446 0.635
## .wm2 0.679 0.061 11.200 0.000 0.560 0.798
## .wx3 0.698 0.070 10.034 0.000 0.562 0.834
## .wy3 0.331 0.033 10.037 0.000 0.266 0.395
## .wm3 0.398 0.040 10.030 0.000 0.320 0.476
## .wx4 0.760 0.079 9.647 0.000 0.606 0.914
## .wy4 0.326 0.034 9.637 0.000 0.260 0.393
## .wm4 0.386 0.040 9.639 0.000 0.307 0.464
## .wx5 0.605 0.064 9.418 0.000 0.479 0.731
## .wy5 0.304 0.032 9.398 0.000 0.241 0.367
## .wm5 0.373 0.039 9.445 0.000 0.295 0.450
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .dep.1 0.000 0.000 0.000
## .dep.2 0.000 0.000 0.000
## .dep.3 0.000 0.000 0.000
## .dep.4 0.000 0.000 0.000
## .dep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.948 0.948
## 0.550 0.550
## 0.701 0.701
## 0.740 0.740
## 0.336 0.336
## 0.427 0.427
## 0.795 0.795
## 0.345 0.345
## 0.431 0.431
## 0.666 0.666
## 0.307 0.307
## 0.407 0.407
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.06, p < .008
(b1) Positive emotions at time t do not predict health at time t+1
PEmoHealthCLPM_2AR_controls <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gHealth.1 + 1*gHealth.2 + 1*gHealth.3 + 1*gHealth.4 + 1*gHealth.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gHealth.1
wy2 =~ 1*gHealth.2
wy3 =~ 1*gHealth.3
wy4 =~ 1*gHealth.4
wy5 =~ 1*gHealth.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Regression of observed variables on controls (constrained).
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Gen1*GenderBinary
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Gen2*GenderBinary
gHealth.1 + gHealth.2 + gHealth.3 + gHealth.4 + gHealth.5 ~ Gen3*GenderBinary
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Edu1*Edu
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Edu2*Edu
gHealth.1 + gHealth.2 + gHealth.3 + gHealth.4 + gHealth.5 ~ Edu3*Edu
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Inc1*Income
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Inc2*Income
gHealth.1 + gHealth.2 + gHealth.3 + gHealth.4 + gHealth.5 ~ Inc3*Income
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Age1*Age
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Age2*Age
gHealth.1 + gHealth.2 + gHealth.3 + gHealth.4 + gHealth.5 ~ Age3*Age
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoHealthCLPM_w2AR_controls.fit <- lavaan(PEmoHealthCLPM_2AR_controls, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoHealthCLPM_w2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 52 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 433 482
## Number of missing patterns 8
##
## Model Test User Model:
##
## Test statistic 198.085
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2808.382
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.967
## Tucker-Lewis Index (TLI) 0.951
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4697.753
## Loglikelihood unrestricted model (H1) -4598.710
##
## Akaike (AIC) 9563.505
## Bayesian (BIC) 9905.447
## Sample-size adjusted Bayesian (BIC) 9638.878
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.043
## 90 Percent confidence interval - lower 0.033
## 90 Percent confidence interval - upper 0.052
## P-value RMSEA <= 0.05 0.898
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.040
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.956 0.957
##
## 0.971 0.958
##
## 0.951 0.956
##
## 0.950 0.956
##
## 0.920 0.953
##
## 0.936 0.938
##
## 0.914 0.935
##
## 0.941 0.938
##
## 0.934 0.937
##
## 0.916 0.935
##
## 0.977 0.981
##
## 0.951 0.980
##
## 0.960 0.980
##
## 0.972 0.981
##
## 0.971 0.981
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) 0.315 0.077 4.104 0.000 0.164 0.465
## LadderDif.2 ~
## GndrBnr (Gen1) 0.315 0.077 4.104 0.000 0.164 0.465
## LadderDif.3 ~
## GndrBnr (Gen1) 0.315 0.077 4.104 0.000 0.164 0.465
## LadderDif.4 ~
## GndrBnr (Gen1) 0.315 0.077 4.104 0.000 0.164 0.465
## LadderDif.5 ~
## GndrBnr (Gen1) 0.315 0.077 4.104 0.000 0.164 0.465
## posEmo.1 ~
## GndrBnr (Gen2) -0.156 0.082 -1.906 0.057 -0.316 0.004
## posEmo.2 ~
## GndrBnr (Gen2) -0.156 0.082 -1.906 0.057 -0.316 0.004
## posEmo.3 ~
## GndrBnr (Gen2) -0.156 0.082 -1.906 0.057 -0.316 0.004
## posEmo.4 ~
## GndrBnr (Gen2) -0.156 0.082 -1.906 0.057 -0.316 0.004
## posEmo.5 ~
## GndrBnr (Gen2) -0.156 0.082 -1.906 0.057 -0.316 0.004
## gHealth.1 ~
## GndrBnr (Gen3) -0.282 0.083 -3.408 0.001 -0.444 -0.120
## gHealth.2 ~
## GndrBnr (Gen3) -0.282 0.083 -3.408 0.001 -0.444 -0.120
## gHealth.3 ~
## GndrBnr (Gen3) -0.282 0.083 -3.408 0.001 -0.444 -0.120
## gHealth.4 ~
## GndrBnr (Gen3) -0.282 0.083 -3.408 0.001 -0.444 -0.120
## gHealth.5 ~
## GndrBnr (Gen3) -0.282 0.083 -3.408 0.001 -0.444 -0.120
## LadderDif.1 ~
## Edu (Edu1) 0.022 0.041 0.548 0.584 -0.057 0.102
## LadderDif.2 ~
## Edu (Edu1) 0.022 0.041 0.548 0.584 -0.057 0.102
## LadderDif.3 ~
## Edu (Edu1) 0.022 0.041 0.548 0.584 -0.057 0.102
## LadderDif.4 ~
## Edu (Edu1) 0.022 0.041 0.548 0.584 -0.057 0.102
## LadderDif.5 ~
## Edu (Edu1) 0.022 0.041 0.548 0.584 -0.057 0.102
## posEmo.1 ~
## Edu (Edu2) -0.063 0.044 -1.443 0.149 -0.149 0.023
## posEmo.2 ~
## Edu (Edu2) -0.063 0.044 -1.443 0.149 -0.149 0.023
## posEmo.3 ~
## Edu (Edu2) -0.063 0.044 -1.443 0.149 -0.149 0.023
## posEmo.4 ~
## Edu (Edu2) -0.063 0.044 -1.443 0.149 -0.149 0.023
## posEmo.5 ~
## Edu (Edu2) -0.063 0.044 -1.443 0.149 -0.149 0.023
## gHealth.1 ~
## Edu (Edu3) 0.178 0.044 4.054 0.000 0.092 0.264
## gHealth.2 ~
## Edu (Edu3) 0.178 0.044 4.054 0.000 0.092 0.264
## gHealth.3 ~
## Edu (Edu3) 0.178 0.044 4.054 0.000 0.092 0.264
## gHealth.4 ~
## Edu (Edu3) 0.178 0.044 4.054 0.000 0.092 0.264
## gHealth.5 ~
## Edu (Edu3) 0.178 0.044 4.054 0.000 0.092 0.264
## LadderDif.1 ~
## Income (Inc1) -0.250 0.041 -6.154 0.000 -0.330 -0.170
## LadderDif.2 ~
## Income (Inc1) -0.250 0.041 -6.154 0.000 -0.330 -0.170
## LadderDif.3 ~
## Income (Inc1) -0.250 0.041 -6.154 0.000 -0.330 -0.170
## LadderDif.4 ~
## Income (Inc1) -0.250 0.041 -6.154 0.000 -0.330 -0.170
## LadderDif.5 ~
## Income (Inc1) -0.250 0.041 -6.154 0.000 -0.330 -0.170
## posEmo.1 ~
## Income (Inc2) 0.153 0.043 3.529 0.000 0.068 0.239
## posEmo.2 ~
## Income (Inc2) 0.153 0.043 3.529 0.000 0.068 0.239
## posEmo.3 ~
## Income (Inc2) 0.153 0.043 3.529 0.000 0.068 0.239
## posEmo.4 ~
## Income (Inc2) 0.153 0.043 3.529 0.000 0.068 0.239
## posEmo.5 ~
## Income (Inc2) 0.153 0.043 3.529 0.000 0.068 0.239
## gHealth.1 ~
## Income (Inc3) 0.198 0.044 4.511 0.000 0.112 0.283
## gHealth.2 ~
## Income (Inc3) 0.198 0.044 4.511 0.000 0.112 0.283
## gHealth.3 ~
## Income (Inc3) 0.198 0.044 4.511 0.000 0.112 0.283
## gHealth.4 ~
## Income (Inc3) 0.198 0.044 4.511 0.000 0.112 0.283
## gHealth.5 ~
## Income (Inc3) 0.198 0.044 4.511 0.000 0.112 0.283
## LadderDif.1 ~
## Age (Age1) -0.008 0.039 -0.195 0.846 -0.083 0.068
## LadderDif.2 ~
## Age (Age1) -0.008 0.039 -0.195 0.846 -0.083 0.068
## LadderDif.3 ~
## Age (Age1) -0.008 0.039 -0.195 0.846 -0.083 0.068
## LadderDif.4 ~
## Age (Age1) -0.008 0.039 -0.195 0.846 -0.083 0.068
## LadderDif.5 ~
## Age (Age1) -0.008 0.039 -0.195 0.846 -0.083 0.068
## posEmo.1 ~
## Age (Age2) 0.094 0.041 2.276 0.023 0.013 0.175
## posEmo.2 ~
## Age (Age2) 0.094 0.041 2.276 0.023 0.013 0.175
## posEmo.3 ~
## Age (Age2) 0.094 0.041 2.276 0.023 0.013 0.175
## posEmo.4 ~
## Age (Age2) 0.094 0.041 2.276 0.023 0.013 0.175
## posEmo.5 ~
## Age (Age2) 0.094 0.041 2.276 0.023 0.013 0.175
## gHealth.1 ~
## Age (Age3) -0.073 0.042 -1.750 0.080 -0.155 0.009
## gHealth.2 ~
## Age (Age3) -0.073 0.042 -1.750 0.080 -0.155 0.009
## gHealth.3 ~
## Age (Age3) -0.073 0.042 -1.750 0.080 -0.155 0.009
## gHealth.4 ~
## Age (Age3) -0.073 0.042 -1.750 0.080 -0.155 0.009
## gHealth.5 ~
## Age (Age3) -0.073 0.042 -1.750 0.080 -0.155 0.009
## wy2 ~
## wy1 0.748 0.035 21.258 0.000 0.679 0.817
## wm1 (b1) 0.014 0.018 0.770 0.441 -0.022 0.050
## wy3 ~
## wx1 (cp1) 0.020 0.023 0.884 0.376 -0.025 0.065
## wy2 0.386 0.059 6.492 0.000 0.270 0.503
## wm2 (b1) 0.014 0.018 0.770 0.441 -0.022 0.050
## wy1 0.504 0.058 8.748 0.000 0.391 0.617
## wy4 ~
## wx2 (cp1) 0.020 0.023 0.884 0.376 -0.025 0.065
## wy3 0.616 0.062 9.904 0.000 0.494 0.738
## wm3 (b1) 0.014 0.018 0.770 0.441 -0.022 0.050
## wy2 0.227 0.065 3.512 0.000 0.100 0.353
## wy5 ~
## wx3 (cp1) 0.020 0.023 0.884 0.376 -0.025 0.065
## wy4 0.412 0.062 6.673 0.000 0.291 0.533
## wm4 (b1) 0.014 0.018 0.770 0.441 -0.022 0.050
## wy3 0.439 0.061 7.149 0.000 0.319 0.559
## wx2 ~
## wx1 0.513 0.050 10.177 0.000 0.414 0.612
## wm1 (b2) -0.031 0.025 -1.234 0.217 -0.081 0.018
## wx3 ~
## wx2 0.284 0.061 4.622 0.000 0.164 0.404
## wy1 (cp2) -0.010 0.032 -0.320 0.749 -0.073 0.053
## wm2 (b2) -0.031 0.025 -1.234 0.217 -0.081 0.018
## wx1 0.402 0.059 6.764 0.000 0.286 0.518
## wx4 ~
## wx3 0.250 0.064 3.916 0.000 0.125 0.375
## wy2 (cp2) -0.010 0.032 -0.320 0.749 -0.073 0.053
## wm3 (b2) -0.031 0.025 -1.234 0.217 -0.081 0.018
## wx2 0.383 0.068 5.671 0.000 0.251 0.515
## wx5 ~
## wx4 0.294 0.057 5.124 0.000 0.182 0.407
## wy3 (cp2) -0.010 0.032 -0.320 0.749 -0.073 0.053
## wm4 (b2) -0.031 0.025 -1.234 0.217 -0.081 0.018
## wx3 0.431 0.056 7.671 0.000 0.321 0.541
## wm2 ~
## wx1 (a1) -0.063 0.024 -2.641 0.008 -0.110 -0.016
## wy1 (a2) 0.045 0.024 1.860 0.063 -0.002 0.092
## wm1 0.557 0.043 12.960 0.000 0.473 0.642
## wm3 ~
## wx2 (a1) -0.063 0.024 -2.641 0.008 -0.110 -0.016
## wy2 (a2) 0.045 0.024 1.860 0.063 -0.002 0.092
## wm2 0.537 0.059 9.157 0.000 0.422 0.652
## wm1 0.196 0.058 3.382 0.001 0.082 0.310
## wm4 ~
## wx3 (a1) -0.063 0.024 -2.641 0.008 -0.110 -0.016
## wy3 (a2) 0.045 0.024 1.860 0.063 -0.002 0.092
## wm3 0.532 0.063 8.447 0.000 0.409 0.655
## wm2 0.251 0.064 3.892 0.000 0.124 0.377
## wm5 ~
## wx4 (a1) -0.063 0.024 -2.641 0.008 -0.110 -0.016
## wy4 (a2) 0.045 0.024 1.860 0.063 -0.002 0.092
## wm4 0.380 0.059 6.414 0.000 0.264 0.496
## wm3 0.451 0.059 7.622 0.000 0.335 0.568
## Std.lv Std.all
##
## 0.315 0.157
##
## 0.315 0.155
##
## 0.315 0.158
##
## 0.315 0.158
##
## 0.315 0.163
##
## -0.156 -0.078
##
## -0.156 -0.080
##
## -0.156 -0.079
##
## -0.156 -0.078
##
## -0.156 -0.079
##
## -0.282 -0.141
##
## -0.282 -0.144
##
## -0.282 -0.140
##
## -0.282 -0.141
##
## -0.282 -0.144
##
## 0.022 0.022
##
## 0.022 0.022
##
## 0.022 0.022
##
## 0.022 0.022
##
## 0.022 0.023
##
## -0.063 -0.063
##
## -0.063 -0.065
##
## -0.063 -0.065
##
## -0.063 -0.064
##
## -0.063 -0.064
##
## 0.178 0.178
##
## 0.178 0.182
##
## 0.178 0.177
##
## 0.178 0.178
##
## 0.178 0.182
##
## -0.250 -0.250
##
## -0.250 -0.247
##
## -0.250 -0.251
##
## -0.250 -0.251
##
## -0.250 -0.259
##
## 0.153 0.154
##
## 0.153 0.158
##
## 0.153 0.157
##
## 0.153 0.155
##
## 0.153 0.155
##
## 0.198 0.198
##
## 0.198 0.202
##
## 0.198 0.197
##
## 0.198 0.198
##
## 0.198 0.202
##
## -0.008 -0.007
##
## -0.008 -0.007
##
## -0.008 -0.008
##
## -0.008 -0.008
##
## -0.008 -0.008
##
## 0.094 0.094
##
## 0.094 0.097
##
## 0.094 0.096
##
## 0.094 0.095
##
## 0.094 0.095
##
## -0.073 -0.073
##
## -0.073 -0.074
##
## -0.073 -0.072
##
## -0.073 -0.073
##
## -0.073 -0.074
##
## 0.765 0.765
## 0.015 0.015
##
## 0.021 0.021
## 0.375 0.375
## 0.014 0.014
## 0.502 0.502
##
## 0.021 0.021
## 0.620 0.620
## 0.014 0.014
## 0.222 0.222
##
## 0.021 0.021
## 0.420 0.420
## 0.015 0.015
## 0.451 0.451
##
## 0.505 0.505
## -0.032 -0.032
##
## 0.290 0.290
## -0.010 -0.010
## -0.031 -0.031
## 0.404 0.404
##
## 0.250 0.250
## -0.010 -0.010
## -0.032 -0.032
## 0.391 0.391
##
## 0.304 0.304
## -0.010 -0.010
## -0.033 -0.033
## 0.445 0.445
##
## -0.063 -0.063
## 0.044 0.044
## 0.573 0.573
##
## -0.064 -0.064
## 0.043 0.043
## 0.531 0.531
## 0.199 0.199
##
## -0.062 -0.062
## 0.044 0.044
## 0.525 0.525
## 0.245 0.245
##
## -0.062 -0.062
## 0.043 0.043
## 0.380 0.380
## 0.447 0.447
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.273 0.045 -6.060 0.000 -0.362 -0.185
## wm1 -0.182 0.046 -3.971 0.000 -0.272 -0.092
## wy1 ~~
## wm1 0.148 0.045 3.329 0.001 0.061 0.236
## .wx2 ~~
## .wy2 -0.034 0.028 -1.186 0.236 -0.089 0.022
## .wx3 ~~
## .wy3 -0.022 0.026 -0.853 0.393 -0.073 0.029
## .wx4 ~~
## .wy4 0.040 0.030 1.349 0.177 -0.018 0.099
## .wx5 ~~
## .wy5 0.028 0.025 1.097 0.273 -0.022 0.077
## .wx2 ~~
## .wm2 -0.040 0.037 -1.095 0.273 -0.112 0.032
## .wx3 ~~
## .wm3 0.008 0.034 0.234 0.815 -0.058 0.074
## .wx4 ~~
## .wm4 0.003 0.035 0.079 0.937 -0.066 0.071
## .wx5 ~~
## .wm5 -0.009 0.029 -0.321 0.748 -0.066 0.048
## .wy2 ~~
## .wm2 0.062 0.026 2.409 0.016 0.012 0.113
## .wy3 ~~
## .wm3 -0.011 0.024 -0.471 0.637 -0.059 0.036
## .wy4 ~~
## .wm4 0.003 0.025 0.137 0.891 -0.045 0.052
## .wy5 ~~
## .wm5 0.037 0.022 1.693 0.090 -0.006 0.080
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.305 -0.305
## -0.195 -0.195
##
## 0.162 0.162
##
## -0.069 -0.069
##
## -0.055 -0.055
##
## 0.092 0.092
##
## 0.076 0.076
##
## -0.063 -0.063
##
## 0.015 0.015
##
## 0.005 0.005
##
## -0.022 -0.022
##
## 0.140 0.140
##
## -0.031 -0.031
##
## 0.009 0.009
##
## 0.119 0.119
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.152 0.060 -2.519 0.012 -0.271 -0.034
## .LadderDif.2 -0.160 0.066 -2.404 0.016 -0.290 -0.029
## .LadderDif.3 -0.193 0.069 -2.803 0.005 -0.328 -0.058
## .LadderDif.4 -0.174 0.072 -2.414 0.016 -0.315 -0.033
## .LadderDif.5 -0.197 0.071 -2.759 0.006 -0.337 -0.057
## .gHealth.1 0.148 0.062 2.389 0.017 0.027 0.269
## .gHealth.2 0.130 0.064 2.046 0.041 0.005 0.255
## .gHealth.3 0.137 0.066 2.062 0.039 0.007 0.267
## .gHealth.4 0.167 0.069 2.418 0.016 0.032 0.302
## .gHealth.5 0.156 0.069 2.252 0.024 0.020 0.291
## .posEmo.1 0.080 0.063 1.264 0.206 -0.044 0.203
## .posEmo.2 0.077 0.066 1.157 0.247 -0.053 0.207
## .posEmo.3 0.106 0.070 1.512 0.131 -0.031 0.244
## .posEmo.4 0.099 0.073 1.359 0.174 -0.044 0.242
## .posEmo.5 0.112 0.074 1.515 0.130 -0.033 0.256
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.152 -0.152
## -0.160 -0.157
## -0.193 -0.194
## -0.174 -0.175
## -0.197 -0.204
## 0.148 0.148
## 0.130 0.133
## 0.137 0.137
## 0.167 0.168
## 0.156 0.159
## 0.080 0.080
## 0.077 0.079
## 0.106 0.108
## 0.099 0.100
## 0.112 0.113
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.914 0.062 14.664 0.000 0.792 1.036
## wy1 0.876 0.060 14.692 0.000 0.759 0.992
## wm1 0.954 0.065 14.703 0.000 0.827 1.081
## .wx2 0.695 0.057 12.262 0.000 0.584 0.806
## .wy2 0.343 0.028 12.278 0.000 0.288 0.398
## .wm2 0.580 0.047 12.286 0.000 0.488 0.673
## .wx3 0.562 0.051 10.988 0.000 0.461 0.662
## .wy3 0.286 0.026 11.011 0.000 0.235 0.337
## .wm3 0.480 0.044 10.973 0.000 0.394 0.565
## .wx4 0.611 0.058 10.466 0.000 0.496 0.725
## .wy4 0.312 0.030 10.477 0.000 0.254 0.370
## .wm4 0.436 0.042 10.479 0.000 0.354 0.517
## .wx5 0.488 0.047 10.296 0.000 0.395 0.580
## .wy5 0.270 0.026 10.275 0.000 0.219 0.322
## .wm5 0.364 0.035 10.279 0.000 0.294 0.433
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.737 0.737
## 0.410 0.410
## 0.642 0.642
## 0.621 0.621
## 0.323 0.323
## 0.520 0.520
## 0.676 0.676
## 0.358 0.358
## 0.461 0.461
## 0.576 0.576
## 0.322 0.322
## 0.386 0.386
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t does not predict positive emotions at time t+1
(b1) Positive emotions at time t do not predict health at time t+1
# Same model as above code, but fit with d_black dataset this time
PEmoHealthCLPM_b2AR_controls.fit <- lavaan(PEmoHealthCLPM_2AR_controls, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoHealthCLPM_b2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 451 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 217.777
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1934.944
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.940
## Tucker-Lewis Index (TLI) 0.910
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4544.524
## Loglikelihood unrestricted model (H1) -4435.635
##
## Akaike (AIC) 9257.047
## Bayesian (BIC) 9602.410
## Sample-size adjusted Bayesian (BIC) 9335.825
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046
## 90 Percent confidence interval - lower 0.037
## 90 Percent confidence interval - upper 0.055
## P-value RMSEA <= 0.05 0.747
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gHealth.1 1.000 1.000 1.000
## gHealth.2 1.000 1.000 1.000
## gHealth.3 1.000 1.000 1.000
## gHealth.4 1.000 1.000 1.000
## gHealth.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gHealth.1 1.000 1.000 1.000
## wy2 =~
## gHealth.2 1.000 1.000 1.000
## wy3 =~
## gHealth.3 1.000 1.000 1.000
## wy4 =~
## gHealth.4 1.000 1.000 1.000
## wy5 =~
## gHealth.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.995 0.985
##
## 0.967 0.984
##
## 0.966 0.984
##
## 0.973 0.984
##
## 0.949 0.983
##
## 0.978 0.979
##
## 0.998 0.980
##
## 0.965 0.979
##
## 0.995 0.980
##
## 1.005 0.981
##
## 0.997 0.994
##
## 0.991 0.994
##
## 0.980 0.994
##
## 0.944 0.993
##
## 0.966 0.994
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) -0.129 0.076 -1.704 0.088 -0.277 0.019
## LadderDif.2 ~
## GndrBnr (Gen1) -0.129 0.076 -1.704 0.088 -0.277 0.019
## LadderDif.3 ~
## GndrBnr (Gen1) -0.129 0.076 -1.704 0.088 -0.277 0.019
## LadderDif.4 ~
## GndrBnr (Gen1) -0.129 0.076 -1.704 0.088 -0.277 0.019
## LadderDif.5 ~
## GndrBnr (Gen1) -0.129 0.076 -1.704 0.088 -0.277 0.019
## posEmo.1 ~
## GndrBnr (Gen2) -0.205 0.086 -2.383 0.017 -0.373 -0.036
## posEmo.2 ~
## GndrBnr (Gen2) -0.205 0.086 -2.383 0.017 -0.373 -0.036
## posEmo.3 ~
## GndrBnr (Gen2) -0.205 0.086 -2.383 0.017 -0.373 -0.036
## posEmo.4 ~
## GndrBnr (Gen2) -0.205 0.086 -2.383 0.017 -0.373 -0.036
## posEmo.5 ~
## GndrBnr (Gen2) -0.205 0.086 -2.383 0.017 -0.373 -0.036
## gHealth.1 ~
## GndrBnr (Gen3) -0.050 0.089 -0.559 0.576 -0.225 0.125
## gHealth.2 ~
## GndrBnr (Gen3) -0.050 0.089 -0.559 0.576 -0.225 0.125
## gHealth.3 ~
## GndrBnr (Gen3) -0.050 0.089 -0.559 0.576 -0.225 0.125
## gHealth.4 ~
## GndrBnr (Gen3) -0.050 0.089 -0.559 0.576 -0.225 0.125
## gHealth.5 ~
## GndrBnr (Gen3) -0.050 0.089 -0.559 0.576 -0.225 0.125
## LadderDif.1 ~
## Edu (Edu1) -0.077 0.039 -1.944 0.052 -0.154 0.001
## LadderDif.2 ~
## Edu (Edu1) -0.077 0.039 -1.944 0.052 -0.154 0.001
## LadderDif.3 ~
## Edu (Edu1) -0.077 0.039 -1.944 0.052 -0.154 0.001
## LadderDif.4 ~
## Edu (Edu1) -0.077 0.039 -1.944 0.052 -0.154 0.001
## LadderDif.5 ~
## Edu (Edu1) -0.077 0.039 -1.944 0.052 -0.154 0.001
## posEmo.1 ~
## Edu (Edu2) -0.002 0.045 -0.050 0.960 -0.091 0.086
## posEmo.2 ~
## Edu (Edu2) -0.002 0.045 -0.050 0.960 -0.091 0.086
## posEmo.3 ~
## Edu (Edu2) -0.002 0.045 -0.050 0.960 -0.091 0.086
## posEmo.4 ~
## Edu (Edu2) -0.002 0.045 -0.050 0.960 -0.091 0.086
## posEmo.5 ~
## Edu (Edu2) -0.002 0.045 -0.050 0.960 -0.091 0.086
## gHealth.1 ~
## Edu (Edu3) 0.097 0.047 2.068 0.039 0.005 0.188
## gHealth.2 ~
## Edu (Edu3) 0.097 0.047 2.068 0.039 0.005 0.188
## gHealth.3 ~
## Edu (Edu3) 0.097 0.047 2.068 0.039 0.005 0.188
## gHealth.4 ~
## Edu (Edu3) 0.097 0.047 2.068 0.039 0.005 0.188
## gHealth.5 ~
## Edu (Edu3) 0.097 0.047 2.068 0.039 0.005 0.188
## LadderDif.1 ~
## Income (Inc1) -0.113 0.039 -2.908 0.004 -0.188 -0.037
## LadderDif.2 ~
## Income (Inc1) -0.113 0.039 -2.908 0.004 -0.188 -0.037
## LadderDif.3 ~
## Income (Inc1) -0.113 0.039 -2.908 0.004 -0.188 -0.037
## LadderDif.4 ~
## Income (Inc1) -0.113 0.039 -2.908 0.004 -0.188 -0.037
## LadderDif.5 ~
## Income (Inc1) -0.113 0.039 -2.908 0.004 -0.188 -0.037
## posEmo.1 ~
## Income (Inc2) 0.040 0.044 0.911 0.363 -0.046 0.127
## posEmo.2 ~
## Income (Inc2) 0.040 0.044 0.911 0.363 -0.046 0.127
## posEmo.3 ~
## Income (Inc2) 0.040 0.044 0.911 0.363 -0.046 0.127
## posEmo.4 ~
## Income (Inc2) 0.040 0.044 0.911 0.363 -0.046 0.127
## posEmo.5 ~
## Income (Inc2) 0.040 0.044 0.911 0.363 -0.046 0.127
## gHealth.1 ~
## Income (Inc3) 0.152 0.046 3.334 0.001 0.063 0.241
## gHealth.2 ~
## Income (Inc3) 0.152 0.046 3.334 0.001 0.063 0.241
## gHealth.3 ~
## Income (Inc3) 0.152 0.046 3.334 0.001 0.063 0.241
## gHealth.4 ~
## Income (Inc3) 0.152 0.046 3.334 0.001 0.063 0.241
## gHealth.5 ~
## Income (Inc3) 0.152 0.046 3.334 0.001 0.063 0.241
## LadderDif.1 ~
## Age (Age1) 0.045 0.038 1.187 0.235 -0.029 0.118
## LadderDif.2 ~
## Age (Age1) 0.045 0.038 1.187 0.235 -0.029 0.118
## LadderDif.3 ~
## Age (Age1) 0.045 0.038 1.187 0.235 -0.029 0.118
## LadderDif.4 ~
## Age (Age1) 0.045 0.038 1.187 0.235 -0.029 0.118
## LadderDif.5 ~
## Age (Age1) 0.045 0.038 1.187 0.235 -0.029 0.118
## posEmo.1 ~
## Age (Age2) 0.031 0.043 0.718 0.473 -0.053 0.115
## posEmo.2 ~
## Age (Age2) 0.031 0.043 0.718 0.473 -0.053 0.115
## posEmo.3 ~
## Age (Age2) 0.031 0.043 0.718 0.473 -0.053 0.115
## posEmo.4 ~
## Age (Age2) 0.031 0.043 0.718 0.473 -0.053 0.115
## posEmo.5 ~
## Age (Age2) 0.031 0.043 0.718 0.473 -0.053 0.115
## gHealth.1 ~
## Age (Age3) 0.010 0.044 0.228 0.820 -0.077 0.097
## gHealth.2 ~
## Age (Age3) 0.010 0.044 0.228 0.820 -0.077 0.097
## gHealth.3 ~
## Age (Age3) 0.010 0.044 0.228 0.820 -0.077 0.097
## gHealth.4 ~
## Age (Age3) 0.010 0.044 0.228 0.820 -0.077 0.097
## gHealth.5 ~
## Age (Age3) 0.010 0.044 0.228 0.820 -0.077 0.097
## wy2 ~
## wy1 0.732 0.047 15.641 0.000 0.640 0.824
## wm1 (b1) 0.014 0.023 0.616 0.538 -0.031 0.059
## wy3 ~
## wx1 (cp1) -0.031 0.027 -1.118 0.264 -0.085 0.023
## wy2 0.478 0.063 7.635 0.000 0.355 0.601
## wm2 (b1) 0.014 0.023 0.616 0.538 -0.031 0.059
## wy1 0.321 0.062 5.157 0.000 0.199 0.443
## wy4 ~
## wx2 (cp1) -0.031 0.027 -1.118 0.264 -0.085 0.023
## wy3 0.452 0.068 6.623 0.000 0.318 0.586
## wm3 (b1) 0.014 0.023 0.616 0.538 -0.031 0.059
## wy2 0.412 0.066 6.264 0.000 0.283 0.541
## wy5 ~
## wx3 (cp1) -0.031 0.027 -1.118 0.264 -0.085 0.023
## wy4 0.473 0.064 7.403 0.000 0.348 0.598
## wm4 (b1) 0.014 0.023 0.616 0.538 -0.031 0.059
## wy3 0.444 0.067 6.645 0.000 0.313 0.575
## wx2 ~
## wx1 0.217 0.061 3.528 0.000 0.096 0.337
## wm1 (b2) -0.041 0.032 -1.299 0.194 -0.103 0.021
## wx3 ~
## wx2 0.432 0.067 6.466 0.000 0.301 0.563
## wy1 (cp2) 0.022 0.037 0.587 0.557 -0.051 0.095
## wm2 (b2) -0.041 0.032 -1.299 0.194 -0.103 0.021
## wx1 0.160 0.064 2.520 0.012 0.036 0.285
## wx4 ~
## wx3 0.267 0.078 3.428 0.001 0.114 0.420
## wy2 (cp2) 0.022 0.037 0.587 0.557 -0.051 0.095
## wm3 (b2) -0.041 0.032 -1.299 0.194 -0.103 0.021
## wx2 0.247 0.079 3.134 0.002 0.093 0.402
## wx5 ~
## wx4 0.241 0.065 3.734 0.000 0.115 0.368
## wy3 (cp2) 0.022 0.037 0.587 0.557 -0.051 0.095
## wm4 (b2) -0.041 0.032 -1.299 0.194 -0.103 0.021
## wx3 0.419 0.070 6.005 0.000 0.282 0.555
## wm2 ~
## wx1 (a1) -0.037 0.025 -1.457 0.145 -0.086 0.013
## wy1 (a2) 0.048 0.026 1.898 0.058 -0.002 0.099
## wm1 0.517 0.053 9.768 0.000 0.413 0.620
## wm3 ~
## wx2 (a1) -0.037 0.025 -1.457 0.145 -0.086 0.013
## wy2 (a2) 0.048 0.026 1.898 0.058 -0.002 0.099
## wm2 0.597 0.054 11.052 0.000 0.491 0.703
## wm1 0.215 0.053 4.090 0.000 0.112 0.319
## wm4 ~
## wx3 (a1) -0.037 0.025 -1.457 0.145 -0.086 0.013
## wy3 (a2) 0.048 0.026 1.898 0.058 -0.002 0.099
## wm3 0.431 0.070 6.204 0.000 0.295 0.567
## wm2 0.332 0.067 4.984 0.000 0.202 0.463
## wm5 ~
## wx4 (a1) -0.037 0.025 -1.457 0.145 -0.086 0.013
## wy4 (a2) 0.048 0.026 1.898 0.058 -0.002 0.099
## wm4 0.338 0.071 4.769 0.000 0.199 0.477
## wm3 0.471 0.068 6.952 0.000 0.338 0.604
## Std.lv Std.all
##
## -0.129 -0.064
##
## -0.129 -0.065
##
## -0.129 -0.065
##
## -0.129 -0.065
##
## -0.129 -0.067
##
## -0.205 -0.102
##
## -0.205 -0.103
##
## -0.205 -0.104
##
## -0.205 -0.108
##
## -0.205 -0.105
##
## -0.050 -0.025
##
## -0.050 -0.024
##
## -0.050 -0.025
##
## -0.050 -0.025
##
## -0.050 -0.024
##
## -0.077 -0.076
##
## -0.077 -0.078
##
## -0.077 -0.078
##
## -0.077 -0.078
##
## -0.077 -0.079
##
## -0.002 -0.002
##
## -0.002 -0.002
##
## -0.002 -0.002
##
## -0.002 -0.002
##
## -0.002 -0.002
##
## 0.097 0.097
##
## 0.097 0.095
##
## 0.097 0.098
##
## 0.097 0.095
##
## 0.097 0.094
##
## -0.113 -0.111
##
## -0.113 -0.114
##
## -0.113 -0.115
##
## -0.113 -0.114
##
## -0.113 -0.117
##
## 0.040 0.040
##
## 0.040 0.040
##
## 0.040 0.041
##
## 0.040 0.042
##
## 0.040 0.041
##
## 0.152 0.152
##
## 0.152 0.149
##
## 0.152 0.154
##
## 0.152 0.149
##
## 0.152 0.148
##
## 0.045 0.044
##
## 0.045 0.045
##
## 0.045 0.045
##
## 0.045 0.045
##
## 0.045 0.046
##
## 0.031 0.031
##
## 0.031 0.031
##
## 0.031 0.031
##
## 0.031 0.032
##
## 0.031 0.032
##
## 0.010 0.010
##
## 0.010 0.010
##
## 0.010 0.010
##
## 0.010 0.010
##
## 0.010 0.010
##
## 0.717 0.717
## 0.014 0.014
##
## -0.032 -0.032
## 0.495 0.495
## 0.015 0.015
## 0.325 0.325
##
## -0.030 -0.030
## 0.438 0.438
## 0.014 0.014
## 0.413 0.413
##
## -0.030 -0.030
## 0.468 0.468
## 0.013 0.013
## 0.426 0.426
##
## 0.223 0.223
## -0.042 -0.042
##
## 0.433 0.433
## 0.022 0.022
## -0.042 -0.042
## 0.165 0.165
##
## 0.265 0.265
## 0.023 0.023
## -0.041 -0.041
## 0.246 0.246
##
## 0.247 0.247
## 0.022 0.022
## -0.041 -0.041
## 0.426 0.426
##
## -0.037 -0.037
## 0.048 0.048
## 0.520 0.520
##
## -0.036 -0.036
## 0.049 0.049
## 0.604 0.604
## 0.219 0.219
##
## -0.038 -0.038
## 0.050 0.050
## 0.448 0.448
## 0.349 0.349
##
## -0.037 -0.037
## 0.050 0.050
## 0.330 0.330
## 0.477 0.477
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.038 0.046 -0.824 0.410 -0.128 0.052
## wm1 -0.012 0.047 -0.260 0.795 -0.104 0.080
## wy1 ~~
## wm1 0.259 0.048 5.440 0.000 0.165 0.352
## .wx2 ~~
## .wy2 -0.003 0.042 -0.080 0.937 -0.086 0.079
## .wx3 ~~
## .wy3 0.057 0.037 1.536 0.125 -0.016 0.129
## .wx4 ~~
## .wy4 -0.053 0.039 -1.345 0.179 -0.130 0.024
## .wx5 ~~
## .wy5 0.026 0.032 0.818 0.414 -0.037 0.089
## .wx2 ~~
## .wm2 -0.027 0.050 -0.536 0.592 -0.125 0.071
## .wx3 ~~
## .wm3 -0.011 0.038 -0.288 0.773 -0.085 0.063
## .wx4 ~~
## .wm4 0.016 0.040 0.395 0.693 -0.062 0.093
## .wx5 ~~
## .wm5 -0.055 0.037 -1.493 0.135 -0.128 0.017
## .wy2 ~~
## .wm2 0.014 0.036 0.378 0.705 -0.058 0.085
## .wy3 ~~
## .wm3 0.070 0.028 2.470 0.013 0.015 0.126
## .wy4 ~~
## .wm4 0.051 0.028 1.840 0.066 -0.003 0.106
## .wy5 ~~
## .wm5 0.031 0.026 1.216 0.224 -0.019 0.082
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.039 -0.039
## -0.012 -0.012
##
## 0.265 0.265
##
## -0.005 -0.005
##
## 0.110 0.110
##
## -0.101 -0.101
##
## 0.062 0.062
##
## -0.034 -0.034
##
## -0.020 -0.020
##
## 0.029 0.029
##
## -0.115 -0.115
##
## 0.024 0.024
##
## 0.178 0.178
##
## 0.139 0.139
##
## 0.093 0.093
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.068 0.061 1.105 0.269 -0.052 0.188
## .LadderDif.2 0.126 0.073 1.732 0.083 -0.017 0.268
## .LadderDif.3 0.105 0.077 1.360 0.174 -0.046 0.256
## .LadderDif.4 0.145 0.081 1.798 0.072 -0.013 0.304
## .LadderDif.5 0.122 0.081 1.508 0.131 -0.036 0.280
## .gHealth.1 0.029 0.066 0.437 0.662 -0.100 0.157
## .gHealth.2 -0.057 0.073 -0.780 0.435 -0.199 0.086
## .gHealth.3 -0.042 0.074 -0.570 0.569 -0.188 0.103
## .gHealth.4 -0.051 0.078 -0.655 0.513 -0.204 0.102
## .gHealth.5 -0.077 0.080 -0.956 0.339 -0.234 0.081
## .posEmo.1 0.107 0.065 1.645 0.100 -0.021 0.234
## .posEmo.2 0.098 0.074 1.319 0.187 -0.047 0.243
## .posEmo.3 0.101 0.076 1.327 0.185 -0.048 0.250
## .posEmo.4 0.080 0.077 1.043 0.297 -0.071 0.231
## .posEmo.5 0.073 0.080 0.906 0.365 -0.084 0.229
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.068 0.067
## 0.126 0.128
## 0.105 0.107
## 0.145 0.147
## 0.122 0.126
## 0.029 0.029
## -0.057 -0.056
## -0.042 -0.043
## -0.051 -0.050
## -0.077 -0.075
## 0.107 0.107
## 0.098 0.098
## 0.101 0.102
## 0.080 0.085
## 0.073 0.075
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.989 0.066 14.939 0.000 0.859 1.119
## wy1 0.956 0.064 14.999 0.000 0.831 1.080
## wm1 0.994 0.066 15.001 0.000 0.864 1.124
## .wx2 0.887 0.079 11.181 0.000 0.732 1.043
## .wy2 0.478 0.043 11.233 0.000 0.394 0.561
## .wm2 0.700 0.062 11.219 0.000 0.578 0.822
## .wx3 0.700 0.070 10.035 0.000 0.563 0.836
## .wy3 0.382 0.038 10.024 0.000 0.307 0.456
## .wm3 0.408 0.041 10.031 0.000 0.328 0.488
## .wx4 0.760 0.079 9.645 0.000 0.605 0.914
## .wy4 0.362 0.038 9.634 0.000 0.288 0.435
## .wm4 0.378 0.039 9.637 0.000 0.301 0.455
## .wx5 0.603 0.064 9.435 0.000 0.477 0.728
## .wy5 0.301 0.032 9.425 0.000 0.238 0.363
## .wm5 0.384 0.041 9.417 0.000 0.304 0.464
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gHealth.1 0.000 0.000 0.000
## .gHealth.2 0.000 0.000 0.000
## .gHealth.3 0.000 0.000 0.000
## .gHealth.4 0.000 0.000 0.000
## .gHealth.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.948 0.948
## 0.480 0.480
## 0.712 0.712
## 0.750 0.750
## 0.410 0.410
## 0.425 0.425
## 0.803 0.803
## 0.365 0.365
## 0.425 0.425
## 0.669 0.669
## 0.297 0.297
## 0.411 0.411
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t predicts fewer positive emotions at time t+1, b = -.071, p = .003
(b1) Positive emotions at time t predict sleep at time t+1, b = .06, p = .003
c’1 path is nonsignificant
a2 path is also significant, and its CIs overlap with a1 CIs
PEmoSleepCLPM_2AR_controls <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*gSleep.1 + 1*gSleep.2 + 1*gSleep.3 + 1*gSleep.4 + 1*gSleep.5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*gSleep.1
wy2 =~ 1*gSleep.2
wy3 =~ 1*gSleep.3
wy4 =~ 1*gSleep.4
wy5 =~ 1*gSleep.5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Regression of observed variables on controls (constrained).
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Gen1*GenderBinary
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Gen2*GenderBinary
gSleep.1 + gSleep.2 + gSleep.3 + gSleep.4 + gSleep.5 ~ Gen3*GenderBinary
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Edu1*Edu
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Edu2*Edu
gSleep.1 + gSleep.2 + gSleep.3 + gSleep.4 + gSleep.5 ~ Edu3*Edu
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Inc1*Income
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Inc2*Income
gSleep.1 + gSleep.2 + gSleep.3 + gSleep.4 + gSleep.5 ~ Inc3*Income
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Age1*Age
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Age2*Age
gSleep.1 + gSleep.2 + gSleep.3 + gSleep.4 + gSleep.5 ~ Age3*Age
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoSleepCLPM_w2AR_controls.fit <- lavaan(PEmoSleepCLPM_2AR_controls, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoSleepCLPM_w2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 53 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 433 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 196.898
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2540.661
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.964
## Tucker-Lewis Index (TLI) 0.946
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4806.157
## Loglikelihood unrestricted model (H1) -4707.708
##
## Akaike (AIC) 9780.314
## Bayesian (BIC) 10122.256
## Sample-size adjusted Bayesian (BIC) 9855.686
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.042
## 90 Percent confidence interval - lower 0.032
## 90 Percent confidence interval - upper 0.052
## P-value RMSEA <= 0.05 0.906
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.042
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.956 0.957
##
## 0.972 0.958
##
## 0.950 0.956
##
## 0.950 0.956
##
## 0.915 0.953
##
## 0.950 0.973
##
## 0.957 0.973
##
## 0.893 0.970
##
## 0.971 0.974
##
## 0.982 0.975
##
## 0.977 0.981
##
## 0.949 0.980
##
## 0.956 0.980
##
## 0.973 0.981
##
## 0.967 0.980
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) 0.312 0.077 4.059 0.000 0.161 0.463
## LadderDif.2 ~
## GndrBnr (Gen1) 0.312 0.077 4.059 0.000 0.161 0.463
## LadderDif.3 ~
## GndrBnr (Gen1) 0.312 0.077 4.059 0.000 0.161 0.463
## LadderDif.4 ~
## GndrBnr (Gen1) 0.312 0.077 4.059 0.000 0.161 0.463
## LadderDif.5 ~
## GndrBnr (Gen1) 0.312 0.077 4.059 0.000 0.161 0.463
## posEmo.1 ~
## GndrBnr (Gen2) -0.153 0.082 -1.875 0.061 -0.313 0.007
## posEmo.2 ~
## GndrBnr (Gen2) -0.153 0.082 -1.875 0.061 -0.313 0.007
## posEmo.3 ~
## GndrBnr (Gen2) -0.153 0.082 -1.875 0.061 -0.313 0.007
## posEmo.4 ~
## GndrBnr (Gen2) -0.153 0.082 -1.875 0.061 -0.313 0.007
## posEmo.5 ~
## GndrBnr (Gen2) -0.153 0.082 -1.875 0.061 -0.313 0.007
## gSleep.1 ~
## GndrBnr (Gen3) -0.261 0.083 -3.144 0.002 -0.424 -0.098
## gSleep.2 ~
## GndrBnr (Gen3) -0.261 0.083 -3.144 0.002 -0.424 -0.098
## gSleep.3 ~
## GndrBnr (Gen3) -0.261 0.083 -3.144 0.002 -0.424 -0.098
## gSleep.4 ~
## GndrBnr (Gen3) -0.261 0.083 -3.144 0.002 -0.424 -0.098
## gSleep.5 ~
## GndrBnr (Gen3) -0.261 0.083 -3.144 0.002 -0.424 -0.098
## LadderDif.1 ~
## Edu (Edu1) 0.022 0.041 0.541 0.588 -0.058 0.102
## LadderDif.2 ~
## Edu (Edu1) 0.022 0.041 0.541 0.588 -0.058 0.102
## LadderDif.3 ~
## Edu (Edu1) 0.022 0.041 0.541 0.588 -0.058 0.102
## LadderDif.4 ~
## Edu (Edu1) 0.022 0.041 0.541 0.588 -0.058 0.102
## LadderDif.5 ~
## Edu (Edu1) 0.022 0.041 0.541 0.588 -0.058 0.102
## posEmo.1 ~
## Edu (Edu2) -0.062 0.044 -1.412 0.158 -0.148 0.024
## posEmo.2 ~
## Edu (Edu2) -0.062 0.044 -1.412 0.158 -0.148 0.024
## posEmo.3 ~
## Edu (Edu2) -0.062 0.044 -1.412 0.158 -0.148 0.024
## posEmo.4 ~
## Edu (Edu2) -0.062 0.044 -1.412 0.158 -0.148 0.024
## posEmo.5 ~
## Edu (Edu2) -0.062 0.044 -1.412 0.158 -0.148 0.024
## gSleep.1 ~
## Edu (Edu3) 0.060 0.045 1.346 0.178 -0.027 0.148
## gSleep.2 ~
## Edu (Edu3) 0.060 0.045 1.346 0.178 -0.027 0.148
## gSleep.3 ~
## Edu (Edu3) 0.060 0.045 1.346 0.178 -0.027 0.148
## gSleep.4 ~
## Edu (Edu3) 0.060 0.045 1.346 0.178 -0.027 0.148
## gSleep.5 ~
## Edu (Edu3) 0.060 0.045 1.346 0.178 -0.027 0.148
## LadderDif.1 ~
## Income (Inc1) -0.248 0.041 -6.091 0.000 -0.328 -0.168
## LadderDif.2 ~
## Income (Inc1) -0.248 0.041 -6.091 0.000 -0.328 -0.168
## LadderDif.3 ~
## Income (Inc1) -0.248 0.041 -6.091 0.000 -0.328 -0.168
## LadderDif.4 ~
## Income (Inc1) -0.248 0.041 -6.091 0.000 -0.328 -0.168
## LadderDif.5 ~
## Income (Inc1) -0.248 0.041 -6.091 0.000 -0.328 -0.168
## posEmo.1 ~
## Income (Inc2) 0.155 0.043 3.579 0.000 0.070 0.240
## posEmo.2 ~
## Income (Inc2) 0.155 0.043 3.579 0.000 0.070 0.240
## posEmo.3 ~
## Income (Inc2) 0.155 0.043 3.579 0.000 0.070 0.240
## posEmo.4 ~
## Income (Inc2) 0.155 0.043 3.579 0.000 0.070 0.240
## posEmo.5 ~
## Income (Inc2) 0.155 0.043 3.579 0.000 0.070 0.240
## gSleep.1 ~
## Income (Inc3) 0.150 0.044 3.405 0.001 0.064 0.236
## gSleep.2 ~
## Income (Inc3) 0.150 0.044 3.405 0.001 0.064 0.236
## gSleep.3 ~
## Income (Inc3) 0.150 0.044 3.405 0.001 0.064 0.236
## gSleep.4 ~
## Income (Inc3) 0.150 0.044 3.405 0.001 0.064 0.236
## gSleep.5 ~
## Income (Inc3) 0.150 0.044 3.405 0.001 0.064 0.236
## LadderDif.1 ~
## Age (Age1) -0.018 0.039 -0.467 0.640 -0.094 0.058
## LadderDif.2 ~
## Age (Age1) -0.018 0.039 -0.467 0.640 -0.094 0.058
## LadderDif.3 ~
## Age (Age1) -0.018 0.039 -0.467 0.640 -0.094 0.058
## LadderDif.4 ~
## Age (Age1) -0.018 0.039 -0.467 0.640 -0.094 0.058
## LadderDif.5 ~
## Age (Age1) -0.018 0.039 -0.467 0.640 -0.094 0.058
## posEmo.1 ~
## Age (Age2) 0.095 0.041 2.302 0.021 0.014 0.175
## posEmo.2 ~
## Age (Age2) 0.095 0.041 2.302 0.021 0.014 0.175
## posEmo.3 ~
## Age (Age2) 0.095 0.041 2.302 0.021 0.014 0.175
## posEmo.4 ~
## Age (Age2) 0.095 0.041 2.302 0.021 0.014 0.175
## posEmo.5 ~
## Age (Age2) 0.095 0.041 2.302 0.021 0.014 0.175
## gSleep.1 ~
## Age (Age3) -0.032 0.042 -0.757 0.449 -0.113 0.050
## gSleep.2 ~
## Age (Age3) -0.032 0.042 -0.757 0.449 -0.113 0.050
## gSleep.3 ~
## Age (Age3) -0.032 0.042 -0.757 0.449 -0.113 0.050
## gSleep.4 ~
## Age (Age3) -0.032 0.042 -0.757 0.449 -0.113 0.050
## gSleep.5 ~
## Age (Age3) -0.032 0.042 -0.757 0.449 -0.113 0.050
## wy2 ~
## wy1 0.761 0.037 20.488 0.000 0.688 0.834
## wm1 (b1) 0.061 0.021 2.955 0.003 0.021 0.102
## wy3 ~
## wx1 (cp1) -0.042 0.025 -1.643 0.100 -0.091 0.008
## wy2 0.384 0.063 6.078 0.000 0.260 0.508
## wm2 (b1) 0.061 0.021 2.955 0.003 0.021 0.102
## wy1 0.337 0.063 5.375 0.000 0.214 0.460
## wy4 ~
## wx2 (cp1) -0.042 0.025 -1.643 0.100 -0.091 0.008
## wy3 0.588 0.067 8.796 0.000 0.457 0.719
## wm3 (b1) 0.061 0.021 2.955 0.003 0.021 0.102
## wy2 0.265 0.063 4.209 0.000 0.142 0.388
## wy5 ~
## wx3 (cp1) -0.042 0.025 -1.643 0.100 -0.091 0.008
## wy4 0.578 0.066 8.801 0.000 0.449 0.706
## wm4 (b1) 0.061 0.021 2.955 0.003 0.021 0.102
## wy3 0.235 0.071 3.320 0.001 0.096 0.374
## wx2 ~
## wx1 0.516 0.051 10.202 0.000 0.417 0.615
## wm1 (b2) -0.033 0.026 -1.286 0.198 -0.084 0.018
## wx3 ~
## wx2 0.282 0.061 4.584 0.000 0.161 0.402
## wy1 (cp2) -0.009 0.031 -0.297 0.766 -0.071 0.052
## wm2 (b2) -0.033 0.026 -1.286 0.198 -0.084 0.018
## wx1 0.401 0.059 6.812 0.000 0.286 0.517
## wx4 ~
## wx3 0.248 0.064 3.856 0.000 0.122 0.374
## wy2 (cp2) -0.009 0.031 -0.297 0.766 -0.071 0.052
## wm3 (b2) -0.033 0.026 -1.286 0.198 -0.084 0.018
## wx2 0.383 0.068 5.669 0.000 0.251 0.516
## wx5 ~
## wx4 0.271 0.058 4.704 0.000 0.158 0.384
## wy3 (cp2) -0.009 0.031 -0.297 0.766 -0.071 0.052
## wm4 (b2) -0.033 0.026 -1.286 0.198 -0.084 0.018
## wx3 0.441 0.056 7.913 0.000 0.332 0.550
## wm2 ~
## wx1 (a1) -0.071 0.024 -3.004 0.003 -0.117 -0.025
## wy1 (a2) 0.079 0.024 3.308 0.001 0.032 0.127
## wm1 0.546 0.043 12.660 0.000 0.462 0.631
## wm3 ~
## wx2 (a1) -0.071 0.024 -3.004 0.003 -0.117 -0.025
## wy2 (a2) 0.079 0.024 3.308 0.001 0.032 0.127
## wm2 0.534 0.058 9.187 0.000 0.420 0.648
## wm1 0.175 0.057 3.066 0.002 0.063 0.287
## wm4 ~
## wx3 (a1) -0.071 0.024 -3.004 0.003 -0.117 -0.025
## wy3 (a2) 0.079 0.024 3.308 0.001 0.032 0.127
## wm3 0.520 0.064 8.188 0.000 0.396 0.645
## wm2 0.242 0.065 3.736 0.000 0.115 0.369
## wm5 ~
## wx4 (a1) -0.071 0.024 -3.004 0.003 -0.117 -0.025
## wy4 (a2) 0.079 0.024 3.308 0.001 0.032 0.127
## wm4 0.369 0.059 6.301 0.000 0.254 0.484
## wm3 0.442 0.059 7.497 0.000 0.327 0.558
## Std.lv Std.all
##
## 0.312 0.156
##
## 0.312 0.154
##
## 0.312 0.157
##
## 0.312 0.157
##
## 0.312 0.163
##
## -0.153 -0.077
##
## -0.153 -0.079
##
## -0.153 -0.078
##
## -0.153 -0.077
##
## -0.153 -0.077
##
## -0.261 -0.134
##
## -0.261 -0.133
##
## -0.261 -0.142
##
## -0.261 -0.131
##
## -0.261 -0.129
##
## 0.022 0.022
##
## 0.022 0.022
##
## 0.022 0.022
##
## 0.022 0.022
##
## 0.022 0.023
##
## -0.062 -0.062
##
## -0.062 -0.064
##
## -0.062 -0.063
##
## -0.062 -0.062
##
## -0.062 -0.063
##
## 0.060 0.061
##
## 0.060 0.061
##
## 0.060 0.065
##
## 0.060 0.060
##
## 0.060 0.060
##
## -0.248 -0.248
##
## -0.248 -0.244
##
## -0.248 -0.250
##
## -0.248 -0.250
##
## -0.248 -0.259
##
## 0.155 0.156
##
## 0.155 0.160
##
## 0.155 0.159
##
## 0.155 0.156
##
## 0.155 0.157
##
## 0.150 0.154
##
## 0.150 0.153
##
## 0.150 0.163
##
## 0.150 0.150
##
## 0.150 0.149
##
## -0.018 -0.018
##
## -0.018 -0.018
##
## -0.018 -0.018
##
## -0.018 -0.018
##
## -0.018 -0.019
##
## 0.095 0.095
##
## 0.095 0.097
##
## 0.095 0.097
##
## 0.095 0.095
##
## 0.095 0.096
##
## -0.032 -0.032
##
## -0.032 -0.032
##
## -0.032 -0.034
##
## -0.032 -0.031
##
## -0.032 -0.031
##
## 0.756 0.756
## 0.063 0.063
##
## -0.045 -0.045
## 0.412 0.412
## 0.065 0.065
## 0.359 0.359
##
## -0.042 -0.042
## 0.540 0.540
## 0.060 0.060
## 0.261 0.261
##
## -0.040 -0.040
## 0.571 0.571
## 0.061 0.061
## 0.214 0.214
##
## 0.507 0.507
## -0.034 -0.034
##
## 0.288 0.288
## -0.009 -0.009
## -0.033 -0.033
## 0.404 0.404
##
## 0.248 0.248
## -0.009 -0.009
## -0.034 -0.034
## 0.393 0.393
##
## 0.281 0.281
## -0.009 -0.009
## -0.036 -0.036
## 0.458 0.458
##
## -0.071 -0.071
## 0.080 0.080
## 0.563 0.563
##
## -0.072 -0.072
## 0.080 0.080
## 0.530 0.530
## 0.179 0.179
##
## -0.069 -0.069
## 0.073 0.073
## 0.511 0.511
## 0.236 0.236
##
## -0.070 -0.070
## 0.080 0.080
## 0.371 0.371
## 0.438 0.438
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.177 0.045 -3.984 0.000 -0.265 -0.090
## wm1 -0.182 0.046 -3.975 0.000 -0.272 -0.092
## wy1 ~~
## wm1 0.211 0.046 4.598 0.000 0.121 0.300
## .wx2 ~~
## .wy2 0.025 0.029 0.839 0.402 -0.033 0.082
## .wx3 ~~
## .wy3 0.019 0.029 0.674 0.500 -0.037 0.076
## .wx4 ~~
## .wy4 0.028 0.033 0.864 0.388 -0.036 0.093
## .wx5 ~~
## .wy5 -0.045 0.031 -1.473 0.141 -0.106 0.015
## .wx2 ~~
## .wm2 -0.039 0.036 -1.078 0.281 -0.110 0.032
## .wx3 ~~
## .wm3 0.010 0.034 0.289 0.773 -0.057 0.076
## .wx4 ~~
## .wm4 0.003 0.035 0.072 0.943 -0.067 0.072
## .wx5 ~~
## .wm5 -0.012 0.029 -0.402 0.688 -0.068 0.045
## .wy2 ~~
## .wm2 -0.009 0.027 -0.347 0.729 -0.061 0.043
## .wy3 ~~
## .wm3 0.048 0.027 1.811 0.070 -0.004 0.100
## .wy4 ~~
## .wm4 0.008 0.028 0.289 0.773 -0.047 0.063
## .wy5 ~~
## .wm5 -0.006 0.026 -0.233 0.815 -0.057 0.045
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.195 -0.195
## -0.195 -0.195
##
## 0.227 0.227
##
## 0.049 0.049
##
## 0.044 0.044
##
## 0.059 0.059
##
## -0.104 -0.104
##
## -0.062 -0.062
##
## 0.019 0.019
##
## 0.005 0.005
##
## -0.028 -0.028
##
## -0.020 -0.020
##
## 0.118 0.118
##
## 0.020 0.020
##
## -0.016 -0.016
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.151 0.061 -2.494 0.013 -0.270 -0.032
## .LadderDif.2 -0.158 0.067 -2.376 0.017 -0.288 -0.028
## .LadderDif.3 -0.190 0.069 -2.764 0.006 -0.325 -0.055
## .LadderDif.4 -0.172 0.072 -2.380 0.017 -0.313 -0.030
## .LadderDif.5 -0.194 0.071 -2.731 0.006 -0.334 -0.055
## .gSleep.1 2.901 0.062 46.440 0.000 2.779 3.023
## .gSleep.2 2.984 0.066 45.440 0.000 2.855 3.113
## .gSleep.3 3.092 0.066 46.871 0.000 2.962 3.221
## .gSleep.4 3.063 0.072 42.607 0.000 2.922 3.204
## .gSleep.5 3.067 0.074 41.184 0.000 2.921 3.213
## .posEmo.1 0.078 0.063 1.242 0.214 -0.045 0.201
## .posEmo.2 0.071 0.066 1.067 0.286 -0.059 0.201
## .posEmo.3 0.101 0.070 1.443 0.149 -0.036 0.238
## .posEmo.4 0.094 0.073 1.281 0.200 -0.050 0.237
## .posEmo.5 0.108 0.073 1.476 0.140 -0.035 0.252
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.151 -0.151
## -0.158 -0.156
## -0.190 -0.192
## -0.172 -0.173
## -0.194 -0.202
## 2.901 2.971
## 2.984 3.036
## 3.092 3.358
## 3.063 3.072
## 3.067 3.044
## 0.078 0.078
## 0.071 0.073
## 0.101 0.104
## 0.094 0.094
## 0.108 0.110
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.913 0.062 14.674 0.000 0.791 1.035
## wy1 0.903 0.061 14.693 0.000 0.782 1.023
## wm1 0.954 0.065 14.704 0.000 0.827 1.081
## .wx2 0.695 0.057 12.261 0.000 0.584 0.807
## .wy2 0.369 0.030 12.252 0.000 0.310 0.428
## .wm2 0.570 0.046 12.276 0.000 0.479 0.661
## .wx3 0.562 0.051 10.981 0.000 0.462 0.662
## .wy3 0.345 0.031 10.971 0.000 0.284 0.407
## .wm3 0.478 0.044 10.985 0.000 0.393 0.564
## .wx4 0.611 0.058 10.458 0.000 0.496 0.725
## .wy4 0.380 0.036 10.459 0.000 0.309 0.451
## .wm4 0.443 0.042 10.461 0.000 0.360 0.525
## .wx5 0.488 0.047 10.292 0.000 0.395 0.581
## .wy5 0.392 0.038 10.294 0.000 0.318 0.467
## .wm5 0.355 0.035 10.273 0.000 0.287 0.423
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.735 0.735
## 0.404 0.404
## 0.634 0.634
## 0.623 0.623
## 0.434 0.434
## 0.524 0.524
## 0.677 0.677
## 0.403 0.403
## 0.467 0.467
## 0.583 0.583
## 0.407 0.407
## 0.380 0.380
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Perceived status difference at time t does not predict positive emotions at time t+1
Positive emotions at time t predict sleep at time t+1, b = .07, p = .005
# Same model as above code, but fit with d_black dataset this time
PEmoSleepCLPM_b2AR_controls.fit <- lavaan(PEmoSleepCLPM_2AR_controls, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoSleepCLPM_b2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 59 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 451 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 182.009
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1768.041
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956
## Tucker-Lewis Index (TLI) 0.934
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4609.890
## Loglikelihood unrestricted model (H1) -4518.885
##
## Akaike (AIC) 9387.779
## Bayesian (BIC) 9733.142
## Sample-size adjusted Bayesian (BIC) 9466.558
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.038
## 90 Percent confidence interval - lower 0.028
## 90 Percent confidence interval - upper 0.047
## P-value RMSEA <= 0.05 0.984
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## gSleep.1 1.000 1.000 1.000
## gSleep.2 1.000 1.000 1.000
## gSleep.3 1.000 1.000 1.000
## gSleep.4 1.000 1.000 1.000
## gSleep.5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## gSleep.1 1.000 1.000 1.000
## wy2 =~
## gSleep.2 1.000 1.000 1.000
## wy3 =~
## gSleep.3 1.000 1.000 1.000
## wy4 =~
## gSleep.4 1.000 1.000 1.000
## wy5 =~
## gSleep.5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.994 0.985
##
## 0.968 0.984
##
## 0.968 0.984
##
## 0.977 0.985
##
## 0.952 0.984
##
## 0.967 0.993
##
## 0.988 0.993
##
## 0.987 0.993
##
## 1.004 0.993
##
## 0.975 0.993
##
## 0.997 0.994
##
## 0.992 0.994
##
## 0.980 0.994
##
## 0.949 0.993
##
## 0.965 0.993
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) -0.135 0.076 -1.781 0.075 -0.283 0.014
## LadderDif.2 ~
## GndrBnr (Gen1) -0.135 0.076 -1.781 0.075 -0.283 0.014
## LadderDif.3 ~
## GndrBnr (Gen1) -0.135 0.076 -1.781 0.075 -0.283 0.014
## LadderDif.4 ~
## GndrBnr (Gen1) -0.135 0.076 -1.781 0.075 -0.283 0.014
## LadderDif.5 ~
## GndrBnr (Gen1) -0.135 0.076 -1.781 0.075 -0.283 0.014
## posEmo.1 ~
## GndrBnr (Gen2) -0.210 0.086 -2.446 0.014 -0.379 -0.042
## posEmo.2 ~
## GndrBnr (Gen2) -0.210 0.086 -2.446 0.014 -0.379 -0.042
## posEmo.3 ~
## GndrBnr (Gen2) -0.210 0.086 -2.446 0.014 -0.379 -0.042
## posEmo.4 ~
## GndrBnr (Gen2) -0.210 0.086 -2.446 0.014 -0.379 -0.042
## posEmo.5 ~
## GndrBnr (Gen2) -0.210 0.086 -2.446 0.014 -0.379 -0.042
## gSleep.1 ~
## GndrBnr (Gen3) -0.150 0.086 -1.746 0.081 -0.318 0.018
## gSleep.2 ~
## GndrBnr (Gen3) -0.150 0.086 -1.746 0.081 -0.318 0.018
## gSleep.3 ~
## GndrBnr (Gen3) -0.150 0.086 -1.746 0.081 -0.318 0.018
## gSleep.4 ~
## GndrBnr (Gen3) -0.150 0.086 -1.746 0.081 -0.318 0.018
## gSleep.5 ~
## GndrBnr (Gen3) -0.150 0.086 -1.746 0.081 -0.318 0.018
## LadderDif.1 ~
## Edu (Edu1) -0.075 0.040 -1.893 0.058 -0.152 0.003
## LadderDif.2 ~
## Edu (Edu1) -0.075 0.040 -1.893 0.058 -0.152 0.003
## LadderDif.3 ~
## Edu (Edu1) -0.075 0.040 -1.893 0.058 -0.152 0.003
## LadderDif.4 ~
## Edu (Edu1) -0.075 0.040 -1.893 0.058 -0.152 0.003
## LadderDif.5 ~
## Edu (Edu1) -0.075 0.040 -1.893 0.058 -0.152 0.003
## posEmo.1 ~
## Edu (Edu2) 0.000 0.045 0.002 0.999 -0.088 0.088
## posEmo.2 ~
## Edu (Edu2) 0.000 0.045 0.002 0.999 -0.088 0.088
## posEmo.3 ~
## Edu (Edu2) 0.000 0.045 0.002 0.999 -0.088 0.088
## posEmo.4 ~
## Edu (Edu2) 0.000 0.045 0.002 0.999 -0.088 0.088
## posEmo.5 ~
## Edu (Edu2) 0.000 0.045 0.002 0.999 -0.088 0.088
## gSleep.1 ~
## Edu (Edu3) -0.016 0.045 -0.366 0.714 -0.105 0.072
## gSleep.2 ~
## Edu (Edu3) -0.016 0.045 -0.366 0.714 -0.105 0.072
## gSleep.3 ~
## Edu (Edu3) -0.016 0.045 -0.366 0.714 -0.105 0.072
## gSleep.4 ~
## Edu (Edu3) -0.016 0.045 -0.366 0.714 -0.105 0.072
## gSleep.5 ~
## Edu (Edu3) -0.016 0.045 -0.366 0.714 -0.105 0.072
## LadderDif.1 ~
## Income (Inc1) -0.108 0.039 -2.789 0.005 -0.184 -0.032
## LadderDif.2 ~
## Income (Inc1) -0.108 0.039 -2.789 0.005 -0.184 -0.032
## LadderDif.3 ~
## Income (Inc1) -0.108 0.039 -2.789 0.005 -0.184 -0.032
## LadderDif.4 ~
## Income (Inc1) -0.108 0.039 -2.789 0.005 -0.184 -0.032
## LadderDif.5 ~
## Income (Inc1) -0.108 0.039 -2.789 0.005 -0.184 -0.032
## posEmo.1 ~
## Income (Inc2) 0.040 0.044 0.903 0.366 -0.047 0.126
## posEmo.2 ~
## Income (Inc2) 0.040 0.044 0.903 0.366 -0.047 0.126
## posEmo.3 ~
## Income (Inc2) 0.040 0.044 0.903 0.366 -0.047 0.126
## posEmo.4 ~
## Income (Inc2) 0.040 0.044 0.903 0.366 -0.047 0.126
## posEmo.5 ~
## Income (Inc2) 0.040 0.044 0.903 0.366 -0.047 0.126
## gSleep.1 ~
## Income (Inc3) 0.095 0.044 2.176 0.030 0.009 0.181
## gSleep.2 ~
## Income (Inc3) 0.095 0.044 2.176 0.030 0.009 0.181
## gSleep.3 ~
## Income (Inc3) 0.095 0.044 2.176 0.030 0.009 0.181
## gSleep.4 ~
## Income (Inc3) 0.095 0.044 2.176 0.030 0.009 0.181
## gSleep.5 ~
## Income (Inc3) 0.095 0.044 2.176 0.030 0.009 0.181
## LadderDif.1 ~
## Age (Age1) 0.040 0.038 1.051 0.293 -0.034 0.113
## LadderDif.2 ~
## Age (Age1) 0.040 0.038 1.051 0.293 -0.034 0.113
## LadderDif.3 ~
## Age (Age1) 0.040 0.038 1.051 0.293 -0.034 0.113
## LadderDif.4 ~
## Age (Age1) 0.040 0.038 1.051 0.293 -0.034 0.113
## LadderDif.5 ~
## Age (Age1) 0.040 0.038 1.051 0.293 -0.034 0.113
## posEmo.1 ~
## Age (Age2) 0.032 0.043 0.749 0.454 -0.052 0.116
## posEmo.2 ~
## Age (Age2) 0.032 0.043 0.749 0.454 -0.052 0.116
## posEmo.3 ~
## Age (Age2) 0.032 0.043 0.749 0.454 -0.052 0.116
## posEmo.4 ~
## Age (Age2) 0.032 0.043 0.749 0.454 -0.052 0.116
## posEmo.5 ~
## Age (Age2) 0.032 0.043 0.749 0.454 -0.052 0.116
## gSleep.1 ~
## Age (Age3) -0.038 0.043 -0.885 0.376 -0.121 0.046
## gSleep.2 ~
## Age (Age3) -0.038 0.043 -0.885 0.376 -0.121 0.046
## gSleep.3 ~
## Age (Age3) -0.038 0.043 -0.885 0.376 -0.121 0.046
## gSleep.4 ~
## Age (Age3) -0.038 0.043 -0.885 0.376 -0.121 0.046
## gSleep.5 ~
## Age (Age3) -0.038 0.043 -0.885 0.376 -0.121 0.046
## wy2 ~
## wy1 0.605 0.048 12.661 0.000 0.511 0.698
## wm1 (b1) 0.070 0.025 2.788 0.005 0.021 0.120
## wy3 ~
## wx1 (cp1) -0.045 0.030 -1.533 0.125 -0.104 0.013
## wy2 0.493 0.063 7.865 0.000 0.370 0.616
## wm2 (b1) 0.070 0.025 2.788 0.005 0.021 0.120
## wy1 0.254 0.061 4.137 0.000 0.134 0.374
## wy4 ~
## wx2 (cp1) -0.045 0.030 -1.533 0.125 -0.104 0.013
## wy3 0.453 0.064 7.095 0.000 0.328 0.578
## wm3 (b1) 0.070 0.025 2.788 0.005 0.021 0.120
## wy2 0.372 0.061 6.044 0.000 0.251 0.492
## wy5 ~
## wx3 (cp1) -0.045 0.030 -1.533 0.125 -0.104 0.013
## wy4 0.500 0.061 8.269 0.000 0.382 0.619
## wm4 (b1) 0.070 0.025 2.788 0.005 0.021 0.120
## wy3 0.328 0.061 5.347 0.000 0.208 0.449
## wx2 ~
## wx1 0.218 0.061 3.556 0.000 0.098 0.339
## wm1 (b2) -0.039 0.031 -1.236 0.216 -0.101 0.023
## wx3 ~
## wx2 0.432 0.066 6.561 0.000 0.303 0.561
## wy1 (cp2) -0.004 0.035 -0.118 0.906 -0.073 0.065
## wm2 (b2) -0.039 0.031 -1.236 0.216 -0.101 0.023
## wx1 0.167 0.064 2.619 0.009 0.042 0.292
## wx4 ~
## wx3 0.281 0.078 3.589 0.000 0.127 0.434
## wy2 (cp2) -0.004 0.035 -0.118 0.906 -0.073 0.065
## wm3 (b2) -0.039 0.031 -1.236 0.216 -0.101 0.023
## wx2 0.239 0.079 3.018 0.003 0.084 0.394
## wx5 ~
## wx4 0.253 0.064 3.931 0.000 0.127 0.380
## wy3 (cp2) -0.004 0.035 -0.118 0.906 -0.073 0.065
## wm4 (b2) -0.039 0.031 -1.236 0.216 -0.101 0.023
## wx3 0.411 0.069 5.924 0.000 0.275 0.547
## wm2 ~
## wx1 (a1) -0.037 0.025 -1.472 0.141 -0.087 0.012
## wy1 (a2) 0.032 0.025 1.315 0.188 -0.016 0.081
## wm1 0.518 0.053 9.825 0.000 0.415 0.622
## wm3 ~
## wx2 (a1) -0.037 0.025 -1.472 0.141 -0.087 0.012
## wy2 (a2) 0.032 0.025 1.315 0.188 -0.016 0.081
## wm2 0.588 0.054 10.841 0.000 0.482 0.694
## wm1 0.228 0.053 4.320 0.000 0.124 0.331
## wm4 ~
## wx3 (a1) -0.037 0.025 -1.472 0.141 -0.087 0.012
## wy3 (a2) 0.032 0.025 1.315 0.188 -0.016 0.081
## wm3 0.446 0.070 6.401 0.000 0.310 0.583
## wm2 0.325 0.067 4.821 0.000 0.193 0.456
## wm5 ~
## wx4 (a1) -0.037 0.025 -1.472 0.141 -0.087 0.012
## wy4 (a2) 0.032 0.025 1.315 0.188 -0.016 0.081
## wm4 0.332 0.071 4.663 0.000 0.192 0.471
## wm3 0.476 0.068 6.995 0.000 0.343 0.610
## Std.lv Std.all
##
## -0.135 -0.067
##
## -0.135 -0.068
##
## -0.135 -0.068
##
## -0.135 -0.068
##
## -0.135 -0.070
##
## -0.210 -0.105
##
## -0.210 -0.105
##
## -0.210 -0.106
##
## -0.210 -0.110
##
## -0.210 -0.108
##
## -0.150 -0.077
##
## -0.150 -0.075
##
## -0.150 -0.075
##
## -0.150 -0.074
##
## -0.150 -0.076
##
## -0.075 -0.074
##
## -0.075 -0.076
##
## -0.075 -0.076
##
## -0.075 -0.075
##
## -0.075 -0.077
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## -0.016 -0.017
##
## -0.016 -0.017
##
## -0.016 -0.017
##
## -0.016 -0.016
##
## -0.016 -0.017
##
## -0.108 -0.107
##
## -0.108 -0.110
##
## -0.108 -0.110
##
## -0.108 -0.109
##
## -0.108 -0.111
##
## 0.040 0.040
##
## 0.040 0.040
##
## 0.040 0.040
##
## 0.040 0.042
##
## 0.040 0.041
##
## 0.095 0.098
##
## 0.095 0.096
##
## 0.095 0.096
##
## 0.095 0.094
##
## 0.095 0.097
##
## 0.040 0.039
##
## 0.040 0.040
##
## 0.040 0.040
##
## 0.040 0.040
##
## 0.040 0.041
##
## 0.032 0.032
##
## 0.032 0.032
##
## 0.032 0.032
##
## 0.032 0.034
##
## 0.032 0.033
##
## -0.038 -0.039
##
## -0.038 -0.038
##
## -0.038 -0.038
##
## -0.038 -0.037
##
## -0.038 -0.038
##
## 0.592 0.592
## 0.071 0.071
##
## -0.046 -0.046
## 0.493 0.493
## 0.071 0.071
## 0.249 0.249
##
## -0.044 -0.044
## 0.445 0.445
## 0.069 0.069
## 0.365 0.365
##
## -0.045 -0.045
## 0.516 0.516
## 0.069 0.069
## 0.332 0.332
##
## 0.224 0.224
## -0.040 -0.040
##
## 0.432 0.432
## -0.004 -0.004
## -0.040 -0.040
## 0.171 0.171
##
## 0.278 0.278
## -0.004 -0.004
## -0.039 -0.039
## 0.237 0.237
##
## 0.260 0.260
## -0.004 -0.004
## -0.039 -0.039
## 0.418 0.418
##
## -0.037 -0.037
## 0.032 0.032
## 0.521 0.521
##
## -0.037 -0.037
## 0.033 0.033
## 0.595 0.595
## 0.232 0.232
##
## -0.038 -0.038
## 0.034 0.034
## 0.461 0.461
## 0.339 0.339
##
## -0.038 -0.038
## 0.034 0.034
## 0.326 0.326
## 0.484 0.484
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.053 0.045 1.164 0.245 -0.036 0.142
## wm1 -0.012 0.047 -0.260 0.795 -0.104 0.080
## wy1 ~~
## wm1 0.300 0.048 6.301 0.000 0.207 0.393
## .wx2 ~~
## .wy2 -0.010 0.046 -0.209 0.834 -0.100 0.081
## .wx3 ~~
## .wy3 -0.059 0.042 -1.406 0.160 -0.141 0.023
## .wx4 ~~
## .wy4 -0.023 0.042 -0.543 0.587 -0.105 0.060
## .wx5 ~~
## .wy5 0.063 0.034 1.882 0.060 -0.003 0.129
## .wx2 ~~
## .wm2 -0.034 0.050 -0.677 0.499 -0.132 0.064
## .wx3 ~~
## .wm3 -0.010 0.038 -0.256 0.798 -0.084 0.065
## .wx4 ~~
## .wm4 0.014 0.040 0.341 0.733 -0.064 0.092
## .wx5 ~~
## .wm5 -0.058 0.037 -1.565 0.118 -0.130 0.015
## .wy2 ~~
## .wm2 0.094 0.041 2.273 0.023 0.013 0.175
## .wy3 ~~
## .wm3 0.073 0.033 2.212 0.027 0.008 0.137
## .wy4 ~~
## .wm4 0.035 0.030 1.175 0.240 -0.023 0.093
## .wy5 ~~
## .wm5 -0.007 0.027 -0.266 0.790 -0.059 0.045
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.055 0.055
## -0.012 -0.012
##
## 0.311 0.311
##
## -0.013 -0.013
##
## -0.100 -0.100
##
## -0.040 -0.040
##
## 0.144 0.144
##
## -0.043 -0.043
##
## -0.018 -0.018
##
## 0.025 0.025
##
## -0.120 -0.120
##
## 0.145 0.145
##
## 0.160 0.160
##
## 0.087 0.087
##
## -0.020 -0.020
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.071 0.061 1.155 0.248 -0.049 0.191
## .LadderDif.2 0.129 0.073 1.782 0.075 -0.013 0.272
## .LadderDif.3 0.111 0.077 1.440 0.150 -0.040 0.262
## .LadderDif.4 0.152 0.081 1.872 0.061 -0.007 0.310
## .LadderDif.5 0.130 0.081 1.609 0.108 -0.028 0.288
## .gSleep.1 3.109 0.064 48.563 0.000 2.984 3.235
## .gSleep.2 3.132 0.073 43.156 0.000 2.990 3.275
## .gSleep.3 3.287 0.077 42.807 0.000 3.136 3.437
## .gSleep.4 3.366 0.080 42.223 0.000 3.209 3.522
## .gSleep.5 3.348 0.080 41.916 0.000 3.191 3.504
## .posEmo.1 0.110 0.065 1.687 0.092 -0.018 0.237
## .posEmo.2 0.105 0.074 1.411 0.158 -0.041 0.250
## .posEmo.3 0.107 0.076 1.408 0.159 -0.042 0.256
## .posEmo.4 0.087 0.077 1.121 0.262 -0.065 0.238
## .posEmo.5 0.080 0.080 0.996 0.319 -0.077 0.236
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.071 0.070
## 0.129 0.132
## 0.111 0.113
## 0.152 0.153
## 0.130 0.134
## 3.109 3.190
## 3.132 3.149
## 3.287 3.305
## 3.366 3.328
## 3.348 3.409
## 0.110 0.109
## 0.105 0.105
## 0.107 0.108
## 0.087 0.091
## 0.080 0.082
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.988 0.066 14.949 0.000 0.858 1.118
## wy1 0.936 0.062 15.002 0.000 0.814 1.058
## wm1 0.994 0.066 14.999 0.000 0.864 1.124
## .wx2 0.889 0.080 11.178 0.000 0.733 1.045
## .wy2 0.603 0.054 11.232 0.000 0.498 0.708
## .wm2 0.703 0.063 11.223 0.000 0.580 0.826
## .wx3 0.699 0.070 10.038 0.000 0.563 0.836
## .wy3 0.503 0.050 10.031 0.000 0.404 0.601
## .wm3 0.410 0.041 10.031 0.000 0.330 0.490
## .wx4 0.762 0.079 9.649 0.000 0.607 0.917
## .wy4 0.418 0.043 9.648 0.000 0.333 0.503
## .wm4 0.381 0.039 9.644 0.000 0.303 0.458
## .wx5 0.603 0.064 9.435 0.000 0.478 0.728
## .wy5 0.319 0.034 9.433 0.000 0.253 0.386
## .wm5 0.381 0.040 9.438 0.000 0.302 0.460
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .gSleep.1 0.000 0.000 0.000
## .gSleep.2 0.000 0.000 0.000
## .gSleep.3 0.000 0.000 0.000
## .gSleep.4 0.000 0.000 0.000
## .gSleep.5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.948 0.948
## 0.618 0.618
## 0.715 0.715
## 0.746 0.746
## 0.515 0.515
## 0.427 0.427
## 0.799 0.799
## 0.415 0.415
## 0.423 0.423
## 0.665 0.665
## 0.336 0.336
## 0.409 0.409
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000These models look at individual health items as outcome variables: pain, fatigue, and physical ability. NOTE: I did not use the reverse scored version of the pain and fatigue variables, so higher values indicate more pain and fatigue. Higher physical ability scores indicate better physical ability.
(a1) Perceived status difference at time t predicts less positive emotions at time t+1, b = -.07, p = .004
(b1) Positive emotions at time t do not predict pain at time t+1
PEmoPainCLPM_2AR_controls <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*Pain_1 + 1*Pain_2 + 1*Pain_3 + 1*Pain_4 + 1*Pain_5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*Pain_1
wy2 =~ 1*Pain_2
wy3 =~ 1*Pain_3
wy4 =~ 1*Pain_4
wy5 =~ 1*Pain_5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Regression of observed variables on controls (constrained).
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Gen1*GenderBinary
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Gen2*GenderBinary
Pain_1 + Pain_2 + Pain_3 + Pain_4 + Pain_5 ~ Gen3*GenderBinary
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Edu1*Edu
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Edu2*Edu
Pain_1 + Pain_2 + Pain_3 + Pain_4 + Pain_5 ~ Edu3*Edu
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Inc1*Income
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Inc2*Income
Pain_1 + Pain_2 + Pain_3 + Pain_4 + Pain_5 ~ Inc3*Income
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Age1*Age
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Age2*Age
Pain_1 + Pain_2 + Pain_3 + Pain_4 + Pain_5 ~ Age3*Age
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoPainCLPM_w2AR_controls.fit <- lavaan(PEmoPainCLPM_2AR_controls, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoPainCLPM_w2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 65 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 433 482
## Number of missing patterns 8
##
## Model Test User Model:
##
## Test statistic 214.424
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2416.129
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.954
## Tucker-Lewis Index (TLI) 0.932
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4868.570
## Loglikelihood unrestricted model (H1) -4761.358
##
## Akaike (AIC) 9905.140
## Bayesian (BIC) 10247.082
## Sample-size adjusted Bayesian (BIC) 9980.512
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046
## 90 Percent confidence interval - lower 0.037
## 90 Percent confidence interval - upper 0.056
## P-value RMSEA <= 0.05 0.729
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.046
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## Pain_1 1.000 1.000 1.000
## Pain_2 1.000 1.000 1.000
## Pain_3 1.000 1.000 1.000
## Pain_4 1.000 1.000 1.000
## Pain_5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## Pain_1 1.000 1.000 1.000
## wy2 =~
## Pain_2 1.000 1.000 1.000
## wy3 =~
## Pain_3 1.000 1.000 1.000
## wy4 =~
## Pain_4 1.000 1.000 1.000
## wy5 =~
## Pain_5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.956 0.955
##
## 0.969 0.956
##
## 0.950 0.955
##
## 0.952 0.955
##
## 0.921 0.952
##
## 0.969 0.956
##
## 0.925 0.952
##
## 0.855 0.945
##
## 0.894 0.949
##
## 0.893 0.949
##
## 0.977 0.981
##
## 0.950 0.979
##
## 0.958 0.980
##
## 0.971 0.980
##
## 0.972 0.980
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) 0.319 0.077 4.144 0.000 0.168 0.469
## LadderDif.2 ~
## GndrBnr (Gen1) 0.319 0.077 4.144 0.000 0.168 0.469
## LadderDif.3 ~
## GndrBnr (Gen1) 0.319 0.077 4.144 0.000 0.168 0.469
## LadderDif.4 ~
## GndrBnr (Gen1) 0.319 0.077 4.144 0.000 0.168 0.469
## LadderDif.5 ~
## GndrBnr (Gen1) 0.319 0.077 4.144 0.000 0.168 0.469
## posEmo.1 ~
## GndrBnr (Gen2) -0.154 0.082 -1.880 0.060 -0.314 0.007
## posEmo.2 ~
## GndrBnr (Gen2) -0.154 0.082 -1.880 0.060 -0.314 0.007
## posEmo.3 ~
## GndrBnr (Gen2) -0.154 0.082 -1.880 0.060 -0.314 0.007
## posEmo.4 ~
## GndrBnr (Gen2) -0.154 0.082 -1.880 0.060 -0.314 0.007
## posEmo.5 ~
## GndrBnr (Gen2) -0.154 0.082 -1.880 0.060 -0.314 0.007
## Pain_1 ~
## GndrBnr (Gen3) 0.148 0.080 1.857 0.063 -0.008 0.304
## Pain_2 ~
## GndrBnr (Gen3) 0.148 0.080 1.857 0.063 -0.008 0.304
## Pain_3 ~
## GndrBnr (Gen3) 0.148 0.080 1.857 0.063 -0.008 0.304
## Pain_4 ~
## GndrBnr (Gen3) 0.148 0.080 1.857 0.063 -0.008 0.304
## Pain_5 ~
## GndrBnr (Gen3) 0.148 0.080 1.857 0.063 -0.008 0.304
## LadderDif.1 ~
## Edu (Edu1) 0.021 0.041 0.516 0.606 -0.059 0.101
## LadderDif.2 ~
## Edu (Edu1) 0.021 0.041 0.516 0.606 -0.059 0.101
## LadderDif.3 ~
## Edu (Edu1) 0.021 0.041 0.516 0.606 -0.059 0.101
## LadderDif.4 ~
## Edu (Edu1) 0.021 0.041 0.516 0.606 -0.059 0.101
## LadderDif.5 ~
## Edu (Edu1) 0.021 0.041 0.516 0.606 -0.059 0.101
## posEmo.1 ~
## Edu (Edu2) -0.063 0.044 -1.436 0.151 -0.149 0.023
## posEmo.2 ~
## Edu (Edu2) -0.063 0.044 -1.436 0.151 -0.149 0.023
## posEmo.3 ~
## Edu (Edu2) -0.063 0.044 -1.436 0.151 -0.149 0.023
## posEmo.4 ~
## Edu (Edu2) -0.063 0.044 -1.436 0.151 -0.149 0.023
## posEmo.5 ~
## Edu (Edu2) -0.063 0.044 -1.436 0.151 -0.149 0.023
## Pain_1 ~
## Edu (Edu3) -0.204 0.042 -4.847 0.000 -0.286 -0.122
## Pain_2 ~
## Edu (Edu3) -0.204 0.042 -4.847 0.000 -0.286 -0.122
## Pain_3 ~
## Edu (Edu3) -0.204 0.042 -4.847 0.000 -0.286 -0.122
## Pain_4 ~
## Edu (Edu3) -0.204 0.042 -4.847 0.000 -0.286 -0.122
## Pain_5 ~
## Edu (Edu3) -0.204 0.042 -4.847 0.000 -0.286 -0.122
## LadderDif.1 ~
## Income (Inc1) -0.253 0.041 -6.197 0.000 -0.333 -0.173
## LadderDif.2 ~
## Income (Inc1) -0.253 0.041 -6.197 0.000 -0.333 -0.173
## LadderDif.3 ~
## Income (Inc1) -0.253 0.041 -6.197 0.000 -0.333 -0.173
## LadderDif.4 ~
## Income (Inc1) -0.253 0.041 -6.197 0.000 -0.333 -0.173
## LadderDif.5 ~
## Income (Inc1) -0.253 0.041 -6.197 0.000 -0.333 -0.173
## posEmo.1 ~
## Income (Inc2) 0.154 0.043 3.535 0.000 0.068 0.239
## posEmo.2 ~
## Income (Inc2) 0.154 0.043 3.535 0.000 0.068 0.239
## posEmo.3 ~
## Income (Inc2) 0.154 0.043 3.535 0.000 0.068 0.239
## posEmo.4 ~
## Income (Inc2) 0.154 0.043 3.535 0.000 0.068 0.239
## posEmo.5 ~
## Income (Inc2) 0.154 0.043 3.535 0.000 0.068 0.239
## Pain_1 ~
## Income (Inc3) -0.109 0.042 -2.590 0.010 -0.192 -0.027
## Pain_2 ~
## Income (Inc3) -0.109 0.042 -2.590 0.010 -0.192 -0.027
## Pain_3 ~
## Income (Inc3) -0.109 0.042 -2.590 0.010 -0.192 -0.027
## Pain_4 ~
## Income (Inc3) -0.109 0.042 -2.590 0.010 -0.192 -0.027
## Pain_5 ~
## Income (Inc3) -0.109 0.042 -2.590 0.010 -0.192 -0.027
## LadderDif.1 ~
## Age (Age1) -0.015 0.039 -0.392 0.695 -0.091 0.061
## LadderDif.2 ~
## Age (Age1) -0.015 0.039 -0.392 0.695 -0.091 0.061
## LadderDif.3 ~
## Age (Age1) -0.015 0.039 -0.392 0.695 -0.091 0.061
## LadderDif.4 ~
## Age (Age1) -0.015 0.039 -0.392 0.695 -0.091 0.061
## LadderDif.5 ~
## Age (Age1) -0.015 0.039 -0.392 0.695 -0.091 0.061
## posEmo.1 ~
## Age (Age2) 0.097 0.041 2.359 0.018 0.016 0.178
## posEmo.2 ~
## Age (Age2) 0.097 0.041 2.359 0.018 0.016 0.178
## posEmo.3 ~
## Age (Age2) 0.097 0.041 2.359 0.018 0.016 0.178
## posEmo.4 ~
## Age (Age2) 0.097 0.041 2.359 0.018 0.016 0.178
## posEmo.5 ~
## Age (Age2) 0.097 0.041 2.359 0.018 0.016 0.178
## Pain_1 ~
## Age (Age3) 0.136 0.040 3.419 0.001 0.058 0.215
## Pain_2 ~
## Age (Age3) 0.136 0.040 3.419 0.001 0.058 0.215
## Pain_3 ~
## Age (Age3) 0.136 0.040 3.419 0.001 0.058 0.215
## Pain_4 ~
## Age (Age3) 0.136 0.040 3.419 0.001 0.058 0.215
## Pain_5 ~
## Age (Age3) 0.136 0.040 3.419 0.001 0.058 0.215
## wy2 ~
## wy1 0.614 0.041 15.001 0.000 0.534 0.695
## wm1 (b1) 0.000 0.021 0.008 0.993 -0.041 0.041
## wy3 ~
## wx1 (cp1) -0.012 0.026 -0.457 0.648 -0.063 0.039
## wy2 0.432 0.052 8.377 0.000 0.331 0.533
## wm2 (b1) 0.000 0.021 0.008 0.993 -0.041 0.041
## wy1 0.300 0.049 6.140 0.000 0.204 0.396
## wy4 ~
## wx2 (cp1) -0.012 0.026 -0.457 0.648 -0.063 0.039
## wy3 0.500 0.070 7.157 0.000 0.363 0.637
## wm3 (b1) 0.000 0.021 0.008 0.993 -0.041 0.041
## wy2 0.280 0.064 4.376 0.000 0.154 0.405
## wy5 ~
## wx3 (cp1) -0.012 0.026 -0.457 0.648 -0.063 0.039
## wy4 0.375 0.065 5.768 0.000 0.248 0.503
## wm4 (b1) 0.000 0.021 0.008 0.993 -0.041 0.041
## wy3 0.434 0.068 6.405 0.000 0.301 0.567
## wx2 ~
## wx1 0.508 0.050 10.107 0.000 0.410 0.607
## wm1 (b2) -0.034 0.025 -1.323 0.186 -0.083 0.016
## wx3 ~
## wx2 0.288 0.061 4.699 0.000 0.168 0.408
## wy1 (cp2) -0.018 0.031 -0.564 0.573 -0.079 0.044
## wm2 (b2) -0.034 0.025 -1.323 0.186 -0.083 0.016
## wx1 0.405 0.059 6.864 0.000 0.289 0.521
## wx4 ~
## wx3 0.261 0.065 4.016 0.000 0.134 0.389
## wy2 (cp2) -0.018 0.031 -0.564 0.573 -0.079 0.044
## wm3 (b2) -0.034 0.025 -1.323 0.186 -0.083 0.016
## wx2 0.379 0.068 5.583 0.000 0.246 0.512
## wx5 ~
## wx4 0.291 0.057 5.082 0.000 0.179 0.403
## wy3 (cp2) -0.018 0.031 -0.564 0.573 -0.079 0.044
## wm4 (b2) -0.034 0.025 -1.323 0.186 -0.083 0.016
## wx3 0.437 0.056 7.811 0.000 0.328 0.547
## wm2 ~
## wx1 (a1) -0.070 0.024 -2.906 0.004 -0.117 -0.023
## wy1 (a2) -0.009 0.024 -0.360 0.719 -0.056 0.039
## wm1 0.561 0.043 13.028 0.000 0.477 0.646
## wm3 ~
## wx2 (a1) -0.070 0.024 -2.906 0.004 -0.117 -0.023
## wy2 (a2) -0.009 0.024 -0.360 0.719 -0.056 0.039
## wm2 0.550 0.058 9.492 0.000 0.436 0.664
## wm1 0.190 0.057 3.333 0.001 0.078 0.302
## wm4 ~
## wx3 (a1) -0.070 0.024 -2.906 0.004 -0.117 -0.023
## wy3 (a2) -0.009 0.024 -0.360 0.719 -0.056 0.039
## wm3 0.532 0.063 8.433 0.000 0.408 0.656
## wm2 0.256 0.065 3.953 0.000 0.129 0.383
## wm5 ~
## wx4 (a1) -0.070 0.024 -2.906 0.004 -0.117 -0.023
## wy4 (a2) -0.009 0.024 -0.360 0.719 -0.056 0.039
## wm4 0.377 0.060 6.335 0.000 0.260 0.494
## wm3 0.461 0.060 7.709 0.000 0.344 0.578
## Std.lv Std.all
##
## 0.319 0.159
##
## 0.319 0.157
##
## 0.319 0.160
##
## 0.319 0.160
##
## 0.319 0.165
##
## -0.154 -0.077
##
## -0.154 -0.079
##
## -0.154 -0.079
##
## -0.154 -0.077
##
## -0.154 -0.077
##
## 0.148 0.073
##
## 0.148 0.076
##
## 0.148 0.082
##
## 0.148 0.079
##
## 0.148 0.079
##
## 0.021 0.021
##
## 0.021 0.021
##
## 0.021 0.021
##
## 0.021 0.021
##
## 0.021 0.022
##
## -0.063 -0.063
##
## -0.063 -0.065
##
## -0.063 -0.064
##
## -0.063 -0.063
##
## -0.063 -0.063
##
## -0.204 -0.201
##
## -0.204 -0.210
##
## -0.204 -0.225
##
## -0.204 -0.216
##
## -0.204 -0.216
##
## -0.253 -0.253
##
## -0.253 -0.250
##
## -0.253 -0.254
##
## -0.253 -0.254
##
## -0.253 -0.261
##
## 0.154 0.154
##
## 0.154 0.158
##
## 0.154 0.157
##
## 0.154 0.155
##
## 0.154 0.155
##
## -0.109 -0.108
##
## -0.109 -0.113
##
## -0.109 -0.121
##
## -0.109 -0.116
##
## -0.109 -0.116
##
## -0.015 -0.015
##
## -0.015 -0.015
##
## -0.015 -0.015
##
## -0.015 -0.015
##
## -0.015 -0.016
##
## 0.097 0.097
##
## 0.097 0.100
##
## 0.097 0.099
##
## 0.097 0.098
##
## 0.097 0.098
##
## 0.136 0.134
##
## 0.136 0.140
##
## 0.136 0.150
##
## 0.136 0.144
##
## 0.136 0.144
##
## 0.644 0.644
## 0.000 0.000
##
## -0.013 -0.013
## 0.467 0.467
## 0.000 0.000
## 0.340 0.340
##
## -0.013 -0.013
## 0.478 0.478
## 0.000 0.000
## 0.289 0.289
##
## -0.013 -0.013
## 0.376 0.376
## 0.000 0.000
## 0.416 0.416
##
## 0.502 0.502
## -0.034 -0.034
##
## 0.294 0.294
## -0.018 -0.018
## -0.034 -0.034
## 0.407 0.407
##
## 0.261 0.261
## -0.017 -0.017
## -0.034 -0.034
## 0.386 0.386
##
## 0.301 0.301
## -0.016 -0.016
## -0.035 -0.035
## 0.452 0.452
##
## -0.070 -0.070
## -0.009 -0.009
## 0.577 0.577
##
## -0.071 -0.071
## -0.008 -0.008
## 0.546 0.546
## 0.194 0.194
##
## -0.068 -0.068
## -0.008 -0.008
## 0.525 0.525
## 0.251 0.251
##
## -0.068 -0.068
## -0.008 -0.008
## 0.377 0.377
## 0.454 0.454
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.217 0.046 4.730 0.000 0.127 0.307
## wm1 -0.182 0.046 -3.970 0.000 -0.272 -0.092
## wy1 ~~
## wm1 -0.063 0.046 -1.378 0.168 -0.152 0.027
## .wx2 ~~
## .wy2 0.070 0.035 2.026 0.043 0.002 0.138
## .wx3 ~~
## .wy3 0.039 0.028 1.374 0.169 -0.017 0.094
## .wx4 ~~
## .wy4 0.011 0.034 0.313 0.754 -0.057 0.078
## .wx5 ~~
## .wy5 -0.008 0.030 -0.252 0.801 -0.066 0.051
## .wx2 ~~
## .wm2 -0.041 0.037 -1.128 0.260 -0.114 0.031
## .wx3 ~~
## .wm3 0.007 0.034 0.218 0.828 -0.059 0.073
## .wx4 ~~
## .wm4 -0.000 0.035 -0.005 0.996 -0.069 0.069
## .wx5 ~~
## .wm5 -0.011 0.029 -0.381 0.703 -0.068 0.046
## .wy2 ~~
## .wm2 -0.064 0.031 -2.043 0.041 -0.126 -0.003
## .wy3 ~~
## .wm3 0.029 0.026 1.119 0.263 -0.022 0.080
## .wy4 ~~
## .wm4 0.011 0.029 0.371 0.711 -0.046 0.068
## .wy5 ~~
## .wm5 -0.030 0.026 -1.147 0.252 -0.081 0.021
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.234 0.234
## -0.195 -0.195
##
## -0.066 -0.066
##
## 0.119 0.119
##
## 0.089 0.089
##
## 0.022 0.022
##
## -0.018 -0.018
##
## -0.065 -0.065
##
## 0.014 0.014
##
## -0.000 -0.000
##
## -0.026 -0.026
##
## -0.119 -0.119
##
## 0.072 0.072
##
## 0.026 0.026
##
## -0.079 -0.079
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.154 0.061 -2.549 0.011 -0.273 -0.036
## .LadderDif.2 -0.161 0.066 -2.423 0.015 -0.291 -0.031
## .LadderDif.3 -0.194 0.069 -2.822 0.005 -0.329 -0.059
## .LadderDif.4 -0.174 0.072 -2.413 0.016 -0.316 -0.033
## .LadderDif.5 -0.197 0.071 -2.764 0.006 -0.337 -0.057
## .Pain_1 2.175 0.062 35.059 0.000 2.053 2.296
## .Pain_2 2.158 0.064 33.578 0.000 2.032 2.284
## .Pain_3 2.157 0.064 33.806 0.000 2.032 2.283
## .Pain_4 2.153 0.068 31.465 0.000 2.019 2.287
## .Pain_5 2.149 0.070 30.871 0.000 2.012 2.285
## .posEmo.1 0.078 0.063 1.246 0.213 -0.045 0.202
## .posEmo.2 0.076 0.066 1.142 0.253 -0.054 0.206
## .posEmo.3 0.105 0.070 1.498 0.134 -0.032 0.243
## .posEmo.4 0.098 0.073 1.344 0.179 -0.045 0.241
## .posEmo.5 0.110 0.074 1.493 0.135 -0.034 0.255
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.154 -0.154
## -0.161 -0.159
## -0.194 -0.195
## -0.174 -0.175
## -0.197 -0.204
## 2.175 2.145
## 2.158 2.223
## 2.157 2.385
## 2.153 2.284
## 2.149 2.284
## 0.078 0.079
## 0.076 0.078
## 0.105 0.107
## 0.098 0.099
## 0.110 0.111
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.914 0.062 14.668 0.000 0.792 1.036
## wy1 0.940 0.064 14.691 0.000 0.814 1.065
## wm1 0.954 0.065 14.704 0.000 0.827 1.081
## .wx2 0.695 0.057 12.263 0.000 0.584 0.806
## .wy2 0.500 0.041 12.252 0.000 0.420 0.580
## .wm2 0.582 0.047 12.273 0.000 0.489 0.675
## .wx3 0.561 0.051 10.989 0.000 0.461 0.661
## .wy3 0.340 0.031 10.980 0.000 0.279 0.400
## .wm3 0.475 0.043 11.004 0.000 0.390 0.560
## .wx4 0.613 0.059 10.465 0.000 0.498 0.727
## .wy4 0.401 0.038 10.455 0.000 0.326 0.476
## .wm4 0.438 0.042 10.467 0.000 0.356 0.520
## .wx5 0.486 0.047 10.294 0.000 0.394 0.579
## .wy5 0.380 0.037 10.280 0.000 0.308 0.453
## .wm5 0.370 0.036 10.304 0.000 0.299 0.440
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .Pain_1 0.000 0.000 0.000
## .Pain_2 0.000 0.000 0.000
## .Pain_3 0.000 0.000 0.000
## .Pain_4 0.000 0.000 0.000
## .Pain_5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.741 0.741
## 0.585 0.585
## 0.645 0.645
## 0.621 0.621
## 0.465 0.465
## 0.518 0.518
## 0.676 0.676
## 0.501 0.501
## 0.464 0.464
## 0.574 0.574
## 0.477 0.477
## 0.391 0.391
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Perceived status difference at time t does not predict positive emotions at time t+1
Positive emotions at time t do not predict pain at time t+1
# Same model as above code, but fit with d_black dataset this time
PEmoPainCLPM_b2AR_controls.fit <- lavaan(PEmoPainCLPM_2AR_controls, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoPainCLPM_b2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 50 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 451 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 197.057
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1620.779
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.941
## Tucker-Lewis Index (TLI) 0.912
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4713.956
## Loglikelihood unrestricted model (H1) -4615.427
##
## Akaike (AIC) 9595.911
## Bayesian (BIC) 9941.275
## Sample-size adjusted Bayesian (BIC) 9674.690
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.041
## 90 Percent confidence interval - lower 0.032
## 90 Percent confidence interval - upper 0.051
## P-value RMSEA <= 0.05 0.933
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.047
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## Pain_1 1.000 1.000 1.000
## Pain_2 1.000 1.000 1.000
## Pain_3 1.000 1.000 1.000
## Pain_4 1.000 1.000 1.000
## Pain_5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## Pain_1 1.000 1.000 1.000
## wy2 =~
## Pain_2 1.000 1.000 1.000
## wy3 =~
## Pain_3 1.000 1.000 1.000
## wy4 =~
## Pain_4 1.000 1.000 1.000
## wy5 =~
## Pain_5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.994 0.985
##
## 0.968 0.984
##
## 0.969 0.984
##
## 0.978 0.985
##
## 0.949 0.984
##
## 1.052 0.987
##
## 1.044 0.987
##
## 0.929 0.984
##
## 0.992 0.986
##
## 1.001 0.986
##
## 0.997 0.994
##
## 0.993 0.994
##
## 0.982 0.994
##
## 0.955 0.994
##
## 0.972 0.994
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) -0.128 0.076 -1.696 0.090 -0.276 0.020
## LadderDif.2 ~
## GndrBnr (Gen1) -0.128 0.076 -1.696 0.090 -0.276 0.020
## LadderDif.3 ~
## GndrBnr (Gen1) -0.128 0.076 -1.696 0.090 -0.276 0.020
## LadderDif.4 ~
## GndrBnr (Gen1) -0.128 0.076 -1.696 0.090 -0.276 0.020
## LadderDif.5 ~
## GndrBnr (Gen1) -0.128 0.076 -1.696 0.090 -0.276 0.020
## posEmo.1 ~
## GndrBnr (Gen2) -0.208 0.086 -2.419 0.016 -0.376 -0.039
## posEmo.2 ~
## GndrBnr (Gen2) -0.208 0.086 -2.419 0.016 -0.376 -0.039
## posEmo.3 ~
## GndrBnr (Gen2) -0.208 0.086 -2.419 0.016 -0.376 -0.039
## posEmo.4 ~
## GndrBnr (Gen2) -0.208 0.086 -2.419 0.016 -0.376 -0.039
## posEmo.5 ~
## GndrBnr (Gen2) -0.208 0.086 -2.419 0.016 -0.376 -0.039
## Pain_1 ~
## GndrBnr (Gen3) -0.035 0.089 -0.397 0.691 -0.210 0.139
## Pain_2 ~
## GndrBnr (Gen3) -0.035 0.089 -0.397 0.691 -0.210 0.139
## Pain_3 ~
## GndrBnr (Gen3) -0.035 0.089 -0.397 0.691 -0.210 0.139
## Pain_4 ~
## GndrBnr (Gen3) -0.035 0.089 -0.397 0.691 -0.210 0.139
## Pain_5 ~
## GndrBnr (Gen3) -0.035 0.089 -0.397 0.691 -0.210 0.139
## LadderDif.1 ~
## Edu (Edu1) -0.077 0.040 -1.953 0.051 -0.155 0.000
## LadderDif.2 ~
## Edu (Edu1) -0.077 0.040 -1.953 0.051 -0.155 0.000
## LadderDif.3 ~
## Edu (Edu1) -0.077 0.040 -1.953 0.051 -0.155 0.000
## LadderDif.4 ~
## Edu (Edu1) -0.077 0.040 -1.953 0.051 -0.155 0.000
## LadderDif.5 ~
## Edu (Edu1) -0.077 0.040 -1.953 0.051 -0.155 0.000
## posEmo.1 ~
## Edu (Edu2) 0.000 0.045 0.003 0.997 -0.088 0.089
## posEmo.2 ~
## Edu (Edu2) 0.000 0.045 0.003 0.997 -0.088 0.089
## posEmo.3 ~
## Edu (Edu2) 0.000 0.045 0.003 0.997 -0.088 0.089
## posEmo.4 ~
## Edu (Edu2) 0.000 0.045 0.003 0.997 -0.088 0.089
## posEmo.5 ~
## Edu (Edu2) 0.000 0.045 0.003 0.997 -0.088 0.089
## Pain_1 ~
## Edu (Edu3) -0.086 0.047 -1.849 0.064 -0.178 0.005
## Pain_2 ~
## Edu (Edu3) -0.086 0.047 -1.849 0.064 -0.178 0.005
## Pain_3 ~
## Edu (Edu3) -0.086 0.047 -1.849 0.064 -0.178 0.005
## Pain_4 ~
## Edu (Edu3) -0.086 0.047 -1.849 0.064 -0.178 0.005
## Pain_5 ~
## Edu (Edu3) -0.086 0.047 -1.849 0.064 -0.178 0.005
## LadderDif.1 ~
## Income (Inc1) -0.108 0.039 -2.790 0.005 -0.184 -0.032
## LadderDif.2 ~
## Income (Inc1) -0.108 0.039 -2.790 0.005 -0.184 -0.032
## LadderDif.3 ~
## Income (Inc1) -0.108 0.039 -2.790 0.005 -0.184 -0.032
## LadderDif.4 ~
## Income (Inc1) -0.108 0.039 -2.790 0.005 -0.184 -0.032
## LadderDif.5 ~
## Income (Inc1) -0.108 0.039 -2.790 0.005 -0.184 -0.032
## posEmo.1 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.2 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.3 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.4 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.5 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## Pain_1 ~
## Income (Inc3) -0.098 0.046 -2.159 0.031 -0.188 -0.009
## Pain_2 ~
## Income (Inc3) -0.098 0.046 -2.159 0.031 -0.188 -0.009
## Pain_3 ~
## Income (Inc3) -0.098 0.046 -2.159 0.031 -0.188 -0.009
## Pain_4 ~
## Income (Inc3) -0.098 0.046 -2.159 0.031 -0.188 -0.009
## Pain_5 ~
## Income (Inc3) -0.098 0.046 -2.159 0.031 -0.188 -0.009
## LadderDif.1 ~
## Age (Age1) 0.041 0.038 1.100 0.271 -0.032 0.115
## LadderDif.2 ~
## Age (Age1) 0.041 0.038 1.100 0.271 -0.032 0.115
## LadderDif.3 ~
## Age (Age1) 0.041 0.038 1.100 0.271 -0.032 0.115
## LadderDif.4 ~
## Age (Age1) 0.041 0.038 1.100 0.271 -0.032 0.115
## LadderDif.5 ~
## Age (Age1) 0.041 0.038 1.100 0.271 -0.032 0.115
## posEmo.1 ~
## Age (Age2) 0.026 0.043 0.610 0.542 -0.058 0.110
## posEmo.2 ~
## Age (Age2) 0.026 0.043 0.610 0.542 -0.058 0.110
## posEmo.3 ~
## Age (Age2) 0.026 0.043 0.610 0.542 -0.058 0.110
## posEmo.4 ~
## Age (Age2) 0.026 0.043 0.610 0.542 -0.058 0.110
## posEmo.5 ~
## Age (Age2) 0.026 0.043 0.610 0.542 -0.058 0.110
## Pain_1 ~
## Age (Age3) 0.105 0.044 2.366 0.018 0.018 0.192
## Pain_2 ~
## Age (Age3) 0.105 0.044 2.366 0.018 0.018 0.192
## Pain_3 ~
## Age (Age3) 0.105 0.044 2.366 0.018 0.018 0.192
## Pain_4 ~
## Age (Age3) 0.105 0.044 2.366 0.018 0.018 0.192
## Pain_5 ~
## Age (Age3) 0.105 0.044 2.366 0.018 0.018 0.192
## wy2 ~
## wy1 0.571 0.054 10.662 0.000 0.466 0.675
## wm1 (b1) 0.028 0.025 1.101 0.271 -0.022 0.077
## wy3 ~
## wx1 (cp1) 0.054 0.030 1.769 0.077 -0.006 0.114
## wy2 0.499 0.056 8.947 0.000 0.390 0.608
## wm2 (b1) 0.028 0.025 1.101 0.271 -0.022 0.077
## wy1 0.189 0.054 3.516 0.000 0.084 0.295
## wy4 ~
## wx2 (cp1) 0.054 0.030 1.769 0.077 -0.006 0.114
## wy3 0.340 0.079 4.304 0.000 0.185 0.495
## wm3 (b1) 0.028 0.025 1.101 0.271 -0.022 0.077
## wy2 0.422 0.070 6.020 0.000 0.285 0.559
## wy5 ~
## wx3 (cp1) 0.054 0.030 1.769 0.077 -0.006 0.114
## wy4 0.376 0.061 6.208 0.000 0.258 0.495
## wm4 (b1) 0.028 0.025 1.101 0.271 -0.022 0.077
## wy3 0.523 0.069 7.635 0.000 0.389 0.657
## wx2 ~
## wx1 0.219 0.061 3.574 0.000 0.099 0.339
## wm1 (b2) -0.040 0.031 -1.304 0.192 -0.100 0.020
## wx3 ~
## wx2 0.434 0.066 6.540 0.000 0.304 0.564
## wy1 (cp2) 0.003 0.037 0.093 0.926 -0.068 0.075
## wm2 (b2) -0.040 0.031 -1.304 0.192 -0.100 0.020
## wx1 0.167 0.064 2.622 0.009 0.042 0.293
## wx4 ~
## wx3 0.288 0.078 3.709 0.000 0.136 0.441
## wy2 (cp2) 0.003 0.037 0.093 0.926 -0.068 0.075
## wm3 (b2) -0.040 0.031 -1.304 0.192 -0.100 0.020
## wx2 0.235 0.079 2.970 0.003 0.080 0.389
## wx5 ~
## wx4 0.240 0.064 3.729 0.000 0.114 0.366
## wy3 (cp2) 0.003 0.037 0.093 0.926 -0.068 0.075
## wm4 (b2) -0.040 0.031 -1.304 0.192 -0.100 0.020
## wx3 0.414 0.069 5.970 0.000 0.278 0.550
## wm2 ~
## wx1 (a1) -0.038 0.025 -1.520 0.128 -0.088 0.011
## wy1 (a2) -0.000 0.025 -0.019 0.985 -0.049 0.048
## wm1 0.529 0.053 10.067 0.000 0.426 0.632
## wm3 ~
## wx2 (a1) -0.038 0.025 -1.520 0.128 -0.088 0.011
## wy2 (a2) -0.000 0.025 -0.019 0.985 -0.049 0.048
## wm2 0.592 0.054 11.057 0.000 0.487 0.697
## wm1 0.237 0.052 4.525 0.000 0.134 0.340
## wm4 ~
## wx3 (a1) -0.038 0.025 -1.520 0.128 -0.088 0.011
## wy3 (a2) -0.000 0.025 -0.019 0.985 -0.049 0.048
## wm3 0.457 0.070 6.550 0.000 0.321 0.594
## wm2 0.327 0.068 4.827 0.000 0.194 0.460
## wm5 ~
## wx4 (a1) -0.038 0.025 -1.520 0.128 -0.088 0.011
## wy4 (a2) -0.000 0.025 -0.019 0.985 -0.049 0.048
## wm4 0.342 0.069 4.922 0.000 0.206 0.478
## wm3 0.486 0.067 7.296 0.000 0.355 0.616
## Std.lv Std.all
##
## -0.128 -0.063
##
## -0.128 -0.065
##
## -0.128 -0.065
##
## -0.128 -0.064
##
## -0.128 -0.066
##
## -0.208 -0.103
##
## -0.208 -0.104
##
## -0.208 -0.105
##
## -0.208 -0.108
##
## -0.208 -0.106
##
## -0.035 -0.017
##
## -0.035 -0.017
##
## -0.035 -0.019
##
## -0.035 -0.018
##
## -0.035 -0.017
##
## -0.077 -0.077
##
## -0.077 -0.079
##
## -0.077 -0.079
##
## -0.077 -0.078
##
## -0.077 -0.080
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## -0.086 -0.081
##
## -0.086 -0.081
##
## -0.086 -0.091
##
## -0.086 -0.086
##
## -0.086 -0.085
##
## -0.108 -0.107
##
## -0.108 -0.110
##
## -0.108 -0.110
##
## -0.108 -0.109
##
## -0.108 -0.112
##
## 0.041 0.041
##
## 0.041 0.041
##
## 0.041 0.042
##
## 0.041 0.043
##
## 0.041 0.042
##
## -0.098 -0.092
##
## -0.098 -0.093
##
## -0.098 -0.104
##
## -0.098 -0.098
##
## -0.098 -0.097
##
## 0.041 0.041
##
## 0.041 0.042
##
## 0.041 0.042
##
## 0.041 0.042
##
## 0.041 0.043
##
## 0.026 0.026
##
## 0.026 0.026
##
## 0.026 0.026
##
## 0.026 0.027
##
## 0.026 0.027
##
## 0.105 0.098
##
## 0.105 0.099
##
## 0.105 0.111
##
## 0.105 0.104
##
## 0.105 0.103
##
## 0.575 0.575
## 0.026 0.026
##
## 0.058 0.058
## 0.561 0.561
## 0.030 0.030
## 0.215 0.215
##
## 0.053 0.053
## 0.318 0.318
## 0.027 0.027
## 0.444 0.444
##
## 0.052 0.052
## 0.373 0.373
## 0.026 0.026
## 0.485 0.485
##
## 0.225 0.225
## -0.041 -0.041
##
## 0.434 0.434
## 0.004 0.004
## -0.041 -0.041
## 0.172 0.172
##
## 0.286 0.286
## 0.004 0.004
## -0.040 -0.040
## 0.232 0.232
##
## 0.247 0.247
## 0.003 0.003
## -0.040 -0.040
## 0.423 0.423
##
## -0.038 -0.038
## -0.000 -0.000
## 0.531 0.531
##
## -0.038 -0.038
## -0.000 -0.000
## 0.599 0.599
## 0.241 0.241
##
## -0.039 -0.039
## -0.000 -0.000
## 0.471 0.471
## 0.341 0.341
##
## -0.039 -0.039
## -0.000 -0.000
## 0.336 0.336
## 0.491 0.491
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.002 0.049 0.050 0.960 -0.094 0.099
## wm1 -0.013 0.047 -0.267 0.789 -0.104 0.079
## wy1 ~~
## wm1 -0.064 0.050 -1.294 0.196 -0.161 0.033
## .wx2 ~~
## .wy2 0.034 0.051 0.667 0.505 -0.066 0.135
## .wx3 ~~
## .wy3 -0.020 0.039 -0.515 0.607 -0.095 0.056
## .wx4 ~~
## .wy4 -0.002 0.045 -0.053 0.957 -0.091 0.086
## .wx5 ~~
## .wy5 -0.041 0.037 -1.129 0.259 -0.113 0.031
## .wx2 ~~
## .wm2 -0.035 0.050 -0.707 0.479 -0.134 0.063
## .wx3 ~~
## .wm3 -0.009 0.038 -0.240 0.810 -0.083 0.065
## .wx4 ~~
## .wm4 0.014 0.040 0.343 0.732 -0.065 0.092
## .wx5 ~~
## .wm5 -0.058 0.037 -1.570 0.116 -0.130 0.014
## .wy2 ~~
## .wm2 0.075 0.045 1.650 0.099 -0.014 0.164
## .wy3 ~~
## .wm3 -0.077 0.030 -2.557 0.011 -0.136 -0.018
## .wy4 ~~
## .wm4 -0.034 0.032 -1.039 0.299 -0.097 0.030
## .wy5 ~~
## .wm5 -0.061 0.030 -2.049 0.041 -0.119 -0.003
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.002 0.002
## -0.013 -0.013
##
## -0.061 -0.061
##
## 0.042 0.042
##
## -0.036 -0.036
##
## -0.004 -0.004
##
## -0.085 -0.085
##
## -0.045 -0.045
##
## -0.017 -0.017
##
## 0.025 0.025
##
## -0.121 -0.121
##
## 0.104 0.104
##
## -0.184 -0.184
##
## -0.077 -0.077
##
## -0.156 -0.156
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.068 0.061 1.100 0.271 -0.053 0.188
## .LadderDif.2 0.126 0.073 1.734 0.083 -0.016 0.268
## .LadderDif.3 0.107 0.077 1.391 0.164 -0.044 0.259
## .LadderDif.4 0.148 0.081 1.823 0.068 -0.011 0.307
## .LadderDif.5 0.126 0.080 1.565 0.118 -0.032 0.284
## .Pain_1 2.134 0.068 31.371 0.000 2.001 2.267
## .Pain_2 2.094 0.077 27.205 0.000 1.943 2.245
## .Pain_3 2.007 0.075 26.679 0.000 1.860 2.155
## .Pain_4 1.992 0.081 24.538 0.000 1.833 2.151
## .Pain_5 1.931 0.083 23.295 0.000 1.768 2.093
## .posEmo.1 0.108 0.065 1.668 0.095 -0.019 0.236
## .posEmo.2 0.105 0.074 1.417 0.156 -0.040 0.250
## .posEmo.3 0.108 0.076 1.426 0.154 -0.041 0.257
## .posEmo.4 0.089 0.078 1.151 0.250 -0.063 0.241
## .posEmo.5 0.083 0.080 1.040 0.298 -0.074 0.241
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.068 0.067
## 0.126 0.128
## 0.107 0.109
## 0.148 0.149
## 0.126 0.130
## 2.134 2.002
## 2.094 1.980
## 2.007 2.126
## 1.992 1.980
## 1.931 1.902
## 0.108 0.108
## 0.105 0.105
## 0.108 0.110
## 0.089 0.093
## 0.083 0.085
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.988 0.066 14.944 0.000 0.859 1.118
## wy1 1.107 0.074 15.008 0.000 0.963 1.252
## wm1 0.994 0.066 15.001 0.000 0.864 1.124
## .wx2 0.888 0.079 11.179 0.000 0.732 1.044
## .wy2 0.731 0.065 11.239 0.000 0.604 0.859
## .wm2 0.706 0.063 11.226 0.000 0.583 0.829
## .wx3 0.699 0.070 10.038 0.000 0.563 0.836
## .wy3 0.427 0.043 10.041 0.000 0.343 0.510
## .wm3 0.409 0.041 10.037 0.000 0.329 0.489
## .wx4 0.762 0.079 9.650 0.000 0.607 0.916
## .wy4 0.493 0.051 9.666 0.000 0.393 0.593
## .wm4 0.384 0.040 9.635 0.000 0.306 0.463
## .wx5 0.603 0.064 9.439 0.000 0.478 0.728
## .wy5 0.396 0.042 9.449 0.000 0.314 0.478
## .wm5 0.380 0.040 9.428 0.000 0.301 0.459
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .Pain_1 0.000 0.000 0.000
## .Pain_2 0.000 0.000 0.000
## .Pain_3 0.000 0.000 0.000
## .Pain_4 0.000 0.000 0.000
## .Pain_5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.947 0.947
## 0.671 0.671
## 0.716 0.716
## 0.744 0.744
## 0.495 0.495
## 0.424 0.424
## 0.796 0.796
## 0.501 0.501
## 0.422 0.422
## 0.670 0.670
## 0.395 0.395
## 0.402 0.402
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t predicts less positive emotions at time t+1, b = -.07, p = .006
(b1) Positive emotions at time t do not predict pain at time t+1
PEmoFatigueCLPM_2AR_controls <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*Fatigue_1 + 1*Fatigue_2 + 1*Fatigue_3 + 1*Fatigue_4 + 1*Fatigue_5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*Fatigue_1
wy2 =~ 1*Fatigue_2
wy3 =~ 1*Fatigue_3
wy4 =~ 1*Fatigue_4
wy5 =~ 1*Fatigue_5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Regression of observed variables on controls (constrained).
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Gen1*GenderBinary
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Gen2*GenderBinary
Fatigue_1 + Fatigue_2 + Fatigue_3 + Fatigue_4 + Fatigue_5 ~ Gen3*GenderBinary
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Edu1*Edu
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Edu2*Edu
Fatigue_1 + Fatigue_2 + Fatigue_3 + Fatigue_4 + Fatigue_5 ~ Edu3*Edu
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Inc1*Income
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Inc2*Income
Fatigue_1 + Fatigue_2 + Fatigue_3 + Fatigue_4 + Fatigue_5 ~ Inc3*Income
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Age1*Age
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Age2*Age
Fatigue_1 + Fatigue_2 + Fatigue_3 + Fatigue_4 + Fatigue_5 ~ Age3*Age
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoFatigueCLPM_w2AR_controls.fit <- lavaan(PEmoFatigueCLPM_2AR_controls, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoFatigueCLPM_w2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 55 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 433 482
## Number of missing patterns 8
##
## Model Test User Model:
##
## Test statistic 201.370
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2213.080
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.956
## Tucker-Lewis Index (TLI) 0.934
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4904.784
## Loglikelihood unrestricted model (H1) -4804.099
##
## Akaike (AIC) 9977.567
## Bayesian (BIC) 10319.509
## Sample-size adjusted Bayesian (BIC) 10052.940
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.043
## 90 Percent confidence interval - lower 0.034
## 90 Percent confidence interval - upper 0.053
## P-value RMSEA <= 0.05 0.872
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.044
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## Fatigue_1 1.000 1.000 1.000
## Fatigue_2 1.000 1.000 1.000
## Fatigue_3 1.000 1.000 1.000
## Fatigue_4 1.000 1.000 1.000
## Fatigue_5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## Fatigue_1 1.000 1.000 1.000
## wy2 =~
## Fatigue_2 1.000 1.000 1.000
## wy3 =~
## Fatigue_3 1.000 1.000 1.000
## wy4 =~
## Fatigue_4 1.000 1.000 1.000
## wy5 =~
## Fatigue_5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.956 0.956
##
## 0.972 0.957
##
## 0.951 0.956
##
## 0.948 0.955
##
## 0.919 0.953
##
## 0.901 0.969
##
## 0.920 0.970
##
## 0.830 0.963
##
## 0.983 0.973
##
## 0.913 0.969
##
## 0.977 0.980
##
## 0.952 0.979
##
## 0.960 0.979
##
## 0.972 0.980
##
## 0.969 0.980
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) 0.308 0.077 3.994 0.000 0.157 0.458
## LadderDif.2 ~
## GndrBnr (Gen1) 0.308 0.077 3.994 0.000 0.157 0.458
## LadderDif.3 ~
## GndrBnr (Gen1) 0.308 0.077 3.994 0.000 0.157 0.458
## LadderDif.4 ~
## GndrBnr (Gen1) 0.308 0.077 3.994 0.000 0.157 0.458
## LadderDif.5 ~
## GndrBnr (Gen1) 0.308 0.077 3.994 0.000 0.157 0.458
## posEmo.1 ~
## GndrBnr (Gen2) -0.158 0.082 -1.929 0.054 -0.318 0.003
## posEmo.2 ~
## GndrBnr (Gen2) -0.158 0.082 -1.929 0.054 -0.318 0.003
## posEmo.3 ~
## GndrBnr (Gen2) -0.158 0.082 -1.929 0.054 -0.318 0.003
## posEmo.4 ~
## GndrBnr (Gen2) -0.158 0.082 -1.929 0.054 -0.318 0.003
## posEmo.5 ~
## GndrBnr (Gen2) -0.158 0.082 -1.929 0.054 -0.318 0.003
## Fatigue_1 ~
## GndrBnr (Gen3) 0.299 0.074 4.008 0.000 0.153 0.445
## Fatigue_2 ~
## GndrBnr (Gen3) 0.299 0.074 4.008 0.000 0.153 0.445
## Fatigue_3 ~
## GndrBnr (Gen3) 0.299 0.074 4.008 0.000 0.153 0.445
## Fatigue_4 ~
## GndrBnr (Gen3) 0.299 0.074 4.008 0.000 0.153 0.445
## Fatigue_5 ~
## GndrBnr (Gen3) 0.299 0.074 4.008 0.000 0.153 0.445
## LadderDif.1 ~
## Edu (Edu1) 0.019 0.041 0.456 0.649 -0.061 0.099
## LadderDif.2 ~
## Edu (Edu1) 0.019 0.041 0.456 0.649 -0.061 0.099
## LadderDif.3 ~
## Edu (Edu1) 0.019 0.041 0.456 0.649 -0.061 0.099
## LadderDif.4 ~
## Edu (Edu1) 0.019 0.041 0.456 0.649 -0.061 0.099
## LadderDif.5 ~
## Edu (Edu1) 0.019 0.041 0.456 0.649 -0.061 0.099
## posEmo.1 ~
## Edu (Edu2) -0.065 0.044 -1.488 0.137 -0.151 0.021
## posEmo.2 ~
## Edu (Edu2) -0.065 0.044 -1.488 0.137 -0.151 0.021
## posEmo.3 ~
## Edu (Edu2) -0.065 0.044 -1.488 0.137 -0.151 0.021
## posEmo.4 ~
## Edu (Edu2) -0.065 0.044 -1.488 0.137 -0.151 0.021
## posEmo.5 ~
## Edu (Edu2) -0.065 0.044 -1.488 0.137 -0.151 0.021
## Fatigue_1 ~
## Edu (Edu3) -0.077 0.040 -1.947 0.052 -0.155 0.001
## Fatigue_2 ~
## Edu (Edu3) -0.077 0.040 -1.947 0.052 -0.155 0.001
## Fatigue_3 ~
## Edu (Edu3) -0.077 0.040 -1.947 0.052 -0.155 0.001
## Fatigue_4 ~
## Edu (Edu3) -0.077 0.040 -1.947 0.052 -0.155 0.001
## Fatigue_5 ~
## Edu (Edu3) -0.077 0.040 -1.947 0.052 -0.155 0.001
## LadderDif.1 ~
## Income (Inc1) -0.253 0.041 -6.200 0.000 -0.333 -0.173
## LadderDif.2 ~
## Income (Inc1) -0.253 0.041 -6.200 0.000 -0.333 -0.173
## LadderDif.3 ~
## Income (Inc1) -0.253 0.041 -6.200 0.000 -0.333 -0.173
## LadderDif.4 ~
## Income (Inc1) -0.253 0.041 -6.200 0.000 -0.333 -0.173
## LadderDif.5 ~
## Income (Inc1) -0.253 0.041 -6.200 0.000 -0.333 -0.173
## posEmo.1 ~
## Income (Inc2) 0.157 0.043 3.608 0.000 0.072 0.242
## posEmo.2 ~
## Income (Inc2) 0.157 0.043 3.608 0.000 0.072 0.242
## posEmo.3 ~
## Income (Inc2) 0.157 0.043 3.608 0.000 0.072 0.242
## posEmo.4 ~
## Income (Inc2) 0.157 0.043 3.608 0.000 0.072 0.242
## posEmo.5 ~
## Income (Inc2) 0.157 0.043 3.608 0.000 0.072 0.242
## Fatigue_1 ~
## Income (Inc3) -0.128 0.040 -3.236 0.001 -0.206 -0.051
## Fatigue_2 ~
## Income (Inc3) -0.128 0.040 -3.236 0.001 -0.206 -0.051
## Fatigue_3 ~
## Income (Inc3) -0.128 0.040 -3.236 0.001 -0.206 -0.051
## Fatigue_4 ~
## Income (Inc3) -0.128 0.040 -3.236 0.001 -0.206 -0.051
## Fatigue_5 ~
## Income (Inc3) -0.128 0.040 -3.236 0.001 -0.206 -0.051
## LadderDif.1 ~
## Age (Age1) -0.012 0.039 -0.304 0.761 -0.088 0.064
## LadderDif.2 ~
## Age (Age1) -0.012 0.039 -0.304 0.761 -0.088 0.064
## LadderDif.3 ~
## Age (Age1) -0.012 0.039 -0.304 0.761 -0.088 0.064
## LadderDif.4 ~
## Age (Age1) -0.012 0.039 -0.304 0.761 -0.088 0.064
## LadderDif.5 ~
## Age (Age1) -0.012 0.039 -0.304 0.761 -0.088 0.064
## posEmo.1 ~
## Age (Age2) 0.095 0.041 2.301 0.021 0.014 0.176
## posEmo.2 ~
## Age (Age2) 0.095 0.041 2.301 0.021 0.014 0.176
## posEmo.3 ~
## Age (Age2) 0.095 0.041 2.301 0.021 0.014 0.176
## posEmo.4 ~
## Age (Age2) 0.095 0.041 2.301 0.021 0.014 0.176
## posEmo.5 ~
## Age (Age2) 0.095 0.041 2.301 0.021 0.014 0.176
## Fatigue_1 ~
## Age (Age3) 0.041 0.038 1.095 0.274 -0.033 0.115
## Fatigue_2 ~
## Age (Age3) 0.041 0.038 1.095 0.274 -0.033 0.115
## Fatigue_3 ~
## Age (Age3) 0.041 0.038 1.095 0.274 -0.033 0.115
## Fatigue_4 ~
## Age (Age3) 0.041 0.038 1.095 0.274 -0.033 0.115
## Fatigue_5 ~
## Age (Age3) 0.041 0.038 1.095 0.274 -0.033 0.115
## wy2 ~
## wy1 0.622 0.045 13.717 0.000 0.533 0.711
## wm1 (b1) -0.019 0.023 -0.823 0.410 -0.063 0.026
## wy3 ~
## wx1 (cp1) 0.032 0.028 1.140 0.254 -0.023 0.088
## wy2 0.274 0.056 4.909 0.000 0.165 0.384
## wm2 (b1) -0.019 0.023 -0.823 0.410 -0.063 0.026
## wy1 0.388 0.057 6.795 0.000 0.276 0.500
## wy4 ~
## wx2 (cp1) 0.032 0.028 1.140 0.254 -0.023 0.088
## wy3 0.595 0.072 8.303 0.000 0.454 0.735
## wm3 (b1) -0.019 0.023 -0.823 0.410 -0.063 0.026
## wy2 0.255 0.065 3.900 0.000 0.127 0.383
## wy5 ~
## wx3 (cp1) 0.032 0.028 1.140 0.254 -0.023 0.088
## wy4 0.444 0.062 7.175 0.000 0.323 0.566
## wm4 (b1) -0.019 0.023 -0.823 0.410 -0.063 0.026
## wy3 0.261 0.074 3.524 0.000 0.116 0.406
## wx2 ~
## wx1 0.514 0.051 10.159 0.000 0.415 0.613
## wm1 (b2) -0.034 0.025 -1.338 0.181 -0.084 0.016
## wx3 ~
## wx2 0.287 0.061 4.670 0.000 0.167 0.408
## wy1 (cp2) -0.004 0.034 -0.105 0.916 -0.070 0.063
## wm2 (b2) -0.034 0.025 -1.338 0.181 -0.084 0.016
## wx1 0.402 0.059 6.759 0.000 0.285 0.519
## wx4 ~
## wx3 0.252 0.064 3.924 0.000 0.126 0.377
## wy2 (cp2) -0.004 0.034 -0.105 0.916 -0.070 0.063
## wm3 (b2) -0.034 0.025 -1.338 0.181 -0.084 0.016
## wx2 0.377 0.067 5.593 0.000 0.245 0.509
## wx5 ~
## wx4 0.288 0.057 5.020 0.000 0.175 0.400
## wy3 (cp2) -0.004 0.034 -0.105 0.916 -0.070 0.063
## wm4 (b2) -0.034 0.025 -1.338 0.181 -0.084 0.016
## wx3 0.437 0.056 7.785 0.000 0.327 0.547
## wm2 ~
## wx1 (a1) -0.065 0.024 -2.748 0.006 -0.112 -0.019
## wy1 (a2) -0.051 0.024 -2.092 0.036 -0.099 -0.003
## wm1 0.562 0.043 13.033 0.000 0.477 0.646
## wm3 ~
## wx2 (a1) -0.065 0.024 -2.748 0.006 -0.112 -0.019
## wy2 (a2) -0.051 0.024 -2.092 0.036 -0.099 -0.003
## wm2 0.545 0.059 9.268 0.000 0.430 0.660
## wm1 0.184 0.059 3.135 0.002 0.069 0.299
## wm4 ~
## wx3 (a1) -0.065 0.024 -2.748 0.006 -0.112 -0.019
## wy3 (a2) -0.051 0.024 -2.092 0.036 -0.099 -0.003
## wm3 0.528 0.063 8.383 0.000 0.404 0.651
## wm2 0.256 0.064 3.989 0.000 0.130 0.382
## wm5 ~
## wx4 (a1) -0.065 0.024 -2.748 0.006 -0.112 -0.019
## wy4 (a2) -0.051 0.024 -2.092 0.036 -0.099 -0.003
## wm4 0.376 0.059 6.362 0.000 0.260 0.492
## wm3 0.450 0.059 7.581 0.000 0.334 0.566
## Std.lv Std.all
##
## 0.308 0.154
##
## 0.308 0.151
##
## 0.308 0.154
##
## 0.308 0.155
##
## 0.308 0.159
##
## -0.158 -0.079
##
## -0.158 -0.081
##
## -0.158 -0.080
##
## -0.158 -0.079
##
## -0.158 -0.080
##
## 0.299 0.160
##
## 0.299 0.157
##
## 0.299 0.173
##
## 0.299 0.148
##
## 0.299 0.158
##
## 0.019 0.019
##
## 0.019 0.018
##
## 0.019 0.019
##
## 0.019 0.019
##
## 0.019 0.019
##
## -0.065 -0.065
##
## -0.065 -0.067
##
## -0.065 -0.066
##
## -0.065 -0.066
##
## -0.065 -0.066
##
## -0.077 -0.083
##
## -0.077 -0.081
##
## -0.077 -0.089
##
## -0.077 -0.076
##
## -0.077 -0.082
##
## -0.253 -0.253
##
## -0.253 -0.249
##
## -0.253 -0.254
##
## -0.253 -0.255
##
## -0.253 -0.262
##
## 0.157 0.157
##
## 0.157 0.161
##
## 0.157 0.160
##
## 0.157 0.158
##
## 0.157 0.159
##
## -0.128 -0.138
##
## -0.128 -0.135
##
## -0.128 -0.149
##
## -0.128 -0.127
##
## -0.128 -0.136
##
## -0.012 -0.012
##
## -0.012 -0.012
##
## -0.012 -0.012
##
## -0.012 -0.012
##
## -0.012 -0.012
##
## 0.095 0.095
##
## 0.095 0.097
##
## 0.095 0.097
##
## 0.095 0.095
##
## 0.095 0.096
##
## 0.041 0.044
##
## 0.041 0.043
##
## 0.041 0.048
##
## 0.041 0.041
##
## 0.041 0.044
##
## 0.610 0.610
## -0.020 -0.020
##
## 0.037 0.037
## 0.304 0.304
## -0.021 -0.021
## 0.421 0.421
##
## 0.032 0.032
## 0.502 0.502
## -0.018 -0.018
## 0.239 0.239
##
## 0.034 0.034
## 0.478 0.478
## -0.020 -0.020
## 0.237 0.237
##
## 0.506 0.506
## -0.034 -0.034
##
## 0.293 0.293
## -0.003 -0.003
## -0.034 -0.034
## 0.404 0.404
##
## 0.252 0.252
## -0.003 -0.003
## -0.035 -0.035
## 0.386 0.386
##
## 0.297 0.297
## -0.003 -0.003
## -0.036 -0.036
## 0.452 0.452
##
## -0.066 -0.066
## -0.048 -0.048
## 0.576 0.576
##
## -0.066 -0.066
## -0.049 -0.049
## 0.540 0.540
## 0.187 0.187
##
## -0.064 -0.064
## -0.044 -0.044
## 0.521 0.521
## 0.250 0.250
##
## -0.064 -0.064
## -0.052 -0.052
## 0.378 0.378
## 0.446 0.446
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.240 0.043 5.556 0.000 0.155 0.324
## wm1 -0.182 0.046 -3.967 0.000 -0.272 -0.092
## wy1 ~~
## wm1 -0.106 0.043 -2.487 0.013 -0.190 -0.022
## .wx2 ~~
## .wy2 -0.005 0.035 -0.156 0.876 -0.074 0.063
## .wx3 ~~
## .wy3 0.025 0.030 0.828 0.408 -0.034 0.084
## .wx4 ~~
## .wy4 -0.046 0.039 -1.200 0.230 -0.122 0.029
## .wx5 ~~
## .wy5 -0.003 0.033 -0.098 0.922 -0.068 0.061
## .wx2 ~~
## .wm2 -0.041 0.037 -1.118 0.264 -0.113 0.031
## .wx3 ~~
## .wm3 0.010 0.034 0.291 0.771 -0.057 0.076
## .wx4 ~~
## .wm4 0.001 0.035 0.041 0.967 -0.067 0.070
## .wx5 ~~
## .wm5 -0.014 0.029 -0.470 0.638 -0.071 0.043
## .wy2 ~~
## .wm2 -0.034 0.032 -1.078 0.281 -0.097 0.028
## .wy3 ~~
## .wm3 -0.028 0.028 -0.998 0.318 -0.084 0.027
## .wy4 ~~
## .wm4 -0.008 0.032 -0.250 0.803 -0.071 0.055
## .wy5 ~~
## .wm5 -0.045 0.029 -1.553 0.120 -0.101 0.012
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.278 0.278
## -0.195 -0.195
##
## -0.121 -0.121
##
## -0.009 -0.009
##
## 0.054 0.054
##
## -0.082 -0.082
##
## -0.007 -0.007
##
## -0.065 -0.065
##
## 0.019 0.019
##
## 0.003 0.003
##
## -0.032 -0.032
##
## -0.062 -0.062
##
## -0.066 -0.066
##
## -0.017 -0.017
##
## -0.109 -0.109
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.149 0.061 -2.454 0.014 -0.268 -0.030
## .LadderDif.2 -0.156 0.067 -2.344 0.019 -0.286 -0.026
## .LadderDif.3 -0.189 0.069 -2.743 0.006 -0.324 -0.054
## .LadderDif.4 -0.169 0.072 -2.351 0.019 -0.311 -0.028
## .LadderDif.5 -0.193 0.071 -2.698 0.007 -0.332 -0.053
## .Fatigue_1 2.510 0.058 43.439 0.000 2.397 2.624
## .Fatigue_2 2.516 0.063 40.092 0.000 2.393 2.639
## .Fatigue_3 2.370 0.062 38.514 0.000 2.249 2.491
## .Fatigue_4 2.413 0.072 33.466 0.000 2.272 2.555
## .Fatigue_5 2.433 0.070 34.561 0.000 2.295 2.571
## .posEmo.1 0.081 0.063 1.280 0.200 -0.043 0.204
## .posEmo.2 0.077 0.066 1.152 0.249 -0.054 0.207
## .posEmo.3 0.106 0.070 1.510 0.131 -0.032 0.244
## .posEmo.4 0.099 0.073 1.359 0.174 -0.044 0.242
## .posEmo.5 0.112 0.074 1.514 0.130 -0.033 0.256
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.149 -0.149
## -0.156 -0.154
## -0.189 -0.190
## -0.169 -0.171
## -0.193 -0.200
## 2.510 2.697
## 2.516 2.652
## 2.370 2.750
## 2.413 2.390
## 2.433 2.584
## 0.081 0.081
## 0.077 0.079
## 0.106 0.108
## 0.099 0.100
## 0.112 0.113
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.914 0.062 14.663 0.000 0.792 1.036
## wy1 0.813 0.055 14.686 0.000 0.704 0.921
## wm1 0.954 0.065 14.703 0.000 0.827 1.081
## .wx2 0.695 0.057 12.261 0.000 0.584 0.806
## .wy2 0.529 0.043 12.273 0.000 0.445 0.613
## .wm2 0.578 0.047 12.284 0.000 0.486 0.670
## .wx3 0.561 0.051 10.990 0.000 0.461 0.661
## .wy3 0.382 0.035 10.996 0.000 0.314 0.450
## .wm3 0.481 0.044 10.964 0.000 0.395 0.566
## .wx4 0.612 0.059 10.457 0.000 0.497 0.726
## .wy4 0.525 0.050 10.476 0.000 0.426 0.623
## .wm4 0.435 0.042 10.480 0.000 0.354 0.517
## .wx5 0.487 0.047 10.287 0.000 0.394 0.580
## .wy5 0.463 0.045 10.272 0.000 0.375 0.552
## .wm5 0.364 0.035 10.288 0.000 0.294 0.433
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .Fatigue_1 0.000 0.000 0.000
## .Fatigue_2 0.000 0.000 0.000
## .Fatigue_3 0.000 0.000 0.000
## .Fatigue_4 0.000 0.000 0.000
## .Fatigue_5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.736 0.736
## 0.625 0.625
## 0.638 0.638
## 0.620 0.620
## 0.554 0.554
## 0.522 0.522
## 0.681 0.681
## 0.543 0.543
## 0.460 0.460
## 0.577 0.577
## 0.556 0.556
## 0.388 0.388
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Perceived status difference at time t does not predict positive emotions at time t+1
Positive emotions at time t do not predict fatigue at time t+1
# Same model as above code, but fit with d_black dataset this time
PEmoFatigueCLPM_b2AR_controls.fit <- lavaan(PEmoFatigueCLPM_2AR_controls, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoFatigueCLPM_b2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 46 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 451 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 215.574
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1513.744
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.922
## Tucker-Lewis Index (TLI) 0.885
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4761.369
## Loglikelihood unrestricted model (H1) -4653.582
##
## Akaike (AIC) 9690.739
## Bayesian (BIC) 10036.102
## Sample-size adjusted Bayesian (BIC) 9769.517
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.046
## 90 Percent confidence interval - lower 0.037
## 90 Percent confidence interval - upper 0.055
## P-value RMSEA <= 0.05 0.774
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.054
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## Fatigue_1 1.000 1.000 1.000
## Fatigue_2 1.000 1.000 1.000
## Fatigue_3 1.000 1.000 1.000
## Fatigue_4 1.000 1.000 1.000
## Fatigue_5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## Fatigue_1 1.000 1.000 1.000
## wy2 =~
## Fatigue_2 1.000 1.000 1.000
## wy3 =~
## Fatigue_3 1.000 1.000 1.000
## wy4 =~
## Fatigue_4 1.000 1.000 1.000
## wy5 =~
## Fatigue_5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.994 0.985
##
## 0.968 0.985
##
## 0.974 0.985
##
## 0.980 0.985
##
## 0.953 0.984
##
## 1.030 0.993
##
## 0.989 0.992
##
## 1.008 0.992
##
## 1.018 0.993
##
## 0.976 0.992
##
## 0.997 0.993
##
## 0.993 0.993
##
## 0.974 0.993
##
## 0.945 0.993
##
## 0.955 0.993
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) -0.124 0.076 -1.638 0.101 -0.273 0.024
## LadderDif.2 ~
## GndrBnr (Gen1) -0.124 0.076 -1.638 0.101 -0.273 0.024
## LadderDif.3 ~
## GndrBnr (Gen1) -0.124 0.076 -1.638 0.101 -0.273 0.024
## LadderDif.4 ~
## GndrBnr (Gen1) -0.124 0.076 -1.638 0.101 -0.273 0.024
## LadderDif.5 ~
## GndrBnr (Gen1) -0.124 0.076 -1.638 0.101 -0.273 0.024
## posEmo.1 ~
## GndrBnr (Gen2) -0.218 0.086 -2.541 0.011 -0.387 -0.050
## posEmo.2 ~
## GndrBnr (Gen2) -0.218 0.086 -2.541 0.011 -0.387 -0.050
## posEmo.3 ~
## GndrBnr (Gen2) -0.218 0.086 -2.541 0.011 -0.387 -0.050
## posEmo.4 ~
## GndrBnr (Gen2) -0.218 0.086 -2.541 0.011 -0.387 -0.050
## posEmo.5 ~
## GndrBnr (Gen2) -0.218 0.086 -2.541 0.011 -0.387 -0.050
## Fatigue_1 ~
## GndrBnr (Gen3) 0.131 0.087 1.503 0.133 -0.040 0.302
## Fatigue_2 ~
## GndrBnr (Gen3) 0.131 0.087 1.503 0.133 -0.040 0.302
## Fatigue_3 ~
## GndrBnr (Gen3) 0.131 0.087 1.503 0.133 -0.040 0.302
## Fatigue_4 ~
## GndrBnr (Gen3) 0.131 0.087 1.503 0.133 -0.040 0.302
## Fatigue_5 ~
## GndrBnr (Gen3) 0.131 0.087 1.503 0.133 -0.040 0.302
## LadderDif.1 ~
## Edu (Edu1) -0.074 0.040 -1.872 0.061 -0.152 0.004
## LadderDif.2 ~
## Edu (Edu1) -0.074 0.040 -1.872 0.061 -0.152 0.004
## LadderDif.3 ~
## Edu (Edu1) -0.074 0.040 -1.872 0.061 -0.152 0.004
## LadderDif.4 ~
## Edu (Edu1) -0.074 0.040 -1.872 0.061 -0.152 0.004
## LadderDif.5 ~
## Edu (Edu1) -0.074 0.040 -1.872 0.061 -0.152 0.004
## posEmo.1 ~
## Edu (Edu2) -0.006 0.045 -0.128 0.898 -0.094 0.082
## posEmo.2 ~
## Edu (Edu2) -0.006 0.045 -0.128 0.898 -0.094 0.082
## posEmo.3 ~
## Edu (Edu2) -0.006 0.045 -0.128 0.898 -0.094 0.082
## posEmo.4 ~
## Edu (Edu2) -0.006 0.045 -0.128 0.898 -0.094 0.082
## posEmo.5 ~
## Edu (Edu2) -0.006 0.045 -0.128 0.898 -0.094 0.082
## Fatigue_1 ~
## Edu (Edu3) -0.026 0.046 -0.576 0.565 -0.116 0.063
## Fatigue_2 ~
## Edu (Edu3) -0.026 0.046 -0.576 0.565 -0.116 0.063
## Fatigue_3 ~
## Edu (Edu3) -0.026 0.046 -0.576 0.565 -0.116 0.063
## Fatigue_4 ~
## Edu (Edu3) -0.026 0.046 -0.576 0.565 -0.116 0.063
## Fatigue_5 ~
## Edu (Edu3) -0.026 0.046 -0.576 0.565 -0.116 0.063
## LadderDif.1 ~
## Income (Inc1) -0.109 0.039 -2.816 0.005 -0.185 -0.033
## LadderDif.2 ~
## Income (Inc1) -0.109 0.039 -2.816 0.005 -0.185 -0.033
## LadderDif.3 ~
## Income (Inc1) -0.109 0.039 -2.816 0.005 -0.185 -0.033
## LadderDif.4 ~
## Income (Inc1) -0.109 0.039 -2.816 0.005 -0.185 -0.033
## LadderDif.5 ~
## Income (Inc1) -0.109 0.039 -2.816 0.005 -0.185 -0.033
## posEmo.1 ~
## Income (Inc2) 0.041 0.044 0.931 0.352 -0.045 0.127
## posEmo.2 ~
## Income (Inc2) 0.041 0.044 0.931 0.352 -0.045 0.127
## posEmo.3 ~
## Income (Inc2) 0.041 0.044 0.931 0.352 -0.045 0.127
## posEmo.4 ~
## Income (Inc2) 0.041 0.044 0.931 0.352 -0.045 0.127
## posEmo.5 ~
## Income (Inc2) 0.041 0.044 0.931 0.352 -0.045 0.127
## Fatigue_1 ~
## Income (Inc3) -0.106 0.044 -2.410 0.016 -0.193 -0.020
## Fatigue_2 ~
## Income (Inc3) -0.106 0.044 -2.410 0.016 -0.193 -0.020
## Fatigue_3 ~
## Income (Inc3) -0.106 0.044 -2.410 0.016 -0.193 -0.020
## Fatigue_4 ~
## Income (Inc3) -0.106 0.044 -2.410 0.016 -0.193 -0.020
## Fatigue_5 ~
## Income (Inc3) -0.106 0.044 -2.410 0.016 -0.193 -0.020
## LadderDif.1 ~
## Age (Age1) 0.042 0.038 1.118 0.264 -0.032 0.116
## LadderDif.2 ~
## Age (Age1) 0.042 0.038 1.118 0.264 -0.032 0.116
## LadderDif.3 ~
## Age (Age1) 0.042 0.038 1.118 0.264 -0.032 0.116
## LadderDif.4 ~
## Age (Age1) 0.042 0.038 1.118 0.264 -0.032 0.116
## LadderDif.5 ~
## Age (Age1) 0.042 0.038 1.118 0.264 -0.032 0.116
## posEmo.1 ~
## Age (Age2) 0.030 0.043 0.707 0.480 -0.054 0.114
## posEmo.2 ~
## Age (Age2) 0.030 0.043 0.707 0.480 -0.054 0.114
## posEmo.3 ~
## Age (Age2) 0.030 0.043 0.707 0.480 -0.054 0.114
## posEmo.4 ~
## Age (Age2) 0.030 0.043 0.707 0.480 -0.054 0.114
## posEmo.5 ~
## Age (Age2) 0.030 0.043 0.707 0.480 -0.054 0.114
## Fatigue_1 ~
## Age (Age3) -0.005 0.043 -0.124 0.901 -0.090 0.079
## Fatigue_2 ~
## Age (Age3) -0.005 0.043 -0.124 0.901 -0.090 0.079
## Fatigue_3 ~
## Age (Age3) -0.005 0.043 -0.124 0.901 -0.090 0.079
## Fatigue_4 ~
## Age (Age3) -0.005 0.043 -0.124 0.901 -0.090 0.079
## Fatigue_5 ~
## Age (Age3) -0.005 0.043 -0.124 0.901 -0.090 0.079
## wy2 ~
## wy1 0.533 0.052 10.187 0.000 0.431 0.636
## wm1 (b1) -0.033 0.028 -1.191 0.234 -0.088 0.021
## wy3 ~
## wx1 (cp1) 0.065 0.035 1.863 0.062 -0.003 0.132
## wy2 0.430 0.072 5.958 0.000 0.289 0.572
## wm2 (b1) -0.033 0.028 -1.191 0.234 -0.088 0.021
## wy1 0.298 0.071 4.227 0.000 0.160 0.437
## wy4 ~
## wx2 (cp1) 0.065 0.035 1.863 0.062 -0.003 0.132
## wy3 0.357 0.065 5.477 0.000 0.229 0.485
## wm3 (b1) -0.033 0.028 -1.191 0.234 -0.088 0.021
## wy2 0.448 0.068 6.576 0.000 0.315 0.582
## wy5 ~
## wx3 (cp1) 0.065 0.035 1.863 0.062 -0.003 0.132
## wy4 0.414 0.069 5.990 0.000 0.278 0.549
## wm4 (b1) -0.033 0.028 -1.191 0.234 -0.088 0.021
## wy3 0.257 0.071 3.628 0.000 0.118 0.396
## wx2 ~
## wx1 0.218 0.061 3.550 0.000 0.098 0.338
## wm1 (b2) -0.042 0.031 -1.343 0.179 -0.103 0.019
## wx3 ~
## wx2 0.445 0.067 6.658 0.000 0.314 0.576
## wy1 (cp2) -0.023 0.036 -0.624 0.532 -0.094 0.049
## wm2 (b2) -0.042 0.031 -1.343 0.179 -0.103 0.019
## wx1 0.163 0.064 2.537 0.011 0.037 0.288
## wx4 ~
## wx3 0.292 0.077 3.779 0.000 0.140 0.443
## wy2 (cp2) -0.023 0.036 -0.624 0.532 -0.094 0.049
## wm3 (b2) -0.042 0.031 -1.343 0.179 -0.103 0.019
## wx2 0.236 0.079 2.996 0.003 0.082 0.390
## wx5 ~
## wx4 0.243 0.064 3.779 0.000 0.117 0.370
## wy3 (cp2) -0.023 0.036 -0.624 0.532 -0.094 0.049
## wm4 (b2) -0.042 0.031 -1.343 0.179 -0.103 0.019
## wx3 0.418 0.069 6.025 0.000 0.282 0.554
## wm2 ~
## wx1 (a1) -0.044 0.025 -1.734 0.083 -0.093 0.006
## wy1 (a2) -0.065 0.024 -2.670 0.008 -0.112 -0.017
## wm1 0.525 0.052 10.020 0.000 0.422 0.628
## wm3 ~
## wx2 (a1) -0.044 0.025 -1.734 0.083 -0.093 0.006
## wy2 (a2) -0.065 0.024 -2.670 0.008 -0.112 -0.017
## wm2 0.589 0.054 10.854 0.000 0.483 0.695
## wm1 0.224 0.053 4.195 0.000 0.119 0.328
## wm4 ~
## wx3 (a1) -0.044 0.025 -1.734 0.083 -0.093 0.006
## wy3 (a2) -0.065 0.024 -2.670 0.008 -0.112 -0.017
## wm3 0.438 0.070 6.278 0.000 0.301 0.575
## wm2 0.331 0.067 4.928 0.000 0.200 0.463
## wm5 ~
## wx4 (a1) -0.044 0.025 -1.734 0.083 -0.093 0.006
## wy4 (a2) -0.065 0.024 -2.670 0.008 -0.112 -0.017
## wm4 0.328 0.070 4.668 0.000 0.191 0.466
## wm3 0.469 0.068 6.947 0.000 0.337 0.602
## Std.lv Std.all
##
## -0.124 -0.062
##
## -0.124 -0.063
##
## -0.124 -0.063
##
## -0.124 -0.062
##
## -0.124 -0.064
##
## -0.218 -0.109
##
## -0.218 -0.109
##
## -0.218 -0.111
##
## -0.218 -0.114
##
## -0.218 -0.113
##
## 0.131 0.063
##
## 0.131 0.066
##
## 0.131 0.065
##
## 0.131 0.064
##
## 0.131 0.067
##
## -0.074 -0.074
##
## -0.074 -0.076
##
## -0.074 -0.075
##
## -0.074 -0.075
##
## -0.074 -0.077
##
## -0.006 -0.006
##
## -0.006 -0.006
##
## -0.006 -0.006
##
## -0.006 -0.006
##
## -0.006 -0.006
##
## -0.026 -0.025
##
## -0.026 -0.026
##
## -0.026 -0.026
##
## -0.026 -0.026
##
## -0.026 -0.027
##
## -0.109 -0.108
##
## -0.109 -0.111
##
## -0.109 -0.110
##
## -0.109 -0.109
##
## -0.109 -0.113
##
## 0.041 0.041
##
## 0.041 0.041
##
## 0.041 0.042
##
## 0.041 0.043
##
## 0.041 0.042
##
## -0.106 -0.102
##
## -0.106 -0.107
##
## -0.106 -0.105
##
## -0.106 -0.104
##
## -0.106 -0.108
##
## 0.042 0.042
##
## 0.042 0.043
##
## 0.042 0.042
##
## 0.042 0.042
##
## 0.042 0.043
##
## 0.030 0.030
##
## 0.030 0.030
##
## 0.030 0.031
##
## 0.030 0.032
##
## 0.030 0.031
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## -0.005 -0.005
##
## 0.555 0.555
## -0.033 -0.033
##
## 0.064 0.064
## 0.422 0.422
## -0.033 -0.033
## 0.305 0.305
##
## 0.061 0.061
## 0.354 0.354
## -0.032 -0.032
## 0.436 0.436
##
## 0.064 0.064
## 0.431 0.431
## -0.032 -0.032
## 0.265 0.265
##
## 0.224 0.224
## -0.043 -0.043
##
## 0.442 0.442
## -0.024 -0.024
## -0.043 -0.043
## 0.166 0.166
##
## 0.290 0.290
## -0.023 -0.023
## -0.042 -0.042
## 0.233 0.233
##
## 0.250 0.250
## -0.024 -0.024
## -0.042 -0.042
## 0.427 0.427
##
## -0.044 -0.044
## -0.067 -0.067
## 0.527 0.527
##
## -0.044 -0.044
## -0.066 -0.066
## 0.601 0.601
## 0.229 0.229
##
## -0.045 -0.045
## -0.069 -0.069
## 0.451 0.451
## 0.348 0.348
##
## -0.045 -0.045
## -0.069 -0.069
## 0.325 0.325
## 0.478 0.478
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 0.016 0.049 0.337 0.736 -0.079 0.112
## wm1 -0.012 0.047 -0.263 0.792 -0.104 0.080
## wy1 ~~
## wm1 -0.078 0.049 -1.594 0.111 -0.173 0.018
## .wx2 ~~
## .wy2 -0.021 0.050 -0.421 0.674 -0.119 0.077
## .wx3 ~~
## .wy3 0.023 0.046 0.512 0.609 -0.066 0.113
## .wx4 ~~
## .wy4 0.029 0.046 0.632 0.527 -0.061 0.120
## .wx5 ~~
## .wy5 -0.046 0.044 -1.047 0.295 -0.132 0.040
## .wx2 ~~
## .wm2 -0.025 0.050 -0.507 0.612 -0.123 0.073
## .wx3 ~~
## .wm3 -0.009 0.038 -0.239 0.811 -0.083 0.065
## .wx4 ~~
## .wm4 0.018 0.040 0.447 0.655 -0.060 0.096
## .wx5 ~~
## .wm5 -0.058 0.037 -1.567 0.117 -0.130 0.014
## .wy2 ~~
## .wm2 0.071 0.044 1.639 0.101 -0.014 0.157
## .wy3 ~~
## .wm3 -0.026 0.035 -0.747 0.455 -0.094 0.042
## .wy4 ~~
## .wm4 -0.029 0.033 -0.876 0.381 -0.093 0.035
## .wy5 ~~
## .wm5 0.032 0.035 0.911 0.363 -0.037 0.101
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## 0.016 0.016
## -0.012 -0.012
##
## -0.075 -0.075
##
## -0.027 -0.027
##
## 0.036 0.036
##
## 0.047 0.047
##
## -0.079 -0.079
##
## -0.032 -0.032
##
## -0.017 -0.017
##
## 0.033 0.033
##
## -0.121 -0.121
##
## 0.104 0.104
##
## -0.053 -0.053
##
## -0.065 -0.065
##
## 0.069 0.069
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.066 0.062 1.066 0.287 -0.055 0.186
## .LadderDif.2 0.124 0.073 1.699 0.089 -0.019 0.266
## .LadderDif.3 0.102 0.078 1.315 0.189 -0.050 0.254
## .LadderDif.4 0.144 0.081 1.776 0.076 -0.015 0.303
## .LadderDif.5 0.120 0.081 1.488 0.137 -0.038 0.278
## .Fatigue_1 2.421 0.067 36.332 0.000 2.291 2.552
## .Fatigue_2 2.313 0.074 31.207 0.000 2.168 2.459
## .Fatigue_3 2.294 0.079 29.062 0.000 2.139 2.449
## .Fatigue_4 2.221 0.082 27.150 0.000 2.061 2.382
## .Fatigue_5 2.214 0.083 26.567 0.000 2.050 2.377
## .posEmo.1 0.114 0.065 1.753 0.080 -0.013 0.241
## .posEmo.2 0.105 0.074 1.412 0.158 -0.041 0.250
## .posEmo.3 0.109 0.076 1.449 0.147 -0.039 0.258
## .posEmo.4 0.089 0.077 1.153 0.249 -0.062 0.240
## .posEmo.5 0.082 0.079 1.039 0.299 -0.073 0.238
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.066 0.065
## 0.124 0.126
## 0.102 0.103
## 0.144 0.145
## 0.120 0.124
## 2.421 2.334
## 2.313 2.321
## 2.294 2.259
## 2.221 2.166
## 2.214 2.249
## 0.114 0.114
## 0.105 0.105
## 0.109 0.112
## 0.089 0.093
## 0.082 0.086
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.988 0.066 14.945 0.000 0.859 1.118
## wy1 1.061 0.071 14.951 0.000 0.922 1.200
## wm1 0.995 0.066 14.994 0.000 0.865 1.125
## .wx2 0.888 0.079 11.179 0.000 0.732 1.044
## .wy2 0.673 0.060 11.179 0.000 0.555 0.791
## .wm2 0.700 0.062 11.225 0.000 0.577 0.822
## .wx3 0.701 0.070 10.021 0.000 0.564 0.838
## .wy3 0.585 0.058 10.027 0.000 0.471 0.700
## .wm3 0.405 0.040 10.035 0.000 0.326 0.484
## .wx4 0.759 0.079 9.646 0.000 0.605 0.914
## .wy4 0.511 0.053 9.640 0.000 0.407 0.614
## .wm4 0.379 0.039 9.661 0.000 0.302 0.456
## .wx5 0.601 0.064 9.427 0.000 0.476 0.726
## .wy5 0.560 0.059 9.418 0.000 0.443 0.676
## .wm5 0.380 0.040 9.439 0.000 0.301 0.459
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .Fatigue_1 0.000 0.000 0.000
## .Fatigue_2 0.000 0.000 0.000
## .Fatigue_3 0.000 0.000 0.000
## .Fatigue_4 0.000 0.000 0.000
## .Fatigue_5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.948 0.948
## 0.688 0.688
## 0.709 0.709
## 0.739 0.739
## 0.576 0.576
## 0.427 0.427
## 0.790 0.790
## 0.493 0.493
## 0.424 0.424
## 0.662 0.662
## 0.587 0.587
## 0.416 0.416
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000(a1) Perceived status difference at time t predicts less positive emotions at time t+1, b = -.07, p = .003
(b1) Positive emotions at time t do not predict physical ability at time t+1
PEmoPhysCLPM_2AR_controls <- '
# Create between components (random intercepts)
RIx =~ 1*LadderDif.1 + 1*LadderDif.2 + 1*LadderDif.3 + 1*LadderDif.4 + 1*LadderDif.5
RIy =~ 1*Phys_1 + 1*Phys_2 + 1*Phys_3 + 1*Phys_4 + 1*Phys_5
RIm =~ 1*posEmo.1 + 1*posEmo.2 + 1*posEmo.3 + 1*posEmo.4 + 1*posEmo.5
# Create within-person centered variables
wx1 =~ 1*LadderDif.1
wx2 =~ 1*LadderDif.2
wx3 =~ 1*LadderDif.3
wx4 =~ 1*LadderDif.4
wx5 =~ 1*LadderDif.5
wy1 =~ 1*Phys_1
wy2 =~ 1*Phys_2
wy3 =~ 1*Phys_3
wy4 =~ 1*Phys_4
wy5 =~ 1*Phys_5
wm1 =~ 1*posEmo.1
wm2 =~ 1*posEmo.2
wm3 =~ 1*posEmo.3
wm4 =~ 1*posEmo.4
wm5 =~ 1*posEmo.5
# Regression of observed variables on controls (constrained).
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Gen1*GenderBinary
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Gen2*GenderBinary
Phys_1 + Phys_2 + Phys_3 + Phys_4 + Phys_5 ~ Gen3*GenderBinary
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Edu1*Edu
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Edu2*Edu
Phys_1 + Phys_2 + Phys_3 + Phys_4 + Phys_5 ~ Edu3*Edu
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Inc1*Income
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Inc2*Income
Phys_1 + Phys_2 + Phys_3 + Phys_4 + Phys_5 ~ Inc3*Income
LadderDif.1 + LadderDif.2 + LadderDif.3 + LadderDif.4 + LadderDif.5 ~ Age1*Age
posEmo.1 + posEmo.2 + posEmo.3 + posEmo.4 + posEmo.5 ~ Age2*Age
Phys_1 + Phys_2 + Phys_3 + Phys_4 + Phys_5 ~ Age3*Age
# Estimate the lagged effects between the within-person centered variables.
wy2 ~ wy1 + b1*wm1
wy3 ~ cp1*wx1 + wy2 + b1*wm2 + wy1
wy4 ~ cp1*wx2 + wy3 + b1*wm3 + wy2
wy5 ~ cp1*wx3 + wy4 + b1*wm4 + wy3
wx2 ~ wx1 + b2*wm1
wx3 ~ wx2 + cp2*wy1 + b2*wm2 + wx1
wx4 ~ wx3 + cp2*wy2 + b2*wm3 + wx2
wx5 ~ wx4 + cp2*wy3 + b2*wm4 + wx3
wm2 ~ a1*wx1 + a2*wy1 + wm1
wm3 ~ a1*wx2 + a2*wy2 + wm2 + wm1
wm4 ~ a1*wx3 + a2*wy3 + wm3 + wm2
wm5 ~ a1*wx4 + a2*wy4 + wm4 + wm3
# Estimate the covariance between the within-person centered variables at the first wave.
wx1 ~~ wy1 # Covariance
wx1 ~~ wm1 # Covariance
wm1 ~~ wy1 # Covariance
# Estimate the covariances between the residuals of the within-person centered variables (the innovations).
wx2 ~~ wy2
wx3 ~~ wy3
wx4 ~~ wy4
wx5 ~~ wy5
wx2 ~~ wm2
wx3 ~~ wm3
wx4 ~~ wm4
wx5 ~~ wm5
wm2 ~~ wy2
wm3 ~~ wy3
wm4 ~~ wy4
wm5 ~~ wy5
# Estimate the variance and covariance of the random intercepts.
RIx ~~ 0*RIx
RIy ~~ 0*RIy
RIm ~~ 0*RIm
RIx ~~ 0*RIy
RIx ~~ 0*RIm
RIy ~~ 0*RIm
# Estimate the (residual) variance of the within-person centered variables.
wx1 ~~ wx1 # Variances
wy1 ~~ wy1
wm1 ~~ wm1
wx2 ~~ wx2 # Residual variances
wy2 ~~ wy2
wm2 ~~ wm2
wx3 ~~ wx3
wy3 ~~ wy3
wm3 ~~ wm3
wx4 ~~ wx4
wy4 ~~ wy4
wm4 ~~ wm4
wx5 ~~ wx5
wy5 ~~ wy5
wm5 ~~ wm5
'
PEmoPhysCLPM_w2AR_controls.fit <- lavaan(PEmoPhysCLPM_2AR_controls, data = d_white, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoPhysCLPM_w2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 60 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 433 482
## Number of missing patterns 8
##
## Model Test User Model:
##
## Test statistic 190.053
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2200.085
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.961
## Tucker-Lewis Index (TLI) 0.942
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5154.658
## Loglikelihood unrestricted model (H1) -5059.632
##
## Akaike (AIC) 10477.316
## Bayesian (BIC) 10819.258
## Sample-size adjusted Bayesian (BIC) 10552.689
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.041
## 90 Percent confidence interval - lower 0.031
## 90 Percent confidence interval - upper 0.050
## P-value RMSEA <= 0.05 0.946
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.044
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## Phys_1 1.000 1.000 1.000
## Phys_2 1.000 1.000 1.000
## Phys_3 1.000 1.000 1.000
## Phys_4 1.000 1.000 1.000
## Phys_5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## Phys_1 1.000 1.000 1.000
## wy2 =~
## Phys_2 1.000 1.000 1.000
## wy3 =~
## Phys_3 1.000 1.000 1.000
## wy4 =~
## Phys_4 1.000 1.000 1.000
## wy5 =~
## Phys_5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.956 0.957
##
## 0.972 0.958
##
## 0.952 0.956
##
## 0.952 0.956
##
## 0.921 0.953
##
## 1.041 0.949
##
## 1.065 0.951
##
## 1.058 0.950
##
## 1.135 0.957
##
## 1.092 0.953
##
## 0.977 0.980
##
## 0.951 0.979
##
## 0.958 0.979
##
## 0.973 0.980
##
## 0.974 0.980
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) 0.302 0.077 3.915 0.000 0.151 0.453
## LadderDif.2 ~
## GndrBnr (Gen1) 0.302 0.077 3.915 0.000 0.151 0.453
## LadderDif.3 ~
## GndrBnr (Gen1) 0.302 0.077 3.915 0.000 0.151 0.453
## LadderDif.4 ~
## GndrBnr (Gen1) 0.302 0.077 3.915 0.000 0.151 0.453
## LadderDif.5 ~
## GndrBnr (Gen1) 0.302 0.077 3.915 0.000 0.151 0.453
## posEmo.1 ~
## GndrBnr (Gen2) -0.152 0.082 -1.862 0.063 -0.312 0.008
## posEmo.2 ~
## GndrBnr (Gen2) -0.152 0.082 -1.862 0.063 -0.312 0.008
## posEmo.3 ~
## GndrBnr (Gen2) -0.152 0.082 -1.862 0.063 -0.312 0.008
## posEmo.4 ~
## GndrBnr (Gen2) -0.152 0.082 -1.862 0.063 -0.312 0.008
## posEmo.5 ~
## GndrBnr (Gen2) -0.152 0.082 -1.862 0.063 -0.312 0.008
## Phys_1 ~
## GndrBnr (Gen3) -0.231 0.089 -2.594 0.009 -0.406 -0.056
## Phys_2 ~
## GndrBnr (Gen3) -0.231 0.089 -2.594 0.009 -0.406 -0.056
## Phys_3 ~
## GndrBnr (Gen3) -0.231 0.089 -2.594 0.009 -0.406 -0.056
## Phys_4 ~
## GndrBnr (Gen3) -0.231 0.089 -2.594 0.009 -0.406 -0.056
## Phys_5 ~
## GndrBnr (Gen3) -0.231 0.089 -2.594 0.009 -0.406 -0.056
## LadderDif.1 ~
## Edu (Edu1) 0.016 0.041 0.397 0.691 -0.064 0.097
## LadderDif.2 ~
## Edu (Edu1) 0.016 0.041 0.397 0.691 -0.064 0.097
## LadderDif.3 ~
## Edu (Edu1) 0.016 0.041 0.397 0.691 -0.064 0.097
## LadderDif.4 ~
## Edu (Edu1) 0.016 0.041 0.397 0.691 -0.064 0.097
## LadderDif.5 ~
## Edu (Edu1) 0.016 0.041 0.397 0.691 -0.064 0.097
## posEmo.1 ~
## Edu (Edu2) -0.063 0.044 -1.430 0.153 -0.148 0.023
## posEmo.2 ~
## Edu (Edu2) -0.063 0.044 -1.430 0.153 -0.148 0.023
## posEmo.3 ~
## Edu (Edu2) -0.063 0.044 -1.430 0.153 -0.148 0.023
## posEmo.4 ~
## Edu (Edu2) -0.063 0.044 -1.430 0.153 -0.148 0.023
## posEmo.5 ~
## Edu (Edu2) -0.063 0.044 -1.430 0.153 -0.148 0.023
## Phys_1 ~
## Edu (Edu3) 0.178 0.047 3.779 0.000 0.086 0.271
## Phys_2 ~
## Edu (Edu3) 0.178 0.047 3.779 0.000 0.086 0.271
## Phys_3 ~
## Edu (Edu3) 0.178 0.047 3.779 0.000 0.086 0.271
## Phys_4 ~
## Edu (Edu3) 0.178 0.047 3.779 0.000 0.086 0.271
## Phys_5 ~
## Edu (Edu3) 0.178 0.047 3.779 0.000 0.086 0.271
## LadderDif.1 ~
## Income (Inc1) -0.250 0.041 -6.131 0.000 -0.330 -0.170
## LadderDif.2 ~
## Income (Inc1) -0.250 0.041 -6.131 0.000 -0.330 -0.170
## LadderDif.3 ~
## Income (Inc1) -0.250 0.041 -6.131 0.000 -0.330 -0.170
## LadderDif.4 ~
## Income (Inc1) -0.250 0.041 -6.131 0.000 -0.330 -0.170
## LadderDif.5 ~
## Income (Inc1) -0.250 0.041 -6.131 0.000 -0.330 -0.170
## posEmo.1 ~
## Income (Inc2) 0.159 0.043 3.648 0.000 0.073 0.244
## posEmo.2 ~
## Income (Inc2) 0.159 0.043 3.648 0.000 0.073 0.244
## posEmo.3 ~
## Income (Inc2) 0.159 0.043 3.648 0.000 0.073 0.244
## posEmo.4 ~
## Income (Inc2) 0.159 0.043 3.648 0.000 0.073 0.244
## posEmo.5 ~
## Income (Inc2) 0.159 0.043 3.648 0.000 0.073 0.244
## Phys_1 ~
## Income (Inc3) 0.201 0.047 4.258 0.000 0.109 0.294
## Phys_2 ~
## Income (Inc3) 0.201 0.047 4.258 0.000 0.109 0.294
## Phys_3 ~
## Income (Inc3) 0.201 0.047 4.258 0.000 0.109 0.294
## Phys_4 ~
## Income (Inc3) 0.201 0.047 4.258 0.000 0.109 0.294
## Phys_5 ~
## Income (Inc3) 0.201 0.047 4.258 0.000 0.109 0.294
## LadderDif.1 ~
## Age (Age1) -0.017 0.039 -0.430 0.667 -0.093 0.059
## LadderDif.2 ~
## Age (Age1) -0.017 0.039 -0.430 0.667 -0.093 0.059
## LadderDif.3 ~
## Age (Age1) -0.017 0.039 -0.430 0.667 -0.093 0.059
## LadderDif.4 ~
## Age (Age1) -0.017 0.039 -0.430 0.667 -0.093 0.059
## LadderDif.5 ~
## Age (Age1) -0.017 0.039 -0.430 0.667 -0.093 0.059
## posEmo.1 ~
## Age (Age2) 0.095 0.041 2.305 0.021 0.014 0.176
## posEmo.2 ~
## Age (Age2) 0.095 0.041 2.305 0.021 0.014 0.176
## posEmo.3 ~
## Age (Age2) 0.095 0.041 2.305 0.021 0.014 0.176
## posEmo.4 ~
## Age (Age2) 0.095 0.041 2.305 0.021 0.014 0.176
## posEmo.5 ~
## Age (Age2) 0.095 0.041 2.305 0.021 0.014 0.176
## Phys_1 ~
## Age (Age3) 0.031 0.045 0.704 0.482 -0.056 0.119
## Phys_2 ~
## Age (Age3) 0.031 0.045 0.704 0.482 -0.056 0.119
## Phys_3 ~
## Age (Age3) 0.031 0.045 0.704 0.482 -0.056 0.119
## Phys_4 ~
## Age (Age3) 0.031 0.045 0.704 0.482 -0.056 0.119
## Phys_5 ~
## Age (Age3) 0.031 0.045 0.704 0.482 -0.056 0.119
## wy2 ~
## wy1 0.606 0.049 12.363 0.000 0.510 0.703
## wm1 (b1) -0.025 0.027 -0.918 0.358 -0.077 0.028
## wy3 ~
## wx1 (cp1) -0.013 0.034 -0.396 0.692 -0.080 0.053
## wy2 0.301 0.057 5.230 0.000 0.188 0.413
## wm2 (b1) -0.025 0.027 -0.918 0.358 -0.077 0.028
## wy1 0.491 0.058 8.482 0.000 0.378 0.605
## wy4 ~
## wx2 (cp1) -0.013 0.034 -0.396 0.692 -0.080 0.053
## wy3 0.466 0.068 6.897 0.000 0.334 0.599
## wm3 (b1) -0.025 0.027 -0.918 0.358 -0.077 0.028
## wy2 0.349 0.067 5.191 0.000 0.217 0.481
## wy5 ~
## wx3 (cp1) -0.013 0.034 -0.396 0.692 -0.080 0.053
## wy4 0.305 0.065 4.702 0.000 0.178 0.432
## wm4 (b1) -0.025 0.027 -0.918 0.358 -0.077 0.028
## wy3 0.393 0.072 5.486 0.000 0.252 0.533
## wx2 ~
## wx1 0.515 0.051 10.134 0.000 0.416 0.615
## wm1 (b2) -0.032 0.025 -1.260 0.208 -0.082 0.018
## wx3 ~
## wx2 0.282 0.062 4.580 0.000 0.162 0.403
## wy1 (cp2) -0.019 0.029 -0.671 0.502 -0.076 0.037
## wm2 (b2) -0.032 0.025 -1.260 0.208 -0.082 0.018
## wx1 0.401 0.059 6.764 0.000 0.285 0.518
## wx4 ~
## wx3 0.256 0.063 4.036 0.000 0.132 0.381
## wy2 (cp2) -0.019 0.029 -0.671 0.502 -0.076 0.037
## wm3 (b2) -0.032 0.025 -1.260 0.208 -0.082 0.018
## wx2 0.381 0.067 5.655 0.000 0.249 0.512
## wx5 ~
## wx4 0.289 0.057 5.065 0.000 0.177 0.402
## wy3 (cp2) -0.019 0.029 -0.671 0.502 -0.076 0.037
## wm4 (b2) -0.032 0.025 -1.260 0.208 -0.082 0.018
## wx3 0.435 0.056 7.808 0.000 0.326 0.544
## wm2 ~
## wx1 (a1) -0.071 0.024 -3.001 0.003 -0.118 -0.025
## wy1 (a2) 0.012 0.021 0.555 0.579 -0.030 0.053
## wm1 0.563 0.043 13.004 0.000 0.478 0.648
## wm3 ~
## wx2 (a1) -0.071 0.024 -3.001 0.003 -0.118 -0.025
## wy2 (a2) 0.012 0.021 0.555 0.579 -0.030 0.053
## wm2 0.548 0.058 9.419 0.000 0.434 0.662
## wm1 0.190 0.057 3.298 0.001 0.077 0.302
## wm4 ~
## wx3 (a1) -0.071 0.024 -3.001 0.003 -0.118 -0.025
## wy3 (a2) 0.012 0.021 0.555 0.579 -0.030 0.053
## wm3 0.534 0.063 8.448 0.000 0.410 0.658
## wm2 0.256 0.064 3.976 0.000 0.130 0.382
## wm5 ~
## wx4 (a1) -0.071 0.024 -3.001 0.003 -0.118 -0.025
## wy4 (a2) 0.012 0.021 0.555 0.579 -0.030 0.053
## wm4 0.377 0.060 6.338 0.000 0.261 0.494
## wm3 0.461 0.060 7.715 0.000 0.344 0.578
## Std.lv Std.all
##
## 0.302 0.151
##
## 0.302 0.149
##
## 0.302 0.152
##
## 0.302 0.152
##
## 0.302 0.156
##
## -0.152 -0.076
##
## -0.152 -0.078
##
## -0.152 -0.078
##
## -0.152 -0.077
##
## -0.152 -0.077
##
## -0.231 -0.105
##
## -0.231 -0.103
##
## -0.231 -0.104
##
## -0.231 -0.097
##
## -0.231 -0.101
##
## 0.016 0.016
##
## 0.016 0.016
##
## 0.016 0.016
##
## 0.016 0.016
##
## 0.016 0.017
##
## -0.063 -0.063
##
## -0.063 -0.064
##
## -0.063 -0.064
##
## -0.063 -0.063
##
## -0.063 -0.063
##
## 0.178 0.162
##
## 0.178 0.159
##
## 0.178 0.160
##
## 0.178 0.150
##
## 0.178 0.156
##
## -0.250 -0.251
##
## -0.250 -0.247
##
## -0.250 -0.251
##
## -0.250 -0.252
##
## -0.250 -0.259
##
## 0.159 0.159
##
## 0.159 0.163
##
## 0.159 0.162
##
## 0.159 0.160
##
## 0.159 0.160
##
## 0.201 0.184
##
## 0.201 0.180
##
## 0.201 0.181
##
## 0.201 0.170
##
## 0.201 0.176
##
## -0.017 -0.017
##
## -0.017 -0.016
##
## -0.017 -0.017
##
## -0.017 -0.017
##
## -0.017 -0.017
##
## 0.095 0.095
##
## 0.095 0.097
##
## 0.095 0.097
##
## 0.095 0.095
##
## 0.095 0.095
##
## 0.031 0.029
##
## 0.031 0.028
##
## 0.031 0.028
##
## 0.031 0.026
##
## 0.031 0.027
##
## 0.592 0.592
## -0.023 -0.023
##
## -0.012 -0.012
## 0.303 0.303
## -0.022 -0.022
## 0.483 0.483
##
## -0.012 -0.012
## 0.435 0.435
## -0.021 -0.021
## 0.328 0.328
##
## -0.012 -0.012
## 0.317 0.317
## -0.022 -0.022
## 0.380 0.380
##
## 0.507 0.507
## -0.032 -0.032
##
## 0.288 0.288
## -0.021 -0.021
## -0.032 -0.032
## 0.403 0.403
##
## 0.256 0.256
## -0.022 -0.022
## -0.032 -0.032
## 0.389 0.389
##
## 0.299 0.299
## -0.022 -0.022
## -0.034 -0.034
## 0.450 0.450
##
## -0.072 -0.072
## 0.013 0.013
## 0.578 0.578
##
## -0.072 -0.072
## 0.013 0.013
## 0.544 0.544
## 0.193 0.193
##
## -0.070 -0.070
## 0.013 0.013
## 0.526 0.526
## 0.250 0.250
##
## -0.070 -0.070
## 0.014 0.014
## 0.377 0.377
## 0.454 0.454
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.175 0.049 -3.596 0.000 -0.270 -0.080
## wm1 -0.182 0.046 -3.971 0.000 -0.272 -0.092
## wy1 ~~
## wm1 0.100 0.049 2.043 0.041 0.004 0.197
## .wx2 ~~
## .wy2 0.011 0.042 0.275 0.783 -0.070 0.093
## .wx3 ~~
## .wy3 -0.007 0.036 -0.189 0.850 -0.078 0.065
## .wx4 ~~
## .wy4 0.065 0.044 1.458 0.145 -0.022 0.152
## .wx5 ~~
## .wy5 0.045 0.041 1.098 0.272 -0.035 0.125
## .wx2 ~~
## .wm2 -0.042 0.037 -1.141 0.254 -0.114 0.030
## .wx3 ~~
## .wm3 0.006 0.034 0.191 0.849 -0.060 0.073
## .wx4 ~~
## .wm4 0.000 0.035 0.013 0.990 -0.068 0.069
## .wx5 ~~
## .wm5 -0.009 0.029 -0.318 0.750 -0.066 0.048
## .wy2 ~~
## .wm2 0.015 0.038 0.392 0.695 -0.059 0.089
## .wy3 ~~
## .wm3 0.002 0.034 0.068 0.946 -0.064 0.069
## .wy4 ~~
## .wm4 0.022 0.037 0.594 0.552 -0.051 0.095
## .wy5 ~~
## .wm5 0.035 0.036 0.974 0.330 -0.035 0.105
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.176 -0.176
## -0.195 -0.195
##
## 0.099 0.099
##
## 0.016 0.016
##
## -0.012 -0.012
##
## 0.100 0.100
##
## 0.076 0.076
##
## -0.066 -0.066
##
## 0.012 0.012
##
## 0.001 0.001
##
## -0.022 -0.022
##
## 0.023 0.023
##
## 0.004 0.004
##
## 0.040 0.040
##
## 0.068 0.068
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 -0.146 0.061 -2.404 0.016 -0.265 -0.027
## .LadderDif.2 -0.153 0.067 -2.298 0.022 -0.283 -0.022
## .LadderDif.3 -0.185 0.069 -2.678 0.007 -0.320 -0.050
## .LadderDif.4 -0.166 0.072 -2.302 0.021 -0.308 -0.025
## .LadderDif.5 -0.188 0.071 -2.635 0.008 -0.328 -0.048
## .Phys_1 4.074 0.068 60.089 0.000 3.941 4.207
## .Phys_2 4.013 0.074 54.419 0.000 3.868 4.157
## .Phys_3 3.956 0.076 52.140 0.000 3.807 4.104
## .Phys_4 3.918 0.084 46.831 0.000 3.754 4.082
## .Phys_5 3.961 0.084 47.149 0.000 3.796 4.126
## .posEmo.1 0.078 0.063 1.235 0.217 -0.046 0.201
## .posEmo.2 0.075 0.066 1.126 0.260 -0.055 0.205
## .posEmo.3 0.104 0.070 1.482 0.138 -0.034 0.241
## .posEmo.4 0.097 0.073 1.322 0.186 -0.047 0.240
## .posEmo.5 0.109 0.074 1.473 0.141 -0.036 0.254
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## -0.146 -0.146
## -0.153 -0.151
## -0.185 -0.186
## -0.166 -0.167
## -0.188 -0.195
## 4.074 3.714
## 4.013 3.582
## 3.956 3.555
## 3.918 3.301
## 3.961 3.458
## 0.078 0.078
## 0.075 0.077
## 0.104 0.106
## 0.097 0.097
## 0.109 0.110
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.914 0.062 14.666 0.000 0.792 1.036
## wy1 1.083 0.074 14.699 0.000 0.939 1.228
## wm1 0.954 0.065 14.705 0.000 0.827 1.081
## .wx2 0.695 0.057 12.260 0.000 0.584 0.807
## .wy2 0.739 0.060 12.254 0.000 0.621 0.858
## .wm2 0.582 0.047 12.286 0.000 0.489 0.674
## .wx3 0.564 0.051 10.957 0.000 0.463 0.664
## .wy3 0.560 0.051 10.997 0.000 0.460 0.660
## .wm3 0.476 0.043 10.986 0.000 0.391 0.561
## .wx4 0.610 0.058 10.465 0.000 0.495 0.724
## .wy4 0.691 0.066 10.468 0.000 0.562 0.820
## .wm4 0.438 0.042 10.481 0.000 0.356 0.520
## .wx5 0.485 0.047 10.283 0.000 0.393 0.578
## .wy5 0.719 0.070 10.275 0.000 0.582 0.857
## .wm5 0.368 0.036 10.287 0.000 0.298 0.439
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .Phys_1 0.000 0.000 0.000
## .Phys_2 0.000 0.000 0.000
## .Phys_3 0.000 0.000 0.000
## .Phys_4 0.000 0.000 0.000
## .Phys_5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.736 0.736
## 0.651 0.651
## 0.643 0.643
## 0.622 0.622
## 0.501 0.501
## 0.519 0.519
## 0.673 0.673
## 0.536 0.536
## 0.463 0.463
## 0.573 0.573
## 0.603 0.603
## 0.389 0.389
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000Perceived status difference at time t does not predict positive emotions at time t+1
Positive emotions at time t do not predict fatigue at time t+1
# Same model as above code, but fit with d_black dataset this time
PEmoPhysCLPM_b2AR_controls.fit <- lavaan(PEmoPhysCLPM_2AR_controls, data = d_black, missing = 'ML', meanstructure = T, int.ov.free = T)
summary(PEmoPhysCLPM_b2AR_controls.fit, standardized = T, fit.measures = T, ci = T)## lavaan 0.6-8 ended normally after 58 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 148
## Number of equality constraints 64
##
## Used Total
## Number of observations 451 482
## Number of missing patterns 7
##
## Model Test User Model:
##
## Test statistic 188.143
## Degrees of freedom 111
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 1546.380
## Degrees of freedom 165
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.944
## Tucker-Lewis Index (TLI) 0.917
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -4843.408
## Loglikelihood unrestricted model (H1) -4749.337
##
## Akaike (AIC) 9854.817
## Bayesian (BIC) 10200.180
## Sample-size adjusted Bayesian (BIC) 9933.595
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.039
## 90 Percent confidence interval - lower 0.029
## 90 Percent confidence interval - upper 0.049
## P-value RMSEA <= 0.05 0.969
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.053
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx =~
## LadderDif.1 1.000 1.000 1.000
## LadderDif.2 1.000 1.000 1.000
## LadderDif.3 1.000 1.000 1.000
## LadderDif.4 1.000 1.000 1.000
## LadderDif.5 1.000 1.000 1.000
## RIy =~
## Phys_1 1.000 1.000 1.000
## Phys_2 1.000 1.000 1.000
## Phys_3 1.000 1.000 1.000
## Phys_4 1.000 1.000 1.000
## Phys_5 1.000 1.000 1.000
## RIm =~
## posEmo.1 1.000 1.000 1.000
## posEmo.2 1.000 1.000 1.000
## posEmo.3 1.000 1.000 1.000
## posEmo.4 1.000 1.000 1.000
## posEmo.5 1.000 1.000 1.000
## wx1 =~
## LadderDif.1 1.000 1.000 1.000
## wx2 =~
## LadderDif.2 1.000 1.000 1.000
## wx3 =~
## LadderDif.3 1.000 1.000 1.000
## wx4 =~
## LadderDif.4 1.000 1.000 1.000
## wx5 =~
## LadderDif.5 1.000 1.000 1.000
## wy1 =~
## Phys_1 1.000 1.000 1.000
## wy2 =~
## Phys_2 1.000 1.000 1.000
## wy3 =~
## Phys_3 1.000 1.000 1.000
## wy4 =~
## Phys_4 1.000 1.000 1.000
## wy5 =~
## Phys_5 1.000 1.000 1.000
## wm1 =~
## posEmo.1 1.000 1.000 1.000
## wm2 =~
## posEmo.2 1.000 1.000 1.000
## wm3 =~
## posEmo.3 1.000 1.000 1.000
## wm4 =~
## posEmo.4 1.000 1.000 1.000
## wm5 =~
## posEmo.5 1.000 1.000 1.000
## Std.lv Std.all
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## 0.994 0.985
##
## 0.967 0.984
##
## 0.966 0.984
##
## 0.969 0.984
##
## 0.946 0.984
##
## 1.119 0.973
##
## 1.088 0.971
##
## 1.072 0.970
##
## 1.067 0.970
##
## 1.079 0.971
##
## 0.997 0.994
##
## 0.995 0.994
##
## 0.985 0.994
##
## 0.950 0.993
##
## 0.966 0.994
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## LadderDif.1 ~
## GndrBnr (Gen1) -0.127 0.075 -1.690 0.091 -0.274 0.020
## LadderDif.2 ~
## GndrBnr (Gen1) -0.127 0.075 -1.690 0.091 -0.274 0.020
## LadderDif.3 ~
## GndrBnr (Gen1) -0.127 0.075 -1.690 0.091 -0.274 0.020
## LadderDif.4 ~
## GndrBnr (Gen1) -0.127 0.075 -1.690 0.091 -0.274 0.020
## LadderDif.5 ~
## GndrBnr (Gen1) -0.127 0.075 -1.690 0.091 -0.274 0.020
## posEmo.1 ~
## GndrBnr (Gen2) -0.207 0.086 -2.406 0.016 -0.376 -0.038
## posEmo.2 ~
## GndrBnr (Gen2) -0.207 0.086 -2.406 0.016 -0.376 -0.038
## posEmo.3 ~
## GndrBnr (Gen2) -0.207 0.086 -2.406 0.016 -0.376 -0.038
## posEmo.4 ~
## GndrBnr (Gen2) -0.207 0.086 -2.406 0.016 -0.376 -0.038
## posEmo.5 ~
## GndrBnr (Gen2) -0.207 0.086 -2.406 0.016 -0.376 -0.038
## Phys_1 ~
## GndrBnr (Gen3) 0.067 0.094 0.713 0.476 -0.117 0.251
## Phys_2 ~
## GndrBnr (Gen3) 0.067 0.094 0.713 0.476 -0.117 0.251
## Phys_3 ~
## GndrBnr (Gen3) 0.067 0.094 0.713 0.476 -0.117 0.251
## Phys_4 ~
## GndrBnr (Gen3) 0.067 0.094 0.713 0.476 -0.117 0.251
## Phys_5 ~
## GndrBnr (Gen3) 0.067 0.094 0.713 0.476 -0.117 0.251
## LadderDif.1 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.2 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.3 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.4 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## LadderDif.5 ~
## Edu (Edu1) -0.074 0.039 -1.874 0.061 -0.151 0.003
## posEmo.1 ~
## Edu (Edu2) 0.000 0.045 0.003 0.998 -0.088 0.089
## posEmo.2 ~
## Edu (Edu2) 0.000 0.045 0.003 0.998 -0.088 0.089
## posEmo.3 ~
## Edu (Edu2) 0.000 0.045 0.003 0.998 -0.088 0.089
## posEmo.4 ~
## Edu (Edu2) 0.000 0.045 0.003 0.998 -0.088 0.089
## posEmo.5 ~
## Edu (Edu2) 0.000 0.045 0.003 0.998 -0.088 0.089
## Phys_1 ~
## Edu (Edu3) 0.134 0.049 2.724 0.006 0.038 0.231
## Phys_2 ~
## Edu (Edu3) 0.134 0.049 2.724 0.006 0.038 0.231
## Phys_3 ~
## Edu (Edu3) 0.134 0.049 2.724 0.006 0.038 0.231
## Phys_4 ~
## Edu (Edu3) 0.134 0.049 2.724 0.006 0.038 0.231
## Phys_5 ~
## Edu (Edu3) 0.134 0.049 2.724 0.006 0.038 0.231
## LadderDif.1 ~
## Income (Inc1) -0.112 0.039 -2.887 0.004 -0.187 -0.036
## LadderDif.2 ~
## Income (Inc1) -0.112 0.039 -2.887 0.004 -0.187 -0.036
## LadderDif.3 ~
## Income (Inc1) -0.112 0.039 -2.887 0.004 -0.187 -0.036
## LadderDif.4 ~
## Income (Inc1) -0.112 0.039 -2.887 0.004 -0.187 -0.036
## LadderDif.5 ~
## Income (Inc1) -0.112 0.039 -2.887 0.004 -0.187 -0.036
## posEmo.1 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.2 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.3 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.4 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## posEmo.5 ~
## Income (Inc2) 0.041 0.044 0.933 0.351 -0.045 0.128
## Phys_1 ~
## Income (Inc3) 0.141 0.049 2.902 0.004 0.046 0.236
## Phys_2 ~
## Income (Inc3) 0.141 0.049 2.902 0.004 0.046 0.236
## Phys_3 ~
## Income (Inc3) 0.141 0.049 2.902 0.004 0.046 0.236
## Phys_4 ~
## Income (Inc3) 0.141 0.049 2.902 0.004 0.046 0.236
## Phys_5 ~
## Income (Inc3) 0.141 0.049 2.902 0.004 0.046 0.236
## LadderDif.1 ~
## Age (Age1) 0.044 0.037 1.188 0.235 -0.029 0.118
## LadderDif.2 ~
## Age (Age1) 0.044 0.037 1.188 0.235 -0.029 0.118
## LadderDif.3 ~
## Age (Age1) 0.044 0.037 1.188 0.235 -0.029 0.118
## LadderDif.4 ~
## Age (Age1) 0.044 0.037 1.188 0.235 -0.029 0.118
## LadderDif.5 ~
## Age (Age1) 0.044 0.037 1.188 0.235 -0.029 0.118
## posEmo.1 ~
## Age (Age2) 0.029 0.043 0.673 0.501 -0.055 0.113
## posEmo.2 ~
## Age (Age2) 0.029 0.043 0.673 0.501 -0.055 0.113
## posEmo.3 ~
## Age (Age2) 0.029 0.043 0.673 0.501 -0.055 0.113
## posEmo.4 ~
## Age (Age2) 0.029 0.043 0.673 0.501 -0.055 0.113
## posEmo.5 ~
## Age (Age2) 0.029 0.043 0.673 0.501 -0.055 0.113
## Phys_1 ~
## Age (Age3) 0.098 0.047 2.084 0.037 0.006 0.189
## Phys_2 ~
## Age (Age3) 0.098 0.047 2.084 0.037 0.006 0.189
## Phys_3 ~
## Age (Age3) 0.098 0.047 2.084 0.037 0.006 0.189
## Phys_4 ~
## Age (Age3) 0.098 0.047 2.084 0.037 0.006 0.189
## Phys_5 ~
## Age (Age3) 0.098 0.047 2.084 0.037 0.006 0.189
## wy2 ~
## wy1 0.497 0.055 9.055 0.000 0.389 0.604
## wm1 (b1) 0.057 0.030 1.869 0.062 -0.003 0.116
## wy3 ~
## wx1 (cp1) 0.007 0.037 0.186 0.853 -0.066 0.080
## wy2 0.378 0.065 5.855 0.000 0.251 0.505
## wm2 (b1) 0.057 0.030 1.869 0.062 -0.003 0.116
## wy1 0.311 0.065 4.792 0.000 0.183 0.438
## wy4 ~
## wx2 (cp1) 0.007 0.037 0.186 0.853 -0.066 0.080
## wy3 0.289 0.068 4.270 0.000 0.156 0.421
## wm3 (b1) 0.057 0.030 1.869 0.062 -0.003 0.116
## wy2 0.392 0.069 5.658 0.000 0.256 0.528
## wy5 ~
## wx3 (cp1) 0.007 0.037 0.186 0.853 -0.066 0.080
## wy4 0.490 0.061 8.015 0.000 0.370 0.609
## wm4 (b1) 0.057 0.030 1.869 0.062 -0.003 0.116
## wy3 0.341 0.063 5.420 0.000 0.218 0.465
## wx2 ~
## wx1 0.213 0.061 3.467 0.001 0.093 0.333
## wm1 (b2) -0.039 0.031 -1.261 0.207 -0.100 0.022
## wx3 ~
## wx2 0.434 0.066 6.578 0.000 0.305 0.563
## wy1 (cp2) 0.019 0.035 0.535 0.593 -0.050 0.088
## wm2 (b2) -0.039 0.031 -1.261 0.207 -0.100 0.022
## wx1 0.157 0.063 2.483 0.013 0.033 0.281
## wx4 ~
## wx3 0.259 0.078 3.326 0.001 0.106 0.412
## wy2 (cp2) 0.019 0.035 0.535 0.593 -0.050 0.088
## wm3 (b2) -0.039 0.031 -1.261 0.207 -0.100 0.022
## wx2 0.246 0.079 3.127 0.002 0.092 0.399
## wx5 ~
## wx4 0.238 0.065 3.672 0.000 0.111 0.365
## wy3 (cp2) 0.019 0.035 0.535 0.593 -0.050 0.088
## wm4 (b2) -0.039 0.031 -1.261 0.207 -0.100 0.022
## wx3 0.416 0.070 5.953 0.000 0.279 0.553
## wm2 ~
## wx1 (a1) -0.034 0.025 -1.334 0.182 -0.084 0.016
## wy1 (a2) 0.016 0.023 0.671 0.502 -0.030 0.061
## wm1 0.529 0.053 10.012 0.000 0.425 0.632
## wm3 ~
## wx2 (a1) -0.034 0.025 -1.334 0.182 -0.084 0.016
## wy2 (a2) 0.016 0.023 0.671 0.502 -0.030 0.061
## wm2 0.599 0.054 11.028 0.000 0.493 0.706
## wm1 0.225 0.053 4.250 0.000 0.121 0.329
## wm4 ~
## wx3 (a1) -0.034 0.025 -1.334 0.182 -0.084 0.016
## wy3 (a2) 0.016 0.023 0.671 0.502 -0.030 0.061
## wm3 0.440 0.070 6.290 0.000 0.303 0.577
## wm2 0.334 0.067 4.973 0.000 0.202 0.465
## wm5 ~
## wx4 (a1) -0.034 0.025 -1.334 0.182 -0.084 0.016
## wy4 (a2) 0.016 0.023 0.671 0.502 -0.030 0.061
## wm4 0.340 0.071 4.816 0.000 0.202 0.479
## wm3 0.475 0.068 6.989 0.000 0.342 0.609
## Std.lv Std.all
##
## -0.127 -0.063
##
## -0.127 -0.065
##
## -0.127 -0.065
##
## -0.127 -0.064
##
## -0.127 -0.066
##
## -0.207 -0.103
##
## -0.207 -0.103
##
## -0.207 -0.104
##
## -0.207 -0.108
##
## -0.207 -0.106
##
## 0.067 0.029
##
## 0.067 0.030
##
## 0.067 0.030
##
## 0.067 0.030
##
## 0.067 0.030
##
## -0.074 -0.073
##
## -0.074 -0.075
##
## -0.074 -0.075
##
## -0.074 -0.075
##
## -0.074 -0.077
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.000 0.000
##
## 0.134 0.117
##
## 0.134 0.120
##
## 0.134 0.122
##
## 0.134 0.122
##
## 0.134 0.121
##
## -0.112 -0.110
##
## -0.112 -0.113
##
## -0.112 -0.114
##
## -0.112 -0.113
##
## -0.112 -0.116
##
## 0.041 0.041
##
## 0.041 0.041
##
## 0.041 0.042
##
## 0.041 0.043
##
## 0.041 0.043
##
## 0.141 0.122
##
## 0.141 0.126
##
## 0.141 0.128
##
## 0.141 0.128
##
## 0.141 0.127
##
## 0.044 0.044
##
## 0.044 0.045
##
## 0.044 0.045
##
## 0.044 0.045
##
## 0.044 0.046
##
## 0.029 0.029
##
## 0.029 0.029
##
## 0.029 0.029
##
## 0.029 0.030
##
## 0.029 0.030
##
## 0.098 0.085
##
## 0.098 0.087
##
## 0.098 0.088
##
## 0.098 0.089
##
## 0.098 0.088
##
## 0.510 0.510
## 0.052 0.052
##
## 0.006 0.006
## 0.384 0.384
## 0.053 0.053
## 0.324 0.324
##
## 0.006 0.006
## 0.290 0.290
## 0.052 0.052
## 0.400 0.400
##
## 0.006 0.006
## 0.484 0.484
## 0.050 0.050
## 0.339 0.339
##
## 0.219 0.219
## -0.040 -0.040
##
## 0.435 0.435
## 0.022 0.022
## -0.040 -0.040
## 0.162 0.162
##
## 0.258 0.258
## 0.021 0.021
## -0.040 -0.040
## 0.245 0.245
##
## 0.244 0.244
## 0.021 0.021
## -0.039 -0.039
## 0.425 0.425
##
## -0.034 -0.034
## 0.018 0.018
## 0.529 0.529
##
## -0.033 -0.033
## 0.017 0.017
## 0.606 0.606
## 0.228 0.228
##
## -0.034 -0.034
## 0.018 0.018
## 0.456 0.456
## 0.350 0.350
##
## -0.034 -0.034
## 0.017 0.017
## 0.335 0.335
## 0.485 0.485
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## wx1 ~~
## wy1 -0.021 0.053 -0.406 0.685 -0.125 0.082
## wm1 -0.013 0.047 -0.267 0.789 -0.104 0.079
## wy1 ~~
## wm1 0.349 0.055 6.330 0.000 0.241 0.457
## .wx2 ~~
## .wy2 -0.057 0.056 -1.025 0.305 -0.167 0.052
## .wx3 ~~
## .wy3 0.101 0.050 2.042 0.041 0.004 0.198
## .wx4 ~~
## .wy4 -0.097 0.055 -1.774 0.076 -0.205 0.010
## .wx5 ~~
## .wy5 0.005 0.042 0.128 0.898 -0.078 0.089
## .wx2 ~~
## .wm2 -0.033 0.050 -0.659 0.510 -0.131 0.065
## .wx3 ~~
## .wm3 -0.010 0.038 -0.276 0.782 -0.085 0.064
## .wx4 ~~
## .wm4 0.014 0.040 0.348 0.728 -0.064 0.092
## .wx5 ~~
## .wm5 -0.057 0.037 -1.551 0.121 -0.129 0.015
## .wy2 ~~
## .wm2 0.099 0.049 2.014 0.044 0.003 0.196
## .wy3 ~~
## .wm3 0.066 0.038 1.732 0.083 -0.009 0.140
## .wy4 ~~
## .wm4 0.074 0.039 1.895 0.058 -0.003 0.150
## .wy5 ~~
## .wm5 0.016 0.034 0.463 0.644 -0.051 0.083
## RIx ~~
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## RIy ~~
## RIm 0.000 0.000 0.000
## Std.lv Std.all
##
## -0.019 -0.019
## -0.013 -0.013
##
## 0.312 0.312
##
## -0.066 -0.066
##
## 0.146 0.146
##
## -0.134 -0.134
##
## 0.010 0.010
##
## -0.042 -0.042
##
## -0.020 -0.020
##
## 0.026 0.026
##
## -0.119 -0.119
##
## 0.128 0.128
##
## 0.124 0.124
##
## 0.143 0.143
##
## 0.035 0.035
##
## NaN NaN
## NaN NaN
##
## NaN NaN
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## .LadderDif.1 0.067 0.061 1.094 0.274 -0.053 0.187
## .LadderDif.2 0.124 0.072 1.718 0.086 -0.018 0.267
## .LadderDif.3 0.104 0.077 1.348 0.178 -0.047 0.255
## .LadderDif.4 0.144 0.081 1.789 0.074 -0.014 0.302
## .LadderDif.5 0.121 0.080 1.502 0.133 -0.037 0.278
## .Phys_1 3.810 0.072 52.909 0.000 3.669 3.952
## .Phys_2 3.754 0.081 46.098 0.000 3.595 3.914
## .Phys_3 3.869 0.085 45.631 0.000 3.703 4.035
## .Phys_4 3.812 0.088 43.138 0.000 3.639 3.985
## .Phys_5 3.874 0.091 42.754 0.000 3.696 4.052
## .posEmo.1 0.108 0.065 1.661 0.097 -0.019 0.236
## .posEmo.2 0.103 0.074 1.382 0.167 -0.043 0.248
## .posEmo.3 0.106 0.076 1.391 0.164 -0.043 0.256
## .posEmo.4 0.086 0.078 1.112 0.266 -0.066 0.238
## .posEmo.5 0.080 0.080 1.004 0.315 -0.077 0.237
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.000 0.000 0.000
## .wx2 0.000 0.000 0.000
## .wx3 0.000 0.000 0.000
## .wx4 0.000 0.000 0.000
## .wx5 0.000 0.000 0.000
## wy1 0.000 0.000 0.000
## .wy2 0.000 0.000 0.000
## .wy3 0.000 0.000 0.000
## .wy4 0.000 0.000 0.000
## .wy5 0.000 0.000 0.000
## wm1 0.000 0.000 0.000
## .wm2 0.000 0.000 0.000
## .wm3 0.000 0.000 0.000
## .wm4 0.000 0.000 0.000
## .wm5 0.000 0.000 0.000
## Std.lv Std.all
## 0.067 0.066
## 0.124 0.127
## 0.104 0.106
## 0.144 0.146
## 0.121 0.125
## 3.810 3.312
## 3.754 3.350
## 3.869 3.502
## 3.812 3.466
## 3.874 3.486
## 0.108 0.108
## 0.103 0.103
## 0.106 0.107
## 0.086 0.090
## 0.080 0.083
## NaN NaN
## NaN NaN
## NaN NaN
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) ci.lower ci.upper
## RIx 0.000 0.000 0.000
## RIy 0.000 0.000 0.000
## RIm 0.000 0.000 0.000
## wx1 0.989 0.066 14.942 0.000 0.859 1.118
## wy1 1.252 0.083 15.009 0.000 1.089 1.416
## wm1 0.994 0.066 15.001 0.000 0.864 1.124
## .wx2 0.888 0.079 11.180 0.000 0.732 1.044
## .wy2 0.853 0.076 11.233 0.000 0.704 1.002
## .wm2 0.706 0.063 11.226 0.000 0.582 0.829
## .wx3 0.699 0.070 10.036 0.000 0.563 0.836
## .wy3 0.688 0.069 10.017 0.000 0.554 0.823
## .wm3 0.409 0.041 10.035 0.000 0.329 0.489
## .wx4 0.760 0.079 9.644 0.000 0.606 0.915
## .wy4 0.691 0.072 9.589 0.000 0.550 0.832
## .wm4 0.383 0.040 9.635 0.000 0.305 0.461
## .wx5 0.603 0.064 9.437 0.000 0.478 0.729
## .wy5 0.530 0.056 9.429 0.000 0.420 0.640
## .wm5 0.381 0.040 9.437 0.000 0.302 0.460
## .LadderDif.1 0.000 0.000 0.000
## .LadderDif.2 0.000 0.000 0.000
## .LadderDif.3 0.000 0.000 0.000
## .LadderDif.4 0.000 0.000 0.000
## .LadderDif.5 0.000 0.000 0.000
## .Phys_1 0.000 0.000 0.000
## .Phys_2 0.000 0.000 0.000
## .Phys_3 0.000 0.000 0.000
## .Phys_4 0.000 0.000 0.000
## .Phys_5 0.000 0.000 0.000
## .posEmo.1 0.000 0.000 0.000
## .posEmo.2 0.000 0.000 0.000
## .posEmo.3 0.000 0.000 0.000
## .posEmo.4 0.000 0.000 0.000
## .posEmo.5 0.000 0.000 0.000
## Std.lv Std.all
## NaN NaN
## NaN NaN
## NaN NaN
## 1.000 1.000
## 1.000 1.000
## 1.000 1.000
## 0.950 0.950
## 0.720 0.720
## 0.712 0.712
## 0.750 0.750
## 0.599 0.599
## 0.422 0.422
## 0.810 0.810
## 0.607 0.607
## 0.424 0.424
## 0.675 0.675
## 0.456 0.456
## 0.408 0.408
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000
## 0.000 0.000