library(dplyr)##
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(modsem)
library(lavaan)## This is lavaan 0.6-19
## lavaan is FREE software! Please report any bugs.library(ggplot2)
library(corrplot)## corrplot 0.95 loadedlibrary(ggcorrplot)
library(psych)##
## Attaching package: 'psych'
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## The following object is masked from 'package:lavaan':
##
## cor2cov
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## %+%, alphalibrary(tidyverse)
library(dplyr)
library(tidyr)
library(semTools)##
## ###############################################################################
## This is semTools 0.5-7
## All users of R (or SEM) are invited to submit functions or ideas for functions.
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##
## Attaching package: 'semTools'
##
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## reliability, skew
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##
## clipboardlibrary(stdmod)
library(semTools)#read in data
setwd("~/Downloads/foldah")
ANES <- read.csv("ANES_cleanmerge.csv")#RICLPM EPE + CA
RICLPM1 <- '
# Create between components (random intercepts)
RIepe =~ 1*ex_eff_w1 + 1*ex_eff_w2 + 1*ex_eff_w3
RIca =~ 1*c_act_w1 + 1*c_act_w2 + 1*c_act_w3
# Create within-person centered variables
wepe1 =~ 1*ex_eff_w1
wepe2 =~ 1*ex_eff_w2
wepe3 =~ 1*ex_eff_w3
wca1 =~ 1*c_act_w1
wca2 =~ 1*c_act_w2
wca3 =~ 1*c_act_w3
# Estimate lagged effects between within-person centered variables
wepe2 + wca2 ~ wepe1 + wca1
wepe3 + wca3 ~ wepe2 + wca2
# Estimate covariance between within-person centered variables at first wave
wepe1 ~~ wca1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wepe2 ~~ wca2
wepe3 ~~ wca3
# Estimate variance and covariance of random intercepts
RIepe ~~ RIepe
RIca ~~ RIca
RIepe ~~ RIca
# Estimate (residual) variance of within-person centered variables
wepe1 ~~ wepe1 # Variances
wca1 ~~ wca1
wepe2 ~~ wepe2 # Residual variances
wca2 ~~ wca2
wepe3 ~~ wepe3
wca3 ~~ wca3'
RICLPM1fit <- lavaan(RICLPM1,
data = ANES,
missing = 'ML',
estimator = "ML",
meanstructure = T,
int.ov.free = T)
summary(RICLPM1fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-19 ended normally after 70 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2171
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 0.487
## Degrees of freedom 1
## P-value (Chi-square) 0.485
##
## Model Test Baseline Model:
##
## Test statistic 1757.420
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.004
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.004
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7735.790
## Loglikelihood unrestricted model (H1) -7735.547
##
## Akaike (AIC) 15523.580
## Bayesian (BIC) 15671.337
## Sample-size adjusted Bayesian (SABIC) 15588.731
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.050
## P-value H_0: RMSEA <= 0.050 0.950
## P-value H_0: RMSEA >= 0.080 0.001
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.051
## P-value H_0: Robust RMSEA <= 0.050 0.944
## P-value H_0: Robust RMSEA >= 0.080 0.002
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.003
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIepe =~
## ex_eff_w1 1.000 0.553 0.547
## ex_eff_w2 1.000 0.553 0.533
## ex_eff_w3 1.000 0.553 0.575
## RIca =~
## c_act_w1 1.000 0.120 0.466
## c_act_w2 1.000 0.120 0.489
## c_act_w3 1.000 0.120 0.672
## wepe1 =~
## ex_eff_w1 1.000 0.847 0.837
## wepe2 =~
## ex_eff_w2 1.000 0.879 0.846
## wepe3 =~
## ex_eff_w3 1.000 0.787 0.818
## wca1 =~
## c_act_w1 1.000 0.228 0.885
## wca2 =~
## c_act_w2 1.000 0.214 0.872
## wca3 =~
## c_act_w3 1.000 0.132 0.740
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wepe2 ~
## wepe1 0.083 0.039 2.115 0.034 0.080 0.080
## wca1 0.265 0.117 2.265 0.024 0.068 0.068
## wca2 ~
## wepe1 0.003 0.007 0.380 0.704 0.010 0.010
## wca1 0.278 0.028 10.081 0.000 0.297 0.297
## wepe3 ~
## wepe2 0.164 0.033 4.989 0.000 0.183 0.183
## wca2 -0.135 0.119 -1.129 0.259 -0.037 -0.037
## wca3 ~
## wepe2 0.010 0.006 1.695 0.090 0.068 0.068
## wca2 0.040 0.032 1.264 0.206 0.065 0.065
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wepe1 ~~
## wca1 0.016 0.005 2.868 0.004 0.081 0.081
## .wepe2 ~~
## .wca2 0.015 0.005 2.849 0.004 0.086 0.086
## .wepe3 ~~
## .wca3 -0.006 0.004 -1.489 0.136 -0.056 -0.056
## RIepe ~~
## RIca 0.012 0.004 3.048 0.002 0.179 0.179
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .ex_eff_w1 2.595 0.022 119.380 0.000 2.595 2.566
## .ex_eff_w2 2.412 0.022 107.996 0.000 2.412 2.322
## .ex_eff_w3 2.241 0.021 106.263 0.000 2.241 2.329
## .c_act_w1 0.213 0.006 38.577 0.000 0.213 0.829
## .c_act_w2 0.171 0.005 32.614 0.000 0.171 0.700
## .c_act_w3 0.081 0.004 20.773 0.000 0.081 0.455
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIepe 0.306 0.026 11.697 0.000 1.000 1.000
## RIca 0.014 0.001 10.397 0.000 1.000 1.000
## wepe1 0.717 0.032 22.243 0.000 1.000 1.000
## wca1 0.052 0.002 26.794 0.000 1.000 1.000
## .wepe2 0.764 0.037 20.843 0.000 0.988 0.988
## .wca2 0.042 0.002 24.776 0.000 0.911 0.911
## .wepe3 0.599 0.025 23.532 0.000 0.967 0.967
## .wca3 0.017 0.001 13.128 0.000 0.990 0.990
## .ex_eff_w1 0.000 0.000 0.000
## .ex_eff_w2 0.000 0.000 0.000
## .ex_eff_w3 0.000 0.000 0.000
## .c_act_w1 0.000 0.000 0.000
## .c_act_w2 0.000 0.000 0.000
## .c_act_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wepe2 0.012
## wca2 0.089
## wepe3 0.033
## wca3 0.010
## ex_eff_w1 1.000
## ex_eff_w2 1.000
## ex_eff_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000#RICLPM CR + CA
RICLPM2 <- '
# Create between components (random intercepts)
RIcr =~ 1*c_ref_w1 + 1*c_ref_w2 + 1*c_ref_w3
RIca =~ 1*c_act_w1 + 1*c_act_w2 + 1*c_act_w3
# Create within-person centered variables
wcr1 =~ 1*c_ref_w1
wcr2 =~ 1*c_ref_w2
wcr3 =~ 1*c_ref_w3
wca1 =~ 1*c_act_w1
wca2 =~ 1*c_act_w2
wca3 =~ 1*c_act_w3
# Estimate lagged effects between within-person centered variables
wcr2 + wca2 ~ wcr1 + wca1
wcr3 + wca3 ~ wcr2 + wca2
# Estimate covariance between within-person centered variables at first wave
wcr1 ~~ wca1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wcr2 ~~ wca2
wcr3 ~~ wca3
# Estimate variance and covariance of random intercepts
RIcr ~~ RIcr
RIca ~~ RIca
RIcr ~~ RIca
# Estimate (residual) variance of within-person centered variables
wcr1 ~~ wcr1 # Variances
wca1 ~~ wca1
wcr2 ~~ wcr2 # Residual variances
wca2 ~~ wca2
wcr3 ~~ wcr3
wca3 ~~ wca3'
RICLPM2fit <- lavaan(RICLPM2,
data = ANES,
missing = 'ML',
estimator = "ML",
meanstructure = T,
int.ov.free = T)
summary(RICLPM2fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-19 ended normally after 69 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2171
## Number of missing patterns 12
##
## Model Test User Model:
##
## Test statistic 3.902
## Degrees of freedom 1
## P-value (Chi-square) 0.048
##
## Model Test Baseline Model:
##
## Test statistic 4427.216
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.999
## Tucker-Lewis Index (TLI) 0.990
##
## Robust Comparative Fit Index (CFI) 0.999
## Robust Tucker-Lewis Index (TLI) 0.990
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -7698.006
## Loglikelihood unrestricted model (H1) -7696.055
##
## Akaike (AIC) 15448.011
## Bayesian (BIC) 15595.768
## Sample-size adjusted Bayesian (SABIC) 15513.162
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.037
## 90 Percent confidence interval - lower 0.003
## 90 Percent confidence interval - upper 0.078
## P-value H_0: RMSEA <= 0.050 0.638
## P-value H_0: RMSEA >= 0.080 0.040
##
## Robust RMSEA 0.037
## 90 Percent confidence interval - lower 0.003
## 90 Percent confidence interval - upper 0.079
## P-value H_0: Robust RMSEA <= 0.050 0.621
## P-value H_0: Robust RMSEA >= 0.080 0.047
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.008
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.974 0.793
## c_ref_w2 1.000 0.974 0.769
## c_ref_w3 1.000 0.974 0.803
## RIca =~
## c_act_w1 1.000 0.120 0.466
## c_act_w2 1.000 0.120 0.489
## c_act_w3 1.000 0.120 0.671
## wcr1 =~
## c_ref_w1 1.000 0.749 0.610
## wcr2 =~
## c_ref_w2 1.000 0.809 0.639
## wcr3 =~
## c_ref_w3 1.000 0.723 0.596
## wca1 =~
## c_act_w1 1.000 0.227 0.885
## wca2 =~
## c_act_w2 1.000 0.214 0.872
## wca3 =~
## c_act_w3 1.000 0.132 0.741
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.212 0.055 3.827 0.000 0.196 0.196
## wca1 0.264 0.110 2.407 0.016 0.074 0.074
## wca2 ~
## wcr1 0.057 0.011 5.337 0.000 0.200 0.200
## wca1 0.260 0.027 9.572 0.000 0.276 0.276
## wcr3 ~
## wcr2 0.265 0.040 6.607 0.000 0.297 0.297
## wca2 0.638 0.129 4.951 0.000 0.188 0.188
## wca3 ~
## wcr2 0.003 0.007 0.429 0.668 0.019 0.019
## wca2 0.043 0.033 1.306 0.192 0.069 0.069
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wca1 0.017 0.006 2.902 0.004 0.099 0.099
## .wcr2 ~~
## .wca2 0.038 0.006 6.839 0.000 0.243 0.243
## .wcr3 ~~
## .wca3 0.005 0.003 1.394 0.163 0.052 0.052
## RIcr ~~
## RIca 0.031 0.005 6.258 0.000 0.267 0.267
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.041 0.026 115.113 0.000 3.041 2.474
## .c_ref_w2 3.280 0.027 120.430 0.000 3.280 2.590
## .c_ref_w3 3.161 0.026 119.929 0.000 3.161 2.606
## .c_act_w1 0.213 0.006 38.655 0.000 0.213 0.831
## .c_act_w2 0.171 0.005 32.606 0.000 0.171 0.700
## .c_act_w3 0.081 0.004 20.755 0.000 0.081 0.454
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.949 0.043 21.953 0.000 1.000 1.000
## RIca 0.014 0.001 10.388 0.000 1.000 1.000
## wcr1 0.562 0.035 16.072 0.000 1.000 1.000
## wca1 0.052 0.002 26.640 0.000 1.000 1.000
## .wcr2 0.624 0.039 16.024 0.000 0.953 0.953
## .wca2 0.040 0.002 24.295 0.000 0.873 0.873
## .wcr3 0.441 0.022 20.436 0.000 0.844 0.844
## .wca3 0.017 0.001 13.274 0.000 0.994 0.994
## .c_ref_w1 0.000 0.000 0.000
## .c_ref_w2 0.000 0.000 0.000
## .c_ref_w3 0.000 0.000 0.000
## .c_act_w1 0.000 0.000 0.000
## .c_act_w2 0.000 0.000 0.000
## .c_act_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.047
## wca2 0.127
## wcr3 0.156
## wca3 0.006
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000#RICLPM CR + EPE
RICLPM4 <- '
# Create between components (random intercepts)
RIcr =~ 1*c_ref_w1 + 1*c_ref_w2 + 1*c_ref_w3
RIepe =~ 1*ex_eff_w1 + 1*ex_eff_w2 + 1*ex_eff_w3
# Create within-person centered variables
wcr1 =~ 1*c_ref_w1
wcr2 =~ 1*c_ref_w2
wcr3 =~ 1*c_ref_w3
wepe1 =~ 1*ex_eff_w1
wepe2 =~ 1*ex_eff_w2
wepe3 =~ 1*ex_eff_w3
# Estimate lagged effects between within-person centered variables
wcr2 + wepe2 ~ wcr1 + wepe1
wcr3 + wepe3 ~ wcr2 + wepe2
# Estimate covariance between within-person centered variables at first wave
wcr1 ~~ wepe1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wcr2 ~~ wepe2
wcr3 ~~ wepe3
# Estimate variance and covariance of random intercepts
RIcr ~~ RIcr
RIepe ~~ RIepe
RIcr ~~ RIepe
# Estimate (residual) variance of within-person centered variables
wcr1 ~~ wcr1 # Variances
wepe1 ~~ wepe1
wcr2 ~~ wcr2 # Residual variances
wepe2 ~~ wepe2
wcr3 ~~ wcr3
wepe3 ~~ wepe3'
RICLPM4fit <- lavaan(RICLPM4,
data = ANES,
missing = 'ML',
estimator = "ML",
meanstructure = T,
int.ov.free = T)
summary(RICLPM4fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-19 ended normally after 43 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2171
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 0.054
## Degrees of freedom 1
## P-value (Chi-square) 0.817
##
## Model Test Baseline Model:
##
## Test statistic 4270.344
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.003
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 1.003
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -17340.570
## Loglikelihood unrestricted model (H1) -17340.543
##
## Akaike (AIC) 34733.139
## Bayesian (BIC) 34880.896
## Sample-size adjusted Bayesian (SABIC) 34798.291
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.035
## P-value H_0: RMSEA <= 0.050 0.987
## P-value H_0: RMSEA >= 0.080 0.000
##
## Robust RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.036
## P-value H_0: Robust RMSEA <= 0.050 0.985
## P-value H_0: Robust RMSEA >= 0.080 0.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.001
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.959 0.781
## c_ref_w2 1.000 0.959 0.757
## c_ref_w3 1.000 0.959 0.790
## RIepe =~
## ex_eff_w1 1.000 0.551 0.545
## ex_eff_w2 1.000 0.551 0.530
## ex_eff_w3 1.000 0.551 0.573
## wcr1 =~
## c_ref_w1 1.000 0.767 0.624
## wcr2 =~
## c_ref_w2 1.000 0.827 0.653
## wcr3 =~
## c_ref_w3 1.000 0.744 0.613
## wepe1 =~
## ex_eff_w1 1.000 0.848 0.839
## wepe2 =~
## ex_eff_w2 1.000 0.881 0.848
## wepe3 =~
## ex_eff_w3 1.000 0.788 0.820
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.252 0.053 4.707 0.000 0.233 0.233
## wepe1 0.069 0.031 2.207 0.027 0.070 0.070
## wepe2 ~
## wcr1 0.376 0.048 7.921 0.000 0.328 0.328
## wepe1 0.066 0.037 1.772 0.076 0.063 0.063
## wcr3 ~
## wcr2 0.274 0.039 6.929 0.000 0.304 0.304
## wepe2 0.193 0.033 5.899 0.000 0.228 0.228
## wepe3 ~
## wcr2 -0.008 0.035 -0.235 0.814 -0.009 -0.009
## wepe2 0.166 0.034 4.827 0.000 0.185 0.185
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wepe1 0.050 0.024 2.060 0.039 0.077 0.077
## .wcr2 ~~
## .wepe2 0.189 0.026 7.413 0.000 0.285 0.285
## .wcr3 ~~
## .wepe3 -0.013 0.016 -0.799 0.424 -0.025 -0.025
## RIcr ~~
## RIepe 0.055 0.024 2.292 0.022 0.105 0.105
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.041 0.026 115.174 0.000 3.041 2.476
## .c_ref_w2 3.280 0.027 120.418 0.000 3.280 2.589
## .c_ref_w3 3.161 0.026 119.839 0.000 3.161 2.604
## .ex_eff_w1 2.596 0.022 119.396 0.000 2.596 2.566
## .ex_eff_w2 2.412 0.022 107.976 0.000 2.412 2.321
## .ex_eff_w3 2.241 0.021 106.311 0.000 2.241 2.330
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.921 0.043 21.268 0.000 1.000 1.000
## RIepe 0.304 0.026 11.560 0.000 1.000 1.000
## wcr1 0.588 0.037 15.834 0.000 1.000 1.000
## wepe1 0.719 0.032 22.155 0.000 1.000 1.000
## .wcr2 0.641 0.038 17.027 0.000 0.938 0.938
## .wepe2 0.687 0.036 18.938 0.000 0.885 0.885
## .wcr3 0.447 0.021 20.855 0.000 0.808 0.808
## .wepe3 0.601 0.025 23.615 0.000 0.967 0.967
## .c_ref_w1 0.000 0.000 0.000
## .c_ref_w2 0.000 0.000 0.000
## .c_ref_w3 0.000 0.000 0.000
## .ex_eff_w1 0.000 0.000 0.000
## .ex_eff_w2 0.000 0.000 0.000
## .ex_eff_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.062
## wepe2 0.115
## wcr3 0.192
## wepe3 0.033
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## ex_eff_w1 1.000
## ex_eff_w2 1.000
## ex_eff_w3 1.000#RICLPM CR + IPE
RICLPM6 <- '
# Create between components (random intercepts)
RIcr =~ 1*c_ref_w1 + 1*c_ref_w2 + 1*c_ref_w3
RIipe =~ 1*in_eff_w1 + 1*in_eff_w2 + 1*in_eff_w3
# Create within-person centered variables
wcr1 =~ 1*c_ref_w1
wcr2 =~ 1*c_ref_w2
wcr3 =~ 1*c_ref_w3
wipe1 =~ 1*in_eff_w1
wipe2 =~ 1*in_eff_w2
wipe3 =~ 1*in_eff_w3
# Estimate lagged effects between within-person centered variables
wcr2 + wipe2 ~ wcr1 + wipe1
wcr3 + wipe3 ~ wcr2 + wipe2
# Estimate covariance between within-person centered variables at first wave
wcr1 ~~ wipe1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wcr2 ~~ wipe2
wcr3 ~~ wipe3
# Estimate variance and covariance of random intercepts
RIcr ~~ RIcr
RIipe ~~ RIipe
RIcr ~~ RIipe
# Estimate (residual) variance of within-person centered variables
wcr1 ~~ wcr1 # Variances
wipe1 ~~ wipe1
wcr2 ~~ wcr2 # Residual variances
wipe2 ~~ wipe2
wcr3 ~~ wcr3
wipe3 ~~ wipe3'
RICLPM6fit <- lavaan(RICLPM6,
data = ANES,
missing = 'ML',
estimator = "ML",
meanstructure = T,
int.ov.free = T)## Warning: lavaan->lav_data_full():
## some cases are empty and will be ignored: 887.summary(RICLPM6fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-19 ended normally after 47 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Used Total
## Number of observations 2170 2171
## Number of missing patterns 17
##
## Model Test User Model:
##
## Test statistic 1.281
## Degrees of freedom 1
## P-value (Chi-square) 0.258
##
## Model Test Baseline Model:
##
## Test statistic 5366.744
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 0.999
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 0.999
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -15431.882
## Loglikelihood unrestricted model (H1) -15431.242
##
## Akaike (AIC) 30915.764
## Bayesian (BIC) 31063.509
## Sample-size adjusted Bayesian (SABIC) 30980.904
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.011
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.060
## P-value H_0: RMSEA <= 0.050 0.885
## P-value H_0: RMSEA >= 0.080 0.005
##
## Robust RMSEA 0.012
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.061
## P-value H_0: Robust RMSEA <= 0.050 0.873
## P-value H_0: Robust RMSEA >= 0.080 0.006
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.004
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr =~
## c_ref_w1 1.000 0.990 0.806
## c_ref_w2 1.000 0.990 0.781
## c_ref_w3 1.000 0.990 0.815
## RIipe =~
## in_eff_w1 1.000 0.567 0.710
## in_eff_w2 1.000 0.567 0.689
## in_eff_w3 1.000 0.567 0.694
## wcr1 =~
## c_ref_w1 1.000 0.726 0.592
## wcr2 =~
## c_ref_w2 1.000 0.790 0.624
## wcr3 =~
## c_ref_w3 1.000 0.704 0.580
## wipe1 =~
## in_eff_w1 1.000 0.562 0.704
## wipe2 =~
## in_eff_w2 1.000 0.596 0.724
## wipe3 =~
## in_eff_w3 1.000 0.588 0.720
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr2 ~
## wcr1 0.173 0.060 2.888 0.004 0.159 0.159
## wipe1 0.074 0.058 1.276 0.202 0.053 0.053
## wipe2 ~
## wcr1 0.010 0.040 0.256 0.798 0.013 0.013
## wipe1 0.145 0.053 2.758 0.006 0.137 0.137
## wcr3 ~
## wcr2 0.285 0.040 7.201 0.000 0.319 0.319
## wipe2 -0.060 0.048 -1.255 0.210 -0.051 -0.051
## wipe3 ~
## wcr2 0.029 0.026 1.124 0.261 0.039 0.039
## wipe2 0.282 0.037 7.525 0.000 0.286 0.286
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wcr1 ~~
## wipe1 0.003 0.018 0.161 0.872 0.007 0.007
## .wcr2 ~~
## .wipe2 0.010 0.021 0.469 0.639 0.021 0.021
## .wcr3 ~~
## .wipe3 0.001 0.012 0.087 0.930 0.003 0.003
## RIcr ~~
## RIipe 0.120 0.021 5.803 0.000 0.214 0.214
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .c_ref_w1 3.041 0.026 115.240 0.000 3.041 2.477
## .c_ref_w2 3.280 0.027 120.397 0.000 3.280 2.589
## .c_ref_w3 3.161 0.026 119.741 0.000 3.161 2.602
## .in_eff_w1 3.221 0.017 187.567 0.000 3.221 4.032
## .in_eff_w2 3.378 0.018 191.015 0.000 3.378 4.105
## .in_eff_w3 3.350 0.018 188.105 0.000 3.350 4.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIcr 0.980 0.044 22.329 0.000 1.000 1.000
## RIipe 0.322 0.019 16.573 0.000 1.000 1.000
## wcr1 0.527 0.034 15.539 0.000 1.000 1.000
## wipe1 0.316 0.019 16.959 0.000 1.000 1.000
## .wcr2 0.607 0.041 14.876 0.000 0.972 0.972
## .wipe2 0.349 0.021 16.779 0.000 0.981 0.981
## .wcr3 0.445 0.022 20.428 0.000 0.896 0.896
## .wipe3 0.317 0.013 24.244 0.000 0.916 0.916
## .c_ref_w1 0.000 0.000 0.000
## .c_ref_w2 0.000 0.000 0.000
## .c_ref_w3 0.000 0.000 0.000
## .in_eff_w1 0.000 0.000 0.000
## .in_eff_w2 0.000 0.000 0.000
## .in_eff_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wcr2 0.028
## wipe2 0.019
## wcr3 0.104
## wipe3 0.084
## c_ref_w1 1.000
## c_ref_w2 1.000
## c_ref_w3 1.000
## in_eff_w1 1.000
## in_eff_w2 1.000
## in_eff_w3 1.000#RICLPM IPE + CA
RICLPM7 <- '
# Create between components (random intercepts)
RIipe =~ 1*in_eff_w1 + 1*in_eff_w2 + 1*in_eff_w3
RIca =~ 1*c_act_w1 + 1*c_act_w2 + 1*c_act_w3
# Create within-person centered variables
wipe1 =~ 1*in_eff_w1
wipe2 =~ 1*in_eff_w2
wipe3 =~ 1*in_eff_w3
wca1 =~ 1*c_act_w1
wca2 =~ 1*c_act_w2
wca3 =~ 1*c_act_w3
# Estimate lagged effects between within-person centered variables
wipe2 + wca2 ~ wipe1 + wca1
wipe3 + wca3 ~ wipe2 + wca2
# Estimate covariance between within-person centered variables at first wave
wipe1 ~~ wca1 # Covariance
# Estimate covariances between residuals of within-person centered variables
# (i.e., innovations)
wipe2 ~~ wca2
wipe3 ~~ wca3
# Estimate variance and covariance of random intercepts
RIipe ~~ RIipe
RIca ~~ RIca
RIipe ~~ RIca
# Estimate (residual) variance of within-person centered variables
wipe1 ~~ wipe1 # Variances
wca1 ~~ wca1
wipe2 ~~ wipe2 # Residual variances
wca2 ~~ wca2
wipe3 ~~ wipe3
wca3 ~~ wca3'
RICLPM7fit <- lavaan(RICLPM7,
data = ANES,
missing = 'ML',
estimator = "ML",
meanstructure = T,
int.ov.free = T)
summary(RICLPM7fit, fit.measures = T, standardized = T, rsquare = T)## lavaan 0.6-19 ended normally after 74 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 26
##
## Number of observations 2171
## Number of missing patterns 13
##
## Model Test User Model:
##
## Test statistic 2.302
## Degrees of freedom 1
## P-value (Chi-square) 0.129
##
## Model Test Baseline Model:
##
## Test statistic 3093.125
## Degrees of freedom 15
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 0.994
##
## Robust Comparative Fit Index (CFI) 1.000
## Robust Tucker-Lewis Index (TLI) 0.994
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -5707.744
## Loglikelihood unrestricted model (H1) -5706.593
##
## Akaike (AIC) 11467.488
## Bayesian (BIC) 11615.245
## Sample-size adjusted Bayesian (SABIC) 11532.639
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.024
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.068
## P-value H_0: RMSEA <= 0.050 0.792
## P-value H_0: RMSEA >= 0.080 0.014
##
## Robust RMSEA 0.025
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.069
## P-value H_0: Robust RMSEA <= 0.050 0.776
## P-value H_0: Robust RMSEA >= 0.080 0.017
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.006
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIipe =~
## in_eff_w1 1.000 0.555 0.696
## in_eff_w2 1.000 0.555 0.675
## in_eff_w3 1.000 0.555 0.679
## RIca =~
## c_act_w1 1.000 0.120 0.465
## c_act_w2 1.000 0.120 0.488
## c_act_w3 1.000 0.120 0.670
## wipe1 =~
## in_eff_w1 1.000 0.572 0.718
## wipe2 =~
## in_eff_w2 1.000 0.607 0.738
## wipe3 =~
## in_eff_w3 1.000 0.600 0.734
## wca1 =~
## c_act_w1 1.000 0.227 0.885
## wca2 =~
## c_act_w2 1.000 0.214 0.873
## wca3 =~
## c_act_w3 1.000 0.132 0.742
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wipe2 ~
## wipe1 0.159 0.051 3.088 0.002 0.149 0.149
## wca1 0.277 0.084 3.287 0.001 0.104 0.104
## wca2 ~
## wipe1 0.053 0.012 4.304 0.000 0.141 0.141
## wca1 0.256 0.027 9.306 0.000 0.272 0.272
## wipe3 ~
## wipe2 0.280 0.037 7.534 0.000 0.283 0.283
## wca2 0.414 0.088 4.697 0.000 0.147 0.147
## wca3 ~
## wipe2 0.008 0.009 0.838 0.402 0.035 0.035
## wca2 0.042 0.032 1.332 0.183 0.069 0.069
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## wipe1 ~~
## wca1 0.023 0.004 5.385 0.000 0.179 0.179
## .wipe2 ~~
## .wca2 0.020 0.004 5.112 0.000 0.168 0.168
## .wipe3 ~~
## .wca3 0.007 0.003 2.757 0.006 0.097 0.097
## RIipe ~~
## RIca 0.019 0.003 5.620 0.000 0.287 0.287
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .in_eff_w1 3.221 0.017 187.844 0.000 3.221 4.038
## .in_eff_w2 3.378 0.018 191.006 0.000 3.378 4.104
## .in_eff_w3 3.351 0.018 187.966 0.000 3.351 4.096
## .c_act_w1 0.213 0.006 38.616 0.000 0.213 0.830
## .c_act_w2 0.171 0.005 32.616 0.000 0.171 0.700
## .c_act_w3 0.081 0.004 20.756 0.000 0.081 0.454
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## RIipe 0.309 0.020 15.811 0.000 1.000 1.000
## RIca 0.014 0.001 10.235 0.000 1.000 1.000
## wipe1 0.328 0.019 16.999 0.000 1.000 1.000
## wca1 0.052 0.002 26.527 0.000 1.000 1.000
## .wipe2 0.355 0.020 17.552 0.000 0.961 0.961
## .wca2 0.041 0.002 24.835 0.000 0.893 0.893
## .wipe3 0.317 0.013 24.454 0.000 0.880 0.880
## .wca3 0.017 0.001 13.216 0.000 0.993 0.993
## .in_eff_w1 0.000 0.000 0.000
## .in_eff_w2 0.000 0.000 0.000
## .in_eff_w3 0.000 0.000 0.000
## .c_act_w1 0.000 0.000 0.000
## .c_act_w2 0.000 0.000 0.000
## .c_act_w3 0.000 0.000 0.000
##
## R-Square:
## Estimate
## wipe2 0.039
## wca2 0.107
## wipe3 0.120
## wca3 0.007
## in_eff_w1 1.000
## in_eff_w2 1.000
## in_eff_w3 1.000
## c_act_w1 1.000
## c_act_w2 1.000
## c_act_w3 1.000