MVPA-Mediation Example
library(lavaan)
setwd(mypath) # change it to the path of your own data folder
mediation.data <- read.table("mvpa_mediation_data.csv", sep = ",", header = F)
colnames(mediation.data) <- paste0("V", 1:4)
mediation.model <- '
V3 ~ a*V1
V2 ~ b*V1
V4 ~ c*V1 + d*V2 + e*V3
V2 ~~ V3
# Indirect Effects
ae := a*e
bd := b*d
# Total Effect
t := a*e + b*d + c
'
mediation.fit <- sem(mediation.model, data = mediation.data, se = "bootstrap")library(lavaan)
summary(mediation.fit, fit.measures = T, standardized = F)## lavaan 0.6.9 ended normally after 48 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 589.591
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8059.340
## Loglikelihood unrestricted model (H1) -8059.340
##
## Akaike (AIC) 16136.680
## Bayesian (BIC) 16180.849
## Sample-size adjusted Bayesian (SABIC) 16152.265
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## V3 ~
## V1 (a) -0.158 0.029 -5.531 0.000
## V2 ~
## V1 (b) 0.149 0.027 5.478 0.000
## V4 ~
## V1 (c) 13.457 0.666 20.203 0.000
## V2 (d) 8.804 0.727 12.106 0.000
## V3 (e) 0.046 0.718 0.065 0.948
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .V3 ~~
## .V2 0.244 0.049 4.998 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .V3 1.573 0.059 26.629 0.000
## .V2 1.583 0.058 27.135 0.000
## .V4 826.683 35.926 23.010 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## ae -0.007 0.116 -0.063 0.950
## bd 1.308 0.262 4.995 0.000
## t 14.758 0.685 21.554 0.000summary(mediation.fit, fit.measures = T, standardized = T)## lavaan 0.6.9 ended normally after 48 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 9
##
## Number of observations 1000
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 589.591
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -8059.340
## Loglikelihood unrestricted model (H1) -8059.340
##
## Akaike (AIC) 16136.680
## Bayesian (BIC) 16180.849
## Sample-size adjusted Bayesian (SABIC) 16152.265
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value H_0: RMSEA <= 0.050 NA
## P-value H_0: RMSEA >= 0.080 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Bootstrap
## Number of requested bootstrap draws 1000
## Number of successful bootstrap draws 1000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## V3 ~
## V1 (a) -0.158 0.029 -5.531 0.000 -0.158 -0.173
## V2 ~
## V1 (b) 0.149 0.027 5.478 0.000 0.149 0.163
## V4 ~
## V1 (c) 13.457 0.666 20.203 0.000 13.457 0.507
## V2 (d) 8.804 0.727 12.106 0.000 8.804 0.303
## V3 (e) 0.046 0.718 0.065 0.948 0.046 0.002
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V3 ~~
## .V2 0.244 0.049 4.998 0.000 0.244 0.155
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .V3 1.573 0.059 26.629 0.000 1.573 0.970
## .V2 1.583 0.058 27.135 0.000 1.583 0.974
## .V4 826.683 35.926 23.010 0.000 826.683 0.602
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## ae -0.007 0.116 -0.063 0.950 -0.007 -0.000
## bd 1.308 0.262 4.995 0.000 1.308 0.049
## t 14.758 0.685 21.554 0.000 14.758 0.556© Copyright 2024 @Yi Feng and @Gregory R. Hancock.