library(lavaan)
## This is lavaan 0.6-17
## lavaan is FREE software! Please report any bugs.
Built data
set.seed(123)
X<-rnorm(100)
M<-0.6*X + rnorm(100)
Y<-0.8*M+rnorm(100)
data<-data.frame(X,M,Y)
head(data)
## X M Y
## 1 -0.56047565 -1.0466920 1.3614568
## 2 -0.23017749 0.1187772 1.4074347
## 3 1.55870831 0.6885331 0.2856814
## 4 0.07050839 -0.3052376 0.2990040
## 5 0.12928774 -0.8740459 -1.1135767
## 6 1.71506499 0.9840113 0.3109621
model
model<-"#Direct effect of X on Y
Y~X_to_Y*X
#effect of X on M
M~X_to_M*X
#effect of M on Y
Y~M_to_Y*M
# LABEL EFFECTS
Direct:=X_to_Y
Indirect:=X_to_M*M_to_Y
Total effect :=Direct+Indirect
Prop_mediated:=Direct/Indirect"
fit the model
fit<-sem(model=model,data=data,se="bootstrap")
summary of the model
summary(fit)
## lavaan 0.6.17 ended normally after 1 iteration
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 5
##
## Number of observations 100
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## 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|)
## Y ~
## X (X__Y) -0.147 0.124 -1.187 0.235
## M ~
## X (X__M) 0.548 0.105 5.223 0.000
## Y ~
## M (M__Y) 0.824 0.103 7.990 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .Y 0.878 0.112 7.826 0.000
## .M 0.923 0.146 6.321 0.000
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|)
## Direct -0.147 0.124 -1.187 0.235
## Indirect 0.451 0.099 4.573 0.000
## Totaleffect 0.304 0.157 1.931 0.053
## Prop_mediated -0.327 0.321 -1.018 0.309
parameter estimates(i.e confidence interval e.t.c)
parameterEstimates(fit)
## lhs op rhs label est se z pvalue
## 1 Y ~ X X_to_Y -0.147 0.124 -1.187 0.235
## 2 M ~ X X_to_M 0.548 0.105 5.223 0.000
## 3 Y ~ M M_to_Y 0.824 0.103 7.990 0.000
## 4 Y ~~ Y 0.878 0.112 7.826 0.000
## 5 M ~~ M 0.923 0.146 6.321 0.000
## 6 X ~~ X 0.825 0.000 NA NA
## 7 Direct := X_to_Y Direct -0.147 0.124 -1.187 0.235
## 8 Indirect := X_to_M*M_to_Y Indirect 0.451 0.099 4.573 0.000
## 9 Totaleffect := Direct+Indirect Totaleffect 0.304 0.157 1.931 0.053
## 10 Prop_mediated := Direct/Indirect Prop_mediated -0.327 0.321 -1.018 0.309
## ci.lower ci.upper
## 1 -0.386 0.113
## 2 0.330 0.733
## 3 0.620 1.026
## 4 0.641 1.072
## 5 0.631 1.211
## 6 0.825 0.825
## 7 -0.386 0.113
## 8 0.259 0.642
## 9 -0.006 0.613
## 10 -1.015 0.267
#summary$paths[Direct] *
#summary$paths[] *
#summary$paths[]