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Mathematic operators for 'indirect'-class object, the output of indirect_effect() and cond_indirect().

Usage

# S3 method for indirect
+(e1, e2)

# S3 method for indirect
-(e1, e2)

Arguments

e1

An 'indirect'-class object.

e2

An 'indirect'-class object.

Value

An 'indirect'-class object with a list of effects stored. See indirect_effect() on details for this class.

Details

For now, only + operator and - operator are supported. These operators can be used to estimate and test a function of effects between the same pair of variables.

For example, they can be used to compute and test the total effects along different paths. They can also be used to compute and test the difference between the effects along two paths.

The operators will check whether an operation is valid. An operation is not valid if

  1. the two paths do not start from the same variable,

  2. the two paths do not end at the same variable,

  3. moderators are involved but they are not set to the same values in both objects, and

  4. bootstrap estimates stored in boot_out, if any, are not identical.

  5. Monte Carlo simulated estimates stored in mc_out, if any, are not identical.

Multigroup Models

Since Version 0.1.14.2, support for multigroup models has been added for models fitted by lavaan. Both bootstrapping and Monte Carlo confidence intervals are supported. These operators can be used to compute and test the difference of an indirect effect between two groups. This can also be used to compute and test the difference between a function of effects between groups, for example, the total indirect effects between two groups.

The operators are flexible and allow users to do many possible computations. Therefore, users need to make sure that the function of effects is meaningful.

Examples

library(lavaan)
dat <- modmed_x1m3w4y1
mod <-
"
m1 ~ a1 * x  + d1 * w1 + e1 * x:w1
m2 ~ m1 + a2 * x
y  ~ b1 * m1 + b2 * m2 + cp * x
"
fit <- sem(mod, dat,
           meanstructure = TRUE, fixed.x = FALSE,
           se = "none", baseline = FALSE)
est <- parameterEstimates(fit)
hi_w1 <- mean(dat$w1) + sd(dat$w1)

# Examples for cond_indirect():

# Conditional effect from x to m1 when w1 is 1 SD above mean
out1 <- cond_indirect(x = "x", y = "y", m = c("m1", "m2"),
              wvalues = c(w1 = hi_w1), fit = fit)
out2 <- cond_indirect(x = "x", y = "y", m = c("m2"),
              wvalues = c(w1 = hi_w1), fit = fit)
out3 <- cond_indirect(x = "x", y = "y",
              wvalues = c(w1 = hi_w1), fit = fit)

out12 <- out1 + out2
out12
#> 
#> == Conditional Indirect Effect   ==
#>                                         
#>  Path:                x -> m1 -> m2 -> y
#>  Path:                x -> m2 -> y      
#>  Moderators:          w1                
#>  Function of Effects: 0.082             
#>  When:                w1 = 1.228        
#> 
#> Computation of the Function of Effects:
#>  (x->m1->m2->y)
#> +(x->m2->y) 
#> 
out123 <- out1 + out2 + out3
out123
#> 
#> == Conditional Indirect Effect   ==
#>                                         
#>  Path:                x -> m1 -> m2 -> y
#>  Path:                x -> m2 -> y      
#>  Path:                x -> y            
#>  Moderators:          w1                
#>  Function of Effects: 0.394             
#>  When:                w1 = 1.228        
#> 
#> Computation of the Function of Effects:
#>  ((x->m1->m2->y)
#> +(x->m2->y))
#> +(x->y) 
#> 
coef(out1) + coef(out2) + coef(out3)
#>     y~x 
#> 0.39405 

# Multigroup model with indirect effects

dat <- data_med_mg
mod <-
"
m ~ x + c1 + c2
y ~ m + x + c1 + c2
"
fit <- sem(mod, dat, meanstructure = TRUE, fixed.x = FALSE, se = "none", baseline = FALSE,
           group = "group")

# If a model has more than one group,
# the argument 'group' must be set.
ind1 <- indirect_effect(x = "x",
                        y = "y",
                        m = "m",
                        fit = fit,
                        group = "Group A")
ind1
#> 
#> == Indirect Effect  ==
#>                                          
#>  Path:            Group A[1]: x -> m -> y
#>  Indirect Effect: 0.409                  
#> 
#> Computation Formula:
#>   (b.m~x)*(b.y~m)
#> Computation:
#>   (0.87989)*(0.46481)
#> Coefficients of Component Paths:
#>  Path Coefficient
#>   m~x       0.880
#>   y~m       0.465
#> 
#> NOTE:
#> - The group label is printed before each path.
#> - The group number in square brackets is the number used internally in
#>   lavaan.
#> 
ind2 <- indirect_effect(x = "x",
                        y = "y",
                        m = "m",
                        fit = fit,
                        group = 2)
ind2
#> 
#> == Indirect Effect  ==
#>                                          
#>  Path:            Group B[2]: x -> m -> y
#>  Indirect Effect: 0.663                  
#> 
#> Computation Formula:
#>   (b.m~x)*(b.y~m)
#> Computation:
#>   (0.59716)*(1.11040)
#> Coefficients of Component Paths:
#>  Path Coefficient
#>   m~x       0.597
#>   y~m       1.110
#> 
#> NOTE:
#> - The group label is printed before each path.
#> - The group number in square brackets is the number used internally in
#>   lavaan.
#> 

# Compute the difference in indirect effects between groups
ind2 - ind1
#> 
#> == Indirect Effect  ==
#>                                              
#>  Path:                Group B[2]: x -> m -> y
#>  Path:                Group A[1]: x -> m -> y
#>  Function of Effects: 0.254                  
#> 
#> Computation of the Function of Effects:
#>  (Group B[2]: x->m->y)
#> -(Group A[1]: x->m->y) 
#> 
#> NOTE:
#> - The group label is printed before each path.
#> - The group number in square brackets is the number used internally in
#>   lavaan.
#>