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The currently supported estimation methods are:

  • covariance-based SEM estimated with the lavaan package
  • PLS-SEM estimated using cSEM
  • Bayesian SEM estimated using blavaan

Lavaan

Example 1 (lavaan)

library(lavaan)
library(rmedsem)

mod.txt <- "
read ~ math
science ~ read + math
"
mod <- lavaan::sem(mod.txt, data=rmedsem::hsbdemo)
out <- rmedsem(mod, indep="math", med="read", dep="science", 
               standardized=T, mcreps=5000,
               approach = c("bk","zlc"))
print(out)
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'math' -> 'read' -> 'science'
#> 
#>                         Sobel         Delta   Monte-Carlo
#> Indirect effect        0.2506        0.2506        0.2506
#> Std. Err.              0.0456        0.0456        0.0449
#> z-value                5.5006        5.5006        5.5701
#> p-value              3.79e-08      3.79e-08      2.55e-08
#> CI              [0.161, 0.34] [0.161, 0.34] [0.164, 0.34]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'math:read' (X -> M) with B=0.662 and p=0.000
#>    STEP 2 - 'read:science' (M -> Y) with B=0.378 and p=0.000
#>    STEP 3 - 'math:science' (X -> Y) with B=0.380 and p=0.000
#>             As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#>             are significant the mediation is partial.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#>   STEP 1 - 'math:science' (X -> Y) with B=0.380 and p=0.000
#>             As the Monte-Carlo test above is significant, STEP 1 is
#>             significant and their coefficients point in same direction,
#>             there is complementary mediation (partial mediation).
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.251/0.631) = 0.397
#>          Meaning that about  40% of the effect of 'math'
#>          on 'science' is mediated by 'read'
#>    RID = (Indirect effect / Direct effect)
#>          (0.251/0.380) = 0.659
#>          That is, the mediated effect is about 0.7 times as
#>          large as the direct effect of 'math' on 'science'

Example 2 (lavaan)

model02 <- "
  # measurement model
    ind60 =~ x1 + x2 + x3
    dem60 =~ y1 + y2 + y3 + y4
    dem65 =~ y5 + y6 + y7 + y8
  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60
"
mod <- sem(model02, data=lavaan::PoliticalDemocracy)
out <- rmedsem(mod, indep="ind60", med="dem60", dep="dem65",
               standardized=T, mcreps=5000,
               approach = c("bk","zlc"))
print(out)
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'ind60' -> 'dem60' -> 'dem65'
#> 
#>                          Sobel          Delta  Monte-Carlo
#> Indirect effect         0.4091          0.409       0.4091
#> Std. Err.               0.0956          0.116       0.0959
#> z-value                 4.2817          3.524       4.2465
#> p-value               1.85e-05       0.000425     2.17e-05
#> CI              [0.222, 0.596] [0.182, 0.637] [0.226, 0.6]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'ind60:dem60' (X -> M) with B=0.448 and p=0.000
#>    STEP 2 - 'dem60:dem65' (M -> Y) with B=0.913 and p=0.000
#>    STEP 3 - 'ind60:dem65' (X -> Y) with B=0.146 and p=0.038
#>             As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#>             are significant the mediation is partial.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#>   STEP 1 - 'ind60:dem65' (X -> Y) with B=0.146 and p=0.038
#>             As the Monte-Carlo test above is significant, STEP 1 is
#>             significant and their coefficients point in same direction,
#>             there is complementary mediation (partial mediation).
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.409/0.555) = 0.738
#>          Meaning that about  74% of the effect of 'ind60'
#>          on 'dem65' is mediated by 'dem60'
#>    RID = (Indirect effect / Direct effect)
#>          (0.409/0.146) = 2.811
#>          That is, the mediated effect is about 2.8 times as
#>          large as the direct effect of 'ind60' on 'dem65'

Example 3 (lavaan)

model03 <- "
   Attractive =~ face + sexy
   Appearance =~ body + appear + attract
   Muscle =~ muscle + strength + endur
   Weight =~ lweight + calories + cweight
   Appearance ~ Attractive + age
   Muscle ~ Appearance + Attractive + age
   Weight ~ Appearance + Attractive + age
"
mod <- sem(model03, data=rmedsem::workout)
rmedsem(mod, indep="Attractive", med="Appearance", dep="Muscle",
       standardized=T, mcreps=5000,
       approach = c("bk","zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'Attractive' -> 'Appearance' -> 'Muscle'
#> 
#>                            Sobel              Delta      Monte-Carlo
#> Indirect effect           0.0654             0.0654           0.0654
#> Std. Err.                 0.0331             0.0338           0.0335
#> z-value                   1.9748             1.9359           1.9641
#> p-value                   0.0483             0.0529           0.0495
#> CI              [0.000491, 0.13] [-0.000814, 0.132] [0.00453, 0.136]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'Attractive:Appearance' (X -> M) with B=0.158 and p=0.033
#>    STEP 2 - 'Appearance:Muscle' (M -> Y) with B=0.414 and p=0.000
#>    STEP 3 - 'Attractive:Muscle' (X -> Y) with B=-0.014 and p=0.850
#>             As STEP 1, STEP 2 and the Sobel's test above are significant
#>             and STEP 3 is not significant the mediation is complete.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#>   STEP 1 - 'Attractive:Muscle' (X -> Y) with B=-0.014 and p=0.850
#>             As the Monte-Carlo test above is significant and STEP 1 is not
#>             significant there indirect-only mediation (full mediation).
#> 
#> Effect sizes
#>    WARNING: Total effect is smaller than indirect effect!
#>             Effect sizes should not be interpreted.
#>    RIT = (Indirect effect / Total effect)
#>          Total effect 0.052 is too small to calculate RIT
#>    RID = (Indirect effect / Direct effect)
#>          (0.065/0.014) = 4.714
#>          That is, the mediated effect is about 4.7 times as
#>          large as the direct effect of 'Attractive' on 'Muscle'
rmedsem(mod, indep="Attractive", med="Appearance", dep="Weight",
        standardized=T, mcreps=5000,
        approach = c("bk","zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'Attractive' -> 'Appearance' -> 'Weight'
#> 
#>                           Sobel            Delta      Monte-Carlo
#> Indirect effect          0.0979           0.0979           0.0979
#> Std. Err.                0.0470           0.0484           0.0472
#> z-value                  2.0810           2.0228           2.0716
#> p-value                  0.0374           0.0431           0.0383
#> CI              [0.00569, 0.19] [0.00304, 0.193] [0.00662, 0.192]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'Attractive:Appearance' (X -> M) with B=0.158 and p=0.033
#>    STEP 2 - 'Appearance:Weight' (M -> Y) with B=0.619 and p=0.000
#>    STEP 3 - 'Attractive:Weight' (X -> Y) with B=-0.125 and p=0.073
#>             As STEP 1, STEP 2 and the Sobel's test above are significant
#>             and STEP 3 is not significant the mediation is complete.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#>   STEP 1 - 'Attractive:Weight' (X -> Y) with B=-0.125 and p=0.073
#>             As the Monte-Carlo test above is significant and STEP 1 is not
#>             significant there indirect-only mediation (full mediation).
#> 
#> Effect sizes
#>    WARNING: Total effect is smaller than indirect effect!
#>             Effect sizes should not be interpreted.
#>    RIT = (Indirect effect / Total effect)
#>          Total effect 0.027 is too small to calculate RIT
#>    RID = (Indirect effect / Direct effect)
#>          (0.098/0.125) = 0.784
#>          That is, the mediated effect is about 0.8 times as
#>          large as the direct effect of 'Attractive' on 'Weight'
rmedsem(mod, indep="age", med="Appearance", dep="Muscle",
        standardized=T, mcreps=5000,
        approach = c("bk","zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'age' -> 'Appearance' -> 'Muscle'
#> 
#>                             Sobel             Delta       Monte-Carlo
#> Indirect effect           -0.1602           -0.1602           -0.1602
#> Std. Err.                  0.0397            0.0395            0.0395
#> z-value                   -4.0391           -4.0579           -4.0487
#> p-value                  5.37e-05          4.95e-05          5.15e-05
#> CI              [-0.238, -0.0825] [-0.238, -0.0828] [-0.243, -0.0886]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'age:Appearance' (X -> M) with B=-0.387 and p=0.000
#>    STEP 2 - 'Appearance:Muscle' (M -> Y) with B=0.414 and p=0.000
#>    STEP 3 - 'age:Muscle' (X -> Y) with B=-0.147 and p=0.065
#>             As STEP 1, STEP 2 and the Sobel's test above are significant
#>             and STEP 3 is not significant the mediation is complete.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#>   STEP 1 - 'age:Muscle' (X -> Y) with B=-0.147 and p=0.065
#>             As the Monte-Carlo test above is significant and STEP 1 is not
#>             significant there indirect-only mediation (full mediation).
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.160/0.307) = 0.521
#>          Meaning that about  52% of the effect of 'age'
#>          on 'Muscle' is mediated by 'Appearance'
#>    RID = (Indirect effect / Direct effect)
#>          (0.160/0.147) = 1.089
#>          That is, the mediated effect is about 1.1 times as
#>          large as the direct effect of 'age' on 'Muscle'
rmedsem(mod, indep="age", med="Appearance", dep="Weight",
        standardized=T, mcreps=5000,
        approach = c("bk","zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'age' -> 'Appearance' -> 'Weight'
#> 
#>                            Sobel            Delta      Monte-Carlo
#> Indirect effect          -0.2397          -0.2397          -0.2397
#> Std. Err.                 0.0453           0.0449           0.0452
#> z-value                  -5.2867          -5.3398          -5.3107
#> p-value                 1.25e-07         9.31e-08         1.09e-07
#> CI              [-0.329, -0.151] [-0.328, -0.152] [-0.334, -0.156]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'age:Appearance' (X -> M) with B=-0.387 and p=0.000
#>    STEP 2 - 'Appearance:Weight' (M -> Y) with B=0.619 and p=0.000
#>    STEP 3 - 'age:Weight' (X -> Y) with B=0.341 and p=0.000
#>             As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#>             are significant the mediation is partial.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#>   STEP 1 - 'age:Weight' (X -> Y) with B=0.341 and p=0.000
#>             As the Monte-Carlo test above is significant, STEP 1 is
#>             significant and their coefficients point in opposite
#>             direction, there is competitive mediation (partial mediation).
#> 
#> Effect sizes
#>    WARNING: Total effect is smaller than indirect effect!
#>             Effect sizes should not be interpreted.
#>    RIT = (Indirect effect / Total effect)
#>          Total effect 0.101 is too small to calculate RIT
#>    RID = (Indirect effect / Direct effect)
#>          (0.240/0.341) = 0.704
#>          That is, the mediated effect is about 0.7 times as
#>          large as the direct effect of 'age' on 'Weight'

cSEM

Example 1 (cSEM)

library(cSEM)
library(rmedsem)

mod.txt <- "
# need to use single-item measurement models for PLS-SEM
Read =~ read
Math =~ math
Science =~ science

# the actual path model
Read ~ Math
Science ~ Read + Math
"
mod <- cSEM::csem(.model=mod.txt, .data=rmedsem::hsbdemo,
                 .resample_method = "bootstrap", .R = 200)
rmedsem(mod, indep="Math", med="Read", dep="Science", 
        approach = c("bk", "zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'cSEM'
#> Mediation effect: 'Math' -> 'Read' -> 'Science'
#> 
#>                          Sobel          Delta      Bootstrap
#> Indirect effect         0.2506         0.2506         0.2506
#> Std. Err.               0.0528         0.0544         0.0515
#> z-value                 4.7464         4.6082         4.8690
#> p-value               2.07e-06       4.06e-06       1.12e-06
#> CI              [0.147, 0.354] [0.144, 0.357] [0.152, 0.348]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'Math:Read' (X -> M) with B=0.662 and p=0.000
#>    STEP 2 - 'Read:Science' (M -> Y) with B=0.378 and p=0.000
#>    STEP 3 - 'Math:Science' (X -> Y) with B=0.380 and p=0.000
#>             As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#>             are significant the mediation is partial.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Bootstrap
#>   STEP 1 - 'Math:Science' (X -> Y) with B=0.380 and p=0.000
#>             As the Bootstrap test above is significant, STEP 1 is
#>             significant and their coefficients point in same direction,
#>             there is complementary mediation (partial mediation).
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.251/0.631) = 0.397
#>          Meaning that about  40% of the effect of 'Math'
#>          on 'Science' is mediated by 'Read'
#>    RID = (Indirect effect / Direct effect)
#>          (0.251/0.380) = 0.659
#>          That is, the mediated effect is about 0.7 times as
#>          large as the direct effect of 'Math' on 'Science'

Example 2 (cSEM)

model02 <- "
  # measurement model
    ind60 =~ x1 + x2 + x3
    dem60 =~ y1 + y2 + y3 + y4
    dem65 =~ y5 + y6 + y7 + y8
  # regressions
    dem60 ~ ind60
    dem65 ~ ind60 + dem60
"
mod <- cSEM::csem(.model=model02, .data=lavaan::PoliticalDemocracy,
                  .resample_method = "bootstrap", .R = 200)
rmedsem(mod, indep="ind60", med="dem60", dep="dem65",
        approach = c("bk","zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'cSEM'
#> Mediation effect: 'ind60' -> 'dem60' -> 'dem65'
#> 
#>                          Sobel          Delta     Bootstrap
#> Indirect effect         0.3988         0.3988        0.3988
#> Std. Err.               0.0879         0.0938        0.0785
#> z-value                 4.5384         4.2498        5.0797
#> p-value               5.67e-06       2.14e-05      3.78e-07
#> CI              [0.227, 0.571] [0.215, 0.583] [0.258, 0.54]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'ind60:dem60' (X -> M) with B=0.439 and p=0.000
#>    STEP 2 - 'dem60:dem65' (M -> Y) with B=0.909 and p=0.000
#>    STEP 3 - 'ind60:dem65' (X -> Y) with B=0.159 and p=0.021
#>             As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#>             are significant the mediation is partial.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Bootstrap
#>   STEP 1 - 'ind60:dem65' (X -> Y) with B=0.159 and p=0.021
#>             As the Bootstrap test above is significant, STEP 1 is
#>             significant and their coefficients point in same direction,
#>             there is complementary mediation (partial mediation).
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.399/0.557) = 0.715
#>          Meaning that about  72% of the effect of 'ind60'
#>          on 'dem65' is mediated by 'dem60'
#>    RID = (Indirect effect / Direct effect)
#>          (0.399/0.159) = 2.514
#>          That is, the mediated effect is about 2.5 times as
#>          large as the direct effect of 'ind60' on 'dem65'

Example 3 (cSEM)

model03 <- "
   Attractive =~ face + sexy
   Appearance =~ body + appear + attract
   Muscle =~ muscle + strength + endur
   Weight =~ lweight + calories + cweight
   Age =~ age ## need single-indicator LV for cSEM
   Appearance ~ Attractive + Age
   Muscle ~ Appearance + Attractive + Age
   Weight ~ Appearance + Attractive + Age
"
mod <- cSEM::csem(.model=model03, .data=na.omit(rmedsem::workout), 
                  .resample_method = "bootstrap", .R = 200)
rmedsem(mod, indep="Attractive", med="Appearance", dep="Muscle",
        approach = c("bk","zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'cSEM'
#> Mediation effect: 'Attractive' -> 'Appearance' -> 'Muscle'
#> 
#>                           Sobel           Delta        Bootstrap
#> Indirect effect          0.1124          0.1124           0.1124
#> Std. Err.                0.0387          0.0404           0.0388
#> z-value                  2.9041          2.7801           2.8984
#> p-value                 0.00368         0.00543          0.00375
#> CI              [0.0365, 0.188] [0.0331, 0.192] [0.0512, -0.282]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'Attractive:Appearance' (X -> M) with B=0.236 and p=0.001
#>    STEP 2 - 'Appearance:Muscle' (M -> Y) with B=0.475 and p=0.000
#>    STEP 3 - 'Attractive:Muscle' (X -> Y) with B=-0.010 and p=0.902
#>             As STEP 1, STEP 2 and the Sobel's test above are significant
#>             and STEP 3 is not significant the mediation is complete.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Bootstrap
#>   STEP 1 - 'Attractive:Muscle' (X -> Y) with B=-0.010 and p=0.902
#>             As the Bootstrap test above is significant and STEP 1 is not
#>             significant there indirect-only mediation (full mediation).
#> 
#> Effect sizes
#>    WARNING: Total effect is smaller than indirect effect!
#>             Effect sizes should not be interpreted.
#>    RIT = (Indirect effect / Total effect)
#>          Total effect 0.102 is too small to calculate RIT
#>    RID = (Indirect effect / Direct effect)
#>          (0.112/0.010) = 10.768
#>          That is, the mediated effect is about 10.8 times as
#>          large as the direct effect of 'Attractive' on 'Muscle'

blavaan

Example 1 (blavaan)

library(blavaan)
library(rmedsem)

mod.txt <- "
read ~ math
science ~ read + math
"
mod <- bsem(mod.txt, data=rmedsem::hsbdemo, 
            n.chains=3, burnin=500, sample=500, 
            bcontrol = list(cores = 3))
#> Computing post-estimation metrics (including lvs if requested)...
rmedsem(mod, indep="math", med="read", dep="science", 
        approach = c("bk","zlc"))
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'blavaan'
#> Mediation effect: 'math' -> 'read' -> 'science'
#> 
#> Prior (regression coefs): normal(0,10)
#>                      Bayes
#> Indirect effect     0.2477
#> Std. Err.           0.0455
#> z-value             5.4472
#> P(z>0)              0.0000
#> P(z<0)              1.0000
#> ER+                      0
#> ER-                      ∞
#> HDI             [0, 0.163]
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.248/0.628) = 0.394
#>          Meaning that about  39% of the effect of 'math'
#>          on 'science' is mediated by 'read'
#>    RID = (Indirect effect / Direct effect)
#>          (0.248/0.381) = 0.651
#>          That is, the mediated effect is about 0.7 times as
#>          large as the direct effect of 'math' on 'science'
print(out)
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'ind60' -> 'dem60' -> 'dem65'
#> 
#>                          Sobel          Delta  Monte-Carlo
#> Indirect effect         0.4091          0.409       0.4091
#> Std. Err.               0.0956          0.116       0.0959
#> z-value                 4.2817          3.524       4.2465
#> p-value               1.85e-05       0.000425     2.17e-05
#> CI              [0.222, 0.596] [0.182, 0.637] [0.226, 0.6]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'ind60:dem60' (X -> M) with B=0.448 and p=0.000
#>    STEP 2 - 'dem60:dem65' (M -> Y) with B=0.913 and p=0.000
#>    STEP 3 - 'ind60:dem65' (X -> Y) with B=0.146 and p=0.038
#>             As STEP 1, STEP 2 and STEP 3 as well as the Sobel's test above
#>             are significant the mediation is partial.
#> 
#> Zhao, Lynch & Chen's approach to testing mediation
#> Based on p-value estimated using Monte-Carlo
#>   STEP 1 - 'ind60:dem65' (X -> Y) with B=0.146 and p=0.038
#>             As the Monte-Carlo test above is significant, STEP 1 is
#>             significant and their coefficients point in same direction,
#>             there is complementary mediation (partial mediation).
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.409/0.555) = 0.738
#>          Meaning that about  74% of the effect of 'ind60'
#>          on 'dem65' is mediated by 'dem60'
#>    RID = (Indirect effect / Direct effect)
#>          (0.409/0.146) = 2.811
#>          That is, the mediated effect is about 2.8 times as
#>          large as the direct effect of 'ind60' on 'dem65'

Example 2 (blavaan)

model02 <- "
 # measurement model
   ind60 =~ x1 + x2 + x3
   dem60 =~ y1 + y2 + y3 + y4
   dem65 =~ y5 + y6 + y7 + y8
 # regressions
   dem60 ~ ind60
   dem65 ~ ind60 + dem60
"
mod <- bsem(model02, data=lavaan::PoliticalDemocracy, std.lv=T,
           meanstructure=T, n.chains=3,
           save.lvs=T, burnin=1000, sample=1000, bcontrol = list(cores = 3))
#> Computing post-estimation metrics (including lvs if requested)...
rmedsem(mod,  indep="ind60", med="dem60", dep="dem65")
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'blavaan'
#> Mediation effect: 'ind60' -> 'dem60' -> 'dem65'
#> 
#> Prior (regression coefs): normal(0,10)
#>                             Bayes
#> Indirect effect          0.386893
#> Std. Err.                0.096384
#> z-value                  4.014075
#> P(z>0)                   0.000333
#> P(z<0)                   0.999667
#> ER+                      3.33e-04
#> ER-                      3.00e+03
#> HDI             [0.000333, 0.192]
#> 
#> Effect sizes
#>    RIT = (Indirect effect / Total effect)
#>          (0.387/0.537) = 0.720
#>          Meaning that about  72% of the effect of 'ind60'
#>          on 'dem65' is mediated by 'dem60'
#>    RID = (Indirect effect / Direct effect)
#>          (0.387/0.151) = 2.571
#>          That is, the mediated effect is about 2.6 times as
#>          large as the direct effect of 'ind60' on 'dem65'