Methods for rmedsem Objects

Print, summarize and extract the results of rmedsem().

Usage

# S3 method for class 'rmedsem'
summary(object, ...)

# S3 method for class 'summary.rmedsem'
print(x, digits = 3, ...)

# S3 method for class 'rmedsem'
coef(object, method = NULL, ...)

# S3 method for class 'rmedsem'
confint(object, parm, level = NULL, method = NULL, ...)

# S3 method for class 'rmedsem'
nobs(object, ...)

# S3 method for class 'rmedsem'
as.data.frame(x, ...)

# S3 method for class 'rmedsem'
print(x, digits = 3, indent = 3, ...)

# S3 method for class 'rmedsem_blavaan'
print(x, digits = 3, indent = 3, ...)

# S3 method for class 'rmedsem_modsem'
print(x, digits = 3, indent = 3, ci_moderation = FALSE, ...)

Arguments

object: an rmedsem object
...: additional arguments (currently unused)
x: an rmedsem object; for print.summary.rmedsem() a summary.rmedsem object
digits: an integer, the number of decimal places to print
method: estimation method for the indirect effect, one of object$est.methods (e.g., "sobel", "delta", "montc", "boot" or "bayes"); see section 'Extracting results' for the default
parm: character vector; a subset of c("indirect", "direct", "total")
level: the confidence level. The intervals are computed by rmedsem() (argument ci.two.tailed), so level can only be used to check that the stored intervals have the requested level.
indent: an integer, the number of spaces to indent
ci_moderation: a logical, whether to print confidence intervals for the moderation effects (moderated mediation with modsem only)

Value

print() returns x invisibly.

summary() returns an object of class summary.rmedsem, a list with elements

package, vars, standardized, nobs, ci.level: copied from object.
ci.type: type of the intervals: "CI" or, for blavaan models fitted with hdi = TRUE, "HDI".
p.threshold: the p-value threshold (NULL for blavaan models).
effects: a data frame with columns effect, method, estimate, se, zval, pval, lower and upper; NA where a quantity is not available. For blavaan models, pval is the posterior probability of the opposite sign.
mediation: a list with elements bk and zlc giving the type of mediation (NULL if the approach was not requested). bk is one of "none", "complete" or "partial"; zlc is one of "indirect-only", "direct-only", "no-effect", "complementary" or "competitive".
zlc.method: the estimation method used for the Zhao, Lynch & Chen approach.
effect.size: a named numeric vector with the requested effect sizes (RIT, RID, upsilon (adjusted) and upsilon.unadjusted).
effect.size.problems: a named character vector with an entry for each of RIT and RID that should not be interpreted (see effect-sizes), describing the reason; empty if there is none.

coef() returns a named numeric vector with elements indirect, direct and total.

confint() returns a matrix with one row per effect and columns giving the lower and upper limits, labelled by their probabilities (e.g., "2.5 %" and "97.5 %") or, for highest density intervals, "lower" and "upper".

nobs() returns an integer.

as.data.frame() returns a data frame with one row per estimation method of the indirect effect and columns package, method and the estimates stored for that method (coef, se, zval, pval, lower and upper; blavaan results additionally contain the posterior probabilities pvpos and pvneg and the evidence ratios ERpos and ERneg).

Printing and summarizing

print() gives a detailed, step-by-step description of the results: a table of the tests of the indirect effect (one column per estimation method), the steps and conclusions of the Baron and Kenny and/or Zhao, Lynch & Chen approaches, and the effect sizes. For blavaan models, the table reports posterior summaries instead; for moderated mediation with modsem, the moderation effects are printed in addition.

summary() collects the same results in compact form: a table of the indirect (for each estimation method), direct and total effects, the type of mediation, and the effect sizes.

Extracting results

coef() returns the estimated indirect, direct and total effects, confint() their confidence (or, for blavaan models, credible) intervals, and nobs() the number of observations. as.data.frame() returns the estimates of the indirect effect for all estimation methods.

The indirect effect is estimated with several methods (see object$est.methods), which all give the same point estimate but different standard errors and intervals. By default, coef() and confint() use the method that also underlies the Zhao, Lynch & Chen approach: "montc" (Monte-Carlo) for lavaan and modsem, "boot" (bootstrap) for cSEM and plssem, and "bayes" for blavaan.

Extending the printed output

print.rmedsem() handles all backends that provide the elements described in section 'Adding a backend' of rmedsem(). Backends that need a different output provide a method for their subclass, either replacing the default output (print.rmedsem_blavaan()) or extending it with NextMethod() (print.rmedsem_modsem()).

Examples

mod.txt <- "
read ~ math
science ~ read + math
"
mod <- lavaan::sem(mod.txt, data = rmedsem::hsbdemo)
out <- rmedsem(mod, indep = "math", med = "read", dep = "science")

# detailed output
print(out)
#> Significance testing of indirect effect (standardized)
#> Model estimated with package 'lavaan'
#> Mediation effect: 'math' -> 'read' -> 'science'
#> 
#>                          Sobel          Delta    Monte-Carlo
#> Indirect effect          0.251          0.251          0.251
#> Std. Err.                0.046          0.046          0.046
#> z-value                  5.501          5.446          5.446
#> p-value               3.79e-08       5.15e-08       5.16e-08
#> CI              [0.161, 0.340] [0.160, 0.341] [0.162, 0.343]
#> 
#> Baron and Kenny approach to testing mediation
#>    STEP 1 - 'math' -> 'read' (X -> M) with B=0.662 and p<0.001
#>    STEP 2 - 'read' -> 'science' (M -> Y) with B=0.378 and p<0.001
#>    STEP 3 - 'math' -> 'science' (X -> Y) with B=0.380 and p<0.001
#>             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<0.001
#>             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'
#>    Upsilon (v) = Variance in Y explained indirectly by X through M
#>          v(unadj) = 0.063, v(adj) = 0.061
#> 

# compact summary and its elements
s <- summary(out)
s
#> Mediation analysis: 'math' -> 'read' -> 'science'
#> Estimated with 'lavaan' (standardized), N = 200
#> 
#> Effects (95% CI):
#>                        Estimate Std. Err. z-value  p-value Lower Upper
#> Indirect (Sobel)          0.251     0.046   5.501 3.79e-08 0.161 0.340
#> Indirect (Delta)          0.251     0.046   5.446 5.15e-08 0.160 0.341
#> Indirect (Monte-Carlo)    0.251     0.046   5.446 5.16e-08 0.162 0.343
#> Direct                    0.380     0.065         4.43e-09 0.253 0.507
#> Total                     0.630     0.039                  0.553 0.706
#> 
#> Type of mediation (significant: p < 0.05):
#>   Baron & Kenny:      partial mediation
#>   Zhao, Lynch & Chen: complementary mediation (partial mediation)
#>                       (based on Monte-Carlo test)
#> 
#> Effect sizes:
#>   RIT = 0.397
#>   RID = 0.659
#>   Upsilon = 0.061
#>   Upsilon (unadj.) = 0.063
#> 
s$mediation
#> $bk
#> [1] "partial"
#> 
#> $zlc
#> [1] "complementary"
#> 
s$effects
#>     effect method  estimate         se     zval         pval     lower
#> 1 indirect  sobel 0.2506159 0.04556196 5.500552 3.786031e-08 0.1613161
#> 2 indirect  delta 0.2506159 0.04601847 5.445986 5.151917e-08 0.1604214
#> 3 indirect  montc 0.2506159 0.04598692 5.445778 5.157931e-08 0.1615148
#> 4   direct   <NA> 0.3801172 0.06478771       NA 4.434311e-09 0.2531357
#> 5    total   <NA> 0.6303867 0.03883725       NA           NA 0.5531633
#>       upper
#> 1 0.3399157
#> 2 0.3408105
#> 3 0.3425981
#> 4 0.5070988
#> 5 0.7059270

# extract estimates
coef(out)
#>  indirect    direct     total 
#> 0.2506159 0.3801172 0.6303867 
confint(out)
#>              2.5 %    97.5 %
#> indirect 0.1615148 0.3425981
#> direct   0.2531357 0.5070988
#> total    0.5531633 0.7059270
confint(out, parm = "indirect", method = "sobel")
#>              2.5 %    97.5 %
#> indirect 0.1613161 0.3399157
nobs(out)
#> [1] 200
as.data.frame(out)
#>   package method      coef         se     zval         pval     lower     upper
#> 1  lavaan  sobel 0.2506159 0.04556196 5.500552 3.786031e-08 0.1613161 0.3399157
#> 2  lavaan  delta 0.2506159 0.04601847 5.445986 5.151917e-08 0.1604214 0.3408105
#> 3  lavaan  montc 0.2506159 0.04598692 5.445778 5.157931e-08 0.1615148 0.3425981