Print a 'cond_indirect_effects' Class Object

Print the content of the output of cond_indirect_effects()

Usage

# S3 method for class 'cond_indirect_effects'
print(
  x,
  digits = 3,
  annotation = TRUE,
  pvalue = NULL,
  pvalue_digits = 3,
  se = NULL,
  level = 0.95,
  se_ci = TRUE,
  ...
)

# S3 method for class 'cond_indirect_effects'
as.data.frame(
  x,
  row.names = NULL,
  optional = NULL,
  digits = 3,
  add_sig = TRUE,
  pvalue = NULL,
  pvalue_digits = 3,
  se = NULL,
  level = 0.95,
  se_ci = TRUE,
  to_string = FALSE,
  ...
)

Arguments

x: The output of cond_indirect_effects().
digits: Number of digits to display. Default is 3.
annotation: Logical. Whether the annotation after the table of effects is to be printed. Default is TRUE.
pvalue: Logical. If TRUE, asymmetric p-values based on bootstrapping will be printed if available. Default to FALSE if confidence intervals have already computed. Default to TRUE if no confidence intervals have been computed and the original standard errors are to be used. See Details on when the original standard errors will be used by default.
pvalue_digits: Number of decimal places to display for the p-values. Default is 3.
se: Logical. If TRUE and confidence intervals are available, the standard errors of the estimates are also printed. They are simply the standard deviations of the bootstrap estimates or Monte Carlo simulated values, depending on the method used to form the confidence intervals. Default to FALSE if confidence intervals are available. Default to TRUE if no confidence intervals have been computed and the original standard errors are to be used. See Details on when the original standard errors will be used by default.
level: The level of confidence for the confidence intervals computed from the original standard errors. Used only for paths without mediators and both x- and y-variables are not standardized.
se_ci: Logical. If TRUE and confidence interval has not been computed, the function will try to compute them from stored standard errors if the original standard errors are to be used. Ignored if confidence intervals have already been computed. Default to TRUE.
...: Other arguments. Not used.
row.names: Not used. Included to be compatible with the generic method.
optional: Not used. Included to be compatible with the generic method.
add_sig: Whether a column of significance test results will be added. Default is TRUE.
to_string: If TRUE, numeric columns will be converted to string columns, formatted based on digits and pvalue_digits. For printing. Default is FALSE.

Value

The print-method returns x invisibly. Called for its side effect.

The as.data.frame-method returns a data frame with the conditional effects and confidence intervals (if available), as well as other columns requested.

Details

The print method of the cond_indirect_effects-class object.

If bootstrapping confidence intervals were requested, this method has the option to print p-values computed by the method presented in Asparouhov and Muthén (2021). Note that these p-values are asymmetric bootstrap p-values based on the distribution of the bootstrap estimates. They not computed based on the distribution under the null hypothesis.

For a p-value of a, it means that a 100(1 - a)% bootstrapping confidence interval will have one of its limits equal to 0. A confidence interval with a higher confidence level will include zero, while a confidence interval with a lower confidence level will exclude zero.

Using Original Standard Errors

If these conditions are met, the stored standard errors, if available, will be used test an effect and form it confidence interval:

Confidence intervals have not been formed (e.g., by bootstrapping or Monte Carlo).
The path has no mediators.
The model has only one group.
The path is moderated by one or more moderator.
Both the x-variable and the y-variable are not standardized.

If the model is fitted by OLS regression (e.g., using stats::lm()), then the variance-covariance matrix of the coefficient estimates will be used, and the p-value and confidence intervals are computed from the t statistic.

If the model is fitted by structural equation modeling using lavaan, then the variance-covariance computed by lavaan will be used, and the p-value and confidence intervals are computed from the z statistic.

Caution

If the model is fitted by structural equation modeling and has moderators, the standard errors, p-values, and confidence interval computed from the variance-covariance matrices for conditional effects can only be trusted if all covariances involving the product terms are free. If any of them are fixed, for example, fixed to zero, it is possible that the model is not invariant to linear transformation of the variables.

The method as.data.frame() for cond_indirect_effects objects is used to convert this class of objects to data frames. Used internally by the print method but can also be used for getting a data frame with columns such as p-values and standard errors added.

Functions

as.data.frame(cond_indirect_effects): The as.data.frame-method for cond_indirect_effects objects. Used internally by the print-method but can also be used directly.

References

Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf

Examples


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

# Conditional effects from x to m1 when w1 is equal to each of the default levels
cond_indirect_effects(x = "x", y = "m1",
                      wlevels = "w1", fit = fit)
#> 
#> == Conditional effects ==
#> 
#>  Path: x -> m1
#>  Conditional on moderator(s): w1
#>  Moderator(s) represented by: w1
#> 
#>      [w1]   (w1)   ind
#> 1 M+1.0SD  1.228 0.750
#> 2 Mean     0.259 0.523
#> 3 M-1.0SD -0.710 0.297
#> 
#>  - The 'ind' column shows the conditional effects.
#>  

# Conditional Indirect effect from x1 through m1 to y,
# when w1 is equal to each of the default levels
out <- cond_indirect_effects(x = "x", y = "y", m = "m1",
                      wlevels = "w1", fit = fit)
out
#> 
#> == Conditional indirect effects ==
#> 
#>  Path: x -> m1 -> y
#>  Conditional on moderator(s): w1
#>  Moderator(s) represented by: w1
#> 
#>      [w1]   (w1)    ind  m1~x   y~m1
#> 1 M+1.0SD  1.228 -0.031 0.750 -0.042
#> 2 Mean     0.259 -0.022 0.523 -0.042
#> 3 M-1.0SD -0.710 -0.012 0.297 -0.042
#> 
#>  - The 'ind' column shows the conditional indirect effects.
#>  - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#>    conditional on the moderator(s).
#> 

print(out, digits = 5)
#> 
#> == Conditional indirect effects ==
#> 
#>  Path: x -> m1 -> y
#>  Conditional on moderator(s): w1
#>  Moderator(s) represented by: w1
#> 
#>      [w1]     (w1)      ind    m1~x     y~m1
#> 1 M+1.0SD  1.22806 -0.03147 0.74988 -0.04197
#> 2 Mean     0.25900 -0.02196 0.52332 -0.04197
#> 3 M-1.0SD -0.71006 -0.01246 0.29676 -0.04197
#> 
#>  - The 'ind' column shows the conditional indirect effects.
#>  - ‘m1~x’,‘y~m1’ is/are the path coefficient(s) along the path
#>    conditional on the moderator(s).
#> 

print(out, annotation = FALSE)
#> 
#> == Conditional indirect effects ==
#> 
#>  Path: x -> m1 -> y
#>  Conditional on moderator(s): w1
#>  Moderator(s) represented by: w1
#> 
#>      [w1]   (w1)    ind  m1~x   y~m1
#> 1 M+1.0SD  1.228 -0.031 0.750 -0.042
#> 2 Mean     0.259 -0.022 0.523 -0.042
#> 3 M-1.0SD -0.710 -0.012 0.297 -0.042



# Convert to data frames

as.data.frame(out)
#>      [w1]       (w1)         ind      m1~x        y~m1
#> 1 M+1.0SD  1.2280576 -0.03147387 0.7498826 -0.04197173
#> 2    Mean  0.2589999 -0.02196465 0.5233201 -0.04197173
#> 3 M-1.0SD -0.7100578 -0.01245543 0.2967576 -0.04197173

as.data.frame(out, to_string = TRUE)
#>      [w1]   (w1)    ind  m1~x   y~m1
#> 1 M+1.0SD  1.228 -0.031 0.750 -0.042
#> 2 Mean     0.259 -0.022 0.523 -0.042
#> 3 M-1.0SD -0.710 -0.012 0.297 -0.042