Confidence Intervals of Indirect Effects or Conditional Indirect Effects

Return the confidence intervals of the conditional indirect effects or conditional effects in the output of cond_indirect_effects().

Usage

# S3 method for class 'cond_indirect_effects'
confint(object, parm, level = 0.95, ...)

Arguments

object: The output of cond_indirect_effects().
parm: Ignored. Always returns the confidence intervals of the effects for all levels stored.
level: The level of confidence, default is .95, returning the 95% confidence interval. Ignored for now and will use the level of the stored intervals.
...: Additional arguments. Ignored by the function.

Value

A data frame with two columns, one for each confidence limit of the confidence intervals. The number of rows is equal to the number of rows of object.

Details

It extracts and returns the columns for confidence intervals, if available.

The type of confidence intervals depends on the call used to compute the effects. If confidence intervals have already been formed (e.g., by bootstrapping or Monte Carlo), then this function merely retrieves the confidence intervals stored.

If the following 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 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 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.

Examples


library(lavaan)
dat <- modmed_x1m3w4y1
mod <-
"
m1 ~ x  + w1 + x:w1
m2 ~ m1
y  ~ m2 + x + w4 + m2:w4
"
fit <- sem(mod, dat, meanstructure = TRUE, fixed.x = FALSE, se = "none", baseline = FALSE)
est <- parameterEstimates(fit)

# Examples for cond_indirect():

# Create levels of w1 and w4
w1levels <- mod_levels("w1", fit = fit)
w1levels
#>                 w1
#> M+1.0SD  1.2280576
#> Mean     0.2589999
#> M-1.0SD -0.7100578
w4levels <- mod_levels("w4", fit = fit)
w4levels
#>                 w4
#> M+1.0SD  1.2087784
#> Mean     0.1532493
#> M-1.0SD -0.9022798
w1w4levels <- merge_mod_levels(w1levels, w4levels)

# Conditional effects from x to m1 when w1 is equal to each of the levels
# R should be at least 2000 or 5000 in real research.
out1 <- suppressWarnings(cond_indirect_effects(x = "x", y = "m1",
                      wlevels = w1levels, fit = fit,
                      boot_ci = TRUE, R = 20, seed = 54151,
                      parallel = FALSE,
                      progress = FALSE))
confint(out1)
#>             2.5 %    97.5 %
#> M+1.0SD 0.5470997 0.9277341
#> Mean    0.4277303 0.5787753
#> M-1.0SD 0.1590855 0.4261894