Function Syntax
standardizedSolution_boot(object,
level = .95,
type = "std.all",
boot_delta_ratio = FALSE,
boot_ci_type = c("perc", "bc", "bca.simple"),
save_boot_est_std = TRUE,
boot_pvalue = TRUE,
boot_pvalue_min_size = 1000,
...)
Arguments
object |
A model fitted by lavaan. |
level |
Confidence level for the confidence intervals. For example,
.95 gives 95% confidence intervals. |
type |
Type of standardized coefficients. Same as in
lavaan::standardizedSolution(), such as
"std.all" or "std.lv". |
boot_delta_ratio |
Whether to calculate how wide the bootstrap confidence interval is
compared to the usual confidence interval (delta method). Useful for
comparing both methods. |
boot_ci_type |
Method for forming bootstrap confidence intervals.
"perc" gives percentile intervals; "bc" and
"bca.simple" give bias-corrected intervals. |
save_boot_est_std |
Whether to save the bootstrap estimates of standardized coefficients
in the result. Saved in the attribute boot_est_std if
TRUE. |
boot_pvalue |
Whether to compute asymmetric p-values based on bootstrap
results. Only available when percentile confidence intervals are
used. |
boot_pvalue_min_size |
Minimum number of valid bootstrap samples needed to compute
asymmetric p-values. If fewer samples are available,
p-values will not be computed and will be shown as
NA. |
... |
Additional arguments passed to
lavaan::standardizedSolution(). |
Example
Data and Model
# Set seed for reproducibility
set.seed(1234)
# Generate data
n <- 1000
x <- runif(n) - 0.5
m <- 0.20 * x + rnorm(n)
y <- 0.17 * m + rnorm(n)
dat <- data.frame(x, y, m)
# Specify mediation model in lavaan syntax
mod <- '
m ~ a * x
y ~ b * m + cp * x
ab := a * b
total := a * b + cp
'
Basic usage: default settings
# (should use ≥2000 in real studies)
fit <- sem(mod, data = dat, se = "boot", bootstrap = 500)
#> Warning: lavaan->lav_model_nvcov_bootstrap():
#> 1 bootstrap runs failed or did not converge.
std_boot <- standardizedSolution_boot(fit)
#> Warning in standardizedSolution_boot(fit): The number of bootstrap samples
#> (499) is less than 'boot_pvalue_min_size' (1000). Bootstrap p-values are not
#> computed.
print(std_boot)
#>
#> Bootstrapping:
#>
#> Valid Bootstrap Samples: 499
#> Level of Confidence: 95.0%
#> CI Type: Percentile
#> Standardization Type: std.all
#>
#> Parameter Estimates Settings:
#>
#> Standard errors: Bootstrap
#> Number of requested bootstrap draws: 500
#> Number of successful bootstrap draws: 499
#>
#> Regressions:
#> Std SE p CI.Lo CI.Up bSE bCI.Lo bCI.Up
#> m ~
#> x (a) 0.027 0.034 0.425 -0.040 0.095 0.035 -0.047 0.088
#> y ~
#> m (b) 0.174 0.032 0.000 0.111 0.237 0.032 0.108 0.237
#> x (cp) -0.005 0.032 0.873 -0.067 0.057 0.032 -0.068 0.059
#>
#> Variances:
#> Std SE p CI.Lo CI.Up bSE bCI.Lo bCI.Up
#> .m 0.999 0.002 0.000 0.996 1.003 0.002 0.992 1.000
#> .y 0.970 0.011 0.000 0.948 0.992 0.011 0.944 0.986
#> x 1.000
#>
#> Defined Parameters:
#> Std SE p CI.Lo CI.Up bSE bCI.Lo bCI.Up
#> ab (ab) 0.005 0.006 0.427 -0.007 0.017 0.006 -0.008 0.017
#> total (total) -0.000 0.033 0.993 -0.065 0.064 0.033 -0.066 0.065
#>
#> Footnote:
#> - Std: Standardized estimates.
#> - SE: Delta method standard errors.
#> - p: Delta method p-values.
#> - CI.Lo, CI.Up: Delta method confidence intervals.
#> - bSE: Bootstrap standard errors.
#> - bCI.Lo, bCI.Up: Bootstrap confidence intervals.
# this function also do not require 'se = "boot"' when fitting the model
fit2 <- sem(mod, data = dat, fixed.x = FALSE)
fit2 <- store_boot(fit2, R = 500)
std_boot2 <- standardizedSolution_boot(fit2)
#> Warning in standardizedSolution_boot(fit2): The number of bootstrap samples
#> (500) is less than 'boot_pvalue_min_size' (1000). Bootstrap p-values are not
#> computed.
print(std_boot)
#>
#> Bootstrapping:
#>
#> Valid Bootstrap Samples: 499
#> Level of Confidence: 95.0%
#> CI Type: Percentile
#> Standardization Type: std.all
#>
#> Parameter Estimates Settings:
#>
#> Standard errors: Bootstrap
#> Number of requested bootstrap draws: 500
#> Number of successful bootstrap draws: 499
#>
#> Regressions:
#> Std SE p CI.Lo CI.Up bSE bCI.Lo bCI.Up
#> m ~
#> x (a) 0.027 0.034 0.425 -0.040 0.095 0.035 -0.047 0.088
#> y ~
#> m (b) 0.174 0.032 0.000 0.111 0.237 0.032 0.108 0.237
#> x (cp) -0.005 0.032 0.873 -0.067 0.057 0.032 -0.068 0.059
#>
#> Variances:
#> Std SE p CI.Lo CI.Up bSE bCI.Lo bCI.Up
#> .m 0.999 0.002 0.000 0.996 1.003 0.002 0.992 1.000
#> .y 0.970 0.011 0.000 0.948 0.992 0.011 0.944 0.986
#> x 1.000
#>
#> Defined Parameters:
#> Std SE p CI.Lo CI.Up bSE bCI.Lo bCI.Up
#> ab (ab) 0.005 0.006 0.427 -0.007 0.017 0.006 -0.008 0.017
#> total (total) -0.000 0.033 0.993 -0.065 0.064 0.033 -0.066 0.065
#>
#> Footnote:
#> - Std: Standardized estimates.
#> - SE: Delta method standard errors.
#> - p: Delta method p-values.
#> - CI.Lo, CI.Up: Delta method confidence intervals.
#> - bSE: Bootstrap standard errors.
#> - bCI.Lo, bCI.Up: Bootstrap confidence intervals.
standardizedSolution_boot(): Different Options
print(): Different Options
# Print standardized solution in friendly format
print(std_boot, output = "text")
# Print with more decimal places (e.g., 5 decimal digits)
print(std_boot, nd = 5)
# Print only bootstrap confidence intervals
print(std_boot, boot_ci_only = TRUE)
# Print both unstandardized and standardized solution
print(std_boot, standardized_only = FALSE)
# Combine options: more decimals + show both solutions
print(std_boot, nd = 4, standardized_only = FALSE)
# Combine options: show only bootstrap CI, 5 decimal places
print(std_boot, boot_ci_only = TRUE, nd = 5)