View source: R/report_paths_and_intervals.R
total_indirect_ci | R Documentation |
total_indirect_ci
provides the verb for calculating the total indirect confidence intervals of a
direct or mediated path in a bootstrapped SEMinR model.
total_indirect_ci(boot_seminr_model, from, to, alpha)
boot_seminr_model |
A bootstrapped model returned by the |
from |
A parameter specifying the antecedent composite for the path. |
to |
A parameter specifying the outcome composite for the path. |
alpha |
A parameter for specifying the alpha for the confidence interval. Default is 0.05. |
A vector of lower and upper confidence intervals for a path.
Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.
bootstrap_model
mobi_mm <- constructs(
composite("Image", multi_items("IMAG", 1:5)),
composite("Expectation", multi_items("CUEX", 1:3)),
composite("Quality", multi_items("PERQ", 1:7)),
composite("Value", multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
composite("Complaints", single_item("CUSCO")),
composite("Loyalty", multi_items("CUSL", 1:3))
)
# Creating structural model
mobi_sm <- relationships(
paths(from = "Image", to = c("Expectation", "Satisfaction", "Loyalty")),
paths(from = "Expectation", to = c("Quality", "Value", "Satisfaction")),
paths(from = "Quality", to = c("Value", "Satisfaction")),
paths(from = "Value", to = c("Satisfaction")),
paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
paths(from = "Complaints", to = "Loyalty")
)
# Estimating the model
mobi_pls <- estimate_pls(data = mobi,
measurement_model = mobi_mm,
structural_model = mobi_sm)
# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = mobi_pls,
nboot = 50, cores = 2, seed = NULL)
total_indirect_ci(boot_seminr_model = boot_seminr_model,
from = "Image",
to = "Loyalty",
alpha = 0.05)
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