summarize: Summarize model

In cSEM: Composite-Based Structural Equation Modeling

Summarize model

Description

\lifecycle

stable

Usage

summarize(
 .object = NULL, 
 .alpha  = 0.05,
 .ci     = NULL,
 ...
 )

Arguments

.object	An R object of class cSEMResults resulting from a call to csem().
.alpha	An integer or a numeric vector of significance levels. Defaults to 0.05.
.ci	A vector of character strings naming the confidence interval to compute. For possible choices see infer().
...	Further arguments to summarize(). Currently ignored.

Details

The summary is mainly focused on estimated parameters. For quality criteria such as the average variance extracted (AVE), reliability estimates, effect size estimates etc., use assess().

If .object contains resamples, standard errors, t-values and p-values (assuming estimates are standard normally distributed) are printed as well. By default the percentile confidence interval is given as well. For other confidence intervals use the .ci argument. See infer() for possible choices and a description.

Value

An object of class cSEMSummarize. A cSEMSummarize object has the same structure as the cSEMResults object with a couple differences:

1. Elements $Path_estimates, $Loadings_estimates, $Weight_estimates, $Weight_estimates, and $Residual_correlation are standardized data frames instead of matrices.

2. Data frames $Effect_estimates, $Indicator_correlation, and $Exo_construct_correlation are added to $Estimates.

The data frame format is usually much more convenient if users intend to present the results in e.g., a paper or a presentation.

See Also

csem, assess(), cSEMResults, exportToExcel()

Examples

## Take a look at the dataset
#?threecommonfactors

## Specify the (correct) model
model <- "
# Structural model
eta2 ~ eta1
eta3 ~ eta1 + eta2

# (Reflective) measurement model
eta1 =~ y11 + y12 + y13
eta2 =~ y21 + y22 + y23
eta3 =~ y31 + y32 + y33
"

## Estimate
res <- csem(threecommonfactors, model, .resample_method = "bootstrap", .R = 40)

## Postestimation
res_summarize <- summarize(res)
res_summarize

# Extract e.g. the loadings
res_summarize$Estimates$Loading_estimates

## By default only the 95% percentile confidence interval is printed. User
## can have several confidence interval computed, however, only the first
## will be printed.

res_summarize <- summarize(res, .ci = c("CI_standard_t", "CI_percentile"), 
                           .alpha = c(0.05, 0.01))
res_summarize

# Extract the loading including both confidence intervals
res_summarize$Estimates$Path_estimates

cSEM documentation built on Nov. 25, 2022, 1:05 a.m.