summarize
Summarize model
Description
![[Stable]](../help/figures/lifecycle-stable.svg)
Usage
summarize( .object = NULL, .alpha = 0.05, .ci = NULL, ... )
Arguments
.object |
An R object of class cSEMResults resulting from a call to |
.alpha |
An integer or a numeric vector of significance levels.
Defaults to |
.ci |
A vector of character strings naming the confidence interval to compute.
For possible choices see |
... |
Further arguments to |
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:
-
Elements
$Path_estimates,$Loadings_estimates,$Weight_estimates,$Weight_estimates, and$Residual_correlationare standardized data frames instead of matrices. -
Data frames
$Effect_estimates,$Indicator_correlation, and$Exo_construct_correlationare added to$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
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