summarize {cSEM} | R Documentation |
summarize( .object = NULL, .alpha = 0.05, .ci = NULL, ... )
.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 |
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.
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_correlation
are standardized data frames instead of matrices.
Data frames $Effect_estimates
, $Indicator_correlation
, and
$Exo_construct_correlation
are added to $Estimates
.
The data frame format is usually much more convenient if users intend to present the results in e.g., a paper or a presentation.
csem, assess()
, cSEMResults, exportToExcel()
## Take a look at the dataset #?threecommonfactors ## Specify the (correct) model model <- " # Structural model eta2 ~ eta1 eta3 ~ eta1 + eta2 # (Reflective) measurement model eta1 =~ y11 + y12 + y13 eta2 =~ y21 + y22 + y23 eta3 =~ y31 + y32 + y33 " ## Estimate res <- csem(threecommonfactors, model, .resample_method = "bootstrap", .R = 40) ## Postestimation res_summarize <- summarize(res) res_summarize # Extract e.g. the loadings res_summarize$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