# summarize

##### Summarize model

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()`

.

##### 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

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_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.

##### See Also

##### Examples

```
# NOT RUN {
## 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 = 50)
## 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
# }
```

*Documentation reproduced from package cSEM, version 0.1.0, License: GPL-3*