stable
summarize(
.object = NULL,
.alpha = 0.05,
.ci = NULL,
...
)An R object of class cSEMResults resulting from a call to csem().
An integer or a numeric vector of significance levels.
Defaults to 0.05.
A vector of character strings naming the confidence interval to compute.
For possible choices see infer().
Further arguments to summarize(). Currently ignored.
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.
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.
## 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
#> ________________________________________________________________________________
#> ----------------------------------- Overview -----------------------------------
#>
#> General information:
#> ------------------------
#> Estimation status = Ok
#> Number of observations = 500
#> Weight estimator = PLS-PM
#> Inner weighting scheme = "path"
#> Type of indicator correlation = Pearson
#> Path model estimator = OLS
#> Second-order approach = NA
#> Type of path model = Linear
#> Disattenuated = Yes (PLSc)
#>
#> Resample information:
#> ---------------------
#> Resample method = "bootstrap"
#> Number of resamples = 40
#> Number of admissible results = 40
#> Approach to handle inadmissibles = "drop"
#> Sign change option = "none"
#> Random seed = -1448516543
#>
#> Construct details:
#> ------------------
#> Name Modeled as Order Mode
#>
#> eta1 Common factor First order "modeA"
#> eta2 Common factor First order "modeA"
#> eta3 Common factor First order "modeA"
#>
#> ----------------------------------- Estimates ----------------------------------
#>
#> Estimated path coefficients:
#> ============================
#> CI_percentile
#> Path Estimate Std. error t-stat. p-value 95%
#> eta2 ~ eta1 0.6713 0.0450 14.9256 0.0000 [ 0.5797; 0.7422 ]
#> eta3 ~ eta1 0.4585 0.0816 5.6166 0.0000 [ 0.2951; 0.6608 ]
#> eta3 ~ eta2 0.3052 0.0808 3.7746 0.0002 [ 0.1142; 0.4427 ]
#>
#> Estimated loadings:
#> ===================
#> CI_percentile
#> Loading Estimate Std. error t-stat. p-value 95%
#> eta1 =~ y11 0.6631 0.0430 15.4167 0.0000 [ 0.5844; 0.7694 ]
#> eta1 =~ y12 0.6493 0.0435 14.9359 0.0000 [ 0.5361; 0.6913 ]
#> eta1 =~ y13 0.7613 0.0376 20.2732 0.0000 [ 0.6801; 0.8230 ]
#> eta2 =~ y21 0.5165 0.0585 8.8221 0.0000 [ 0.3864; 0.5877 ]
#> eta2 =~ y22 0.7554 0.0342 22.0990 0.0000 [ 0.7037; 0.8197 ]
#> eta2 =~ y23 0.7997 0.0355 22.5237 0.0000 [ 0.7343; 0.8617 ]
#> eta3 =~ y31 0.8223 0.0353 23.2870 0.0000 [ 0.7547; 0.8870 ]
#> eta3 =~ y32 0.6581 0.0379 17.3646 0.0000 [ 0.5919; 0.7234 ]
#> eta3 =~ y33 0.7474 0.0382 19.5765 0.0000 [ 0.6681; 0.8074 ]
#>
#> Estimated weights:
#> ==================
#> CI_percentile
#> Weight Estimate Std. error t-stat. p-value 95%
#> eta1 <~ y11 0.3956 0.0280 14.1044 0.0000 [ 0.3641; 0.4693 ]
#> eta1 <~ y12 0.3873 0.0203 19.1000 0.0000 [ 0.3402; 0.4088 ]
#> eta1 <~ y13 0.4542 0.0186 24.3816 0.0000 [ 0.4256; 0.4897 ]
#> eta2 <~ y21 0.3058 0.0294 10.4070 0.0000 [ 0.2391; 0.3532 ]
#> eta2 <~ y22 0.4473 0.0207 21.5898 0.0000 [ 0.4144; 0.4852 ]
#> eta2 <~ y23 0.4735 0.0219 21.6550 0.0000 [ 0.4322; 0.5136 ]
#> eta3 <~ y31 0.4400 0.0149 29.5423 0.0000 [ 0.4199; 0.4645 ]
#> eta3 <~ y32 0.3521 0.0181 19.4721 0.0000 [ 0.3189; 0.3896 ]
#> eta3 <~ y33 0.3999 0.0208 19.2066 0.0000 [ 0.3557; 0.4283 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_percentile
#> Total effect Estimate Std. error t-stat. p-value 95%
#> eta2 ~ eta1 0.6713 0.0450 14.9256 0.0000 [ 0.5797; 0.7422 ]
#> eta3 ~ eta1 0.6634 0.0428 15.5039 0.0000 [ 0.5834; 0.7399 ]
#> eta3 ~ eta2 0.3052 0.0808 3.7746 0.0002 [ 0.1142; 0.4427 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_percentile
#> Indirect effect Estimate Std. error t-stat. p-value 95%
#> eta3 ~ eta1 0.2049 0.0516 3.9693 0.0001 [ 0.0791; 0.2892 ]
#> ________________________________________________________________________________
# Extract e.g. the loadings
res_summarize$Estimates$Loading_estimates
#> Name Construct_type Estimate Std_err t_stat p_value
#> 1 eta1 =~ y11 Common factor 0.6630699 0.04300998 15.416652 1.264943e-53
#> 2 eta1 =~ y12 Common factor 0.6492779 0.04347106 14.935867 1.925539e-50
#> 3 eta1 =~ y13 Common factor 0.7613458 0.03755424 20.273230 2.216221e-91
#> 4 eta2 =~ y21 Common factor 0.5164548 0.05854107 8.822094 1.123393e-18
#> 5 eta2 =~ y22 Common factor 0.7553877 0.03418199 22.098998 3.231609e-108
#> 6 eta2 =~ y23 Common factor 0.7996637 0.03550327 22.523662 2.433985e-112
#> 7 eta3 =~ y31 Common factor 0.8222773 0.03531062 23.286972 6.008335e-120
#> 8 eta3 =~ y32 Common factor 0.6580689 0.03789720 17.364577 1.530230e-67
#> 9 eta3 =~ y33 Common factor 0.7474241 0.03817959 19.576536 2.451340e-85
#> CI_percentile.95%L CI_percentile.95%U
#> 1 0.5844162 0.7693603
#> 2 0.5360643 0.6912905
#> 3 0.6801222 0.8229997
#> 4 0.3863727 0.5877064
#> 5 0.7036584 0.8196760
#> 6 0.7343090 0.8617070
#> 7 0.7547130 0.8870358
#> 8 0.5919415 0.7234484
#> 9 0.6680650 0.8074060
## 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
#> ________________________________________________________________________________
#> ----------------------------------- Overview -----------------------------------
#>
#> General information:
#> ------------------------
#> Estimation status = Ok
#> Number of observations = 500
#> Weight estimator = PLS-PM
#> Inner weighting scheme = "path"
#> Type of indicator correlation = Pearson
#> Path model estimator = OLS
#> Second-order approach = NA
#> Type of path model = Linear
#> Disattenuated = Yes (PLSc)
#>
#> Resample information:
#> ---------------------
#> Resample method = "bootstrap"
#> Number of resamples = 40
#> Number of admissible results = 40
#> Approach to handle inadmissibles = "drop"
#> Sign change option = "none"
#> Random seed = -1448516543
#>
#> Construct details:
#> ------------------
#> Name Modeled as Order Mode
#>
#> eta1 Common factor First order "modeA"
#> eta2 Common factor First order "modeA"
#> eta3 Common factor First order "modeA"
#>
#> ----------------------------------- Estimates ----------------------------------By default, only one confidence interval supplied to `.ci` is printed.
#> Use `xxx` to print all confidence intervals (not yet implemented).
#>
#>
#>
#> Estimated path coefficients:
#> ============================
#> CI_standard_t
#> Path Estimate Std. error t-stat. p-value 99%
#> eta2 ~ eta1 0.6713 0.0450 14.9256 0.0000 [ 0.5512; 0.7838 ]
#> eta3 ~ eta1 0.4585 0.0816 5.6166 0.0000 [ 0.2286; 0.6508 ]
#> eta3 ~ eta2 0.3052 0.0808 3.7746 0.0002 [ 0.1139; 0.5320 ]
#>
#> Estimated loadings:
#> ===================
#> CI_standard_t
#> Loading Estimate Std. error t-stat. p-value 99%
#> eta1 =~ y11 0.6631 0.0430 15.4167 0.0000 [ 0.5435; 0.7659 ]
#> eta1 =~ y12 0.6493 0.0435 14.9359 0.0000 [ 0.5549; 0.7797 ]
#> eta1 =~ y13 0.7613 0.0376 20.2732 0.0000 [ 0.6646; 0.8588 ]
#> eta2 =~ y21 0.5165 0.0585 8.8221 0.0000 [ 0.3745; 0.6772 ]
#> eta2 =~ y22 0.7554 0.0342 22.0990 0.0000 [ 0.6569; 0.8336 ]
#> eta2 =~ y23 0.7997 0.0355 22.5237 0.0000 [ 0.7132; 0.8968 ]
#> eta3 =~ y31 0.8223 0.0353 23.2870 0.0000 [ 0.7301; 0.9127 ]
#> eta3 =~ y32 0.6581 0.0379 17.3646 0.0000 [ 0.5528; 0.7487 ]
#> eta3 =~ y33 0.7474 0.0382 19.5765 0.0000 [ 0.6565; 0.8539 ]
#>
#> Estimated weights:
#> ==================
#> CI_standard_t
#> Weight Estimate Std. error t-stat. p-value 99%
#> eta1 <~ y11 0.3956 0.0280 14.1044 0.0000 [ 0.3153; 0.4603 ]
#> eta1 <~ y12 0.3873 0.0203 19.1000 0.0000 [ 0.3435; 0.4484 ]
#> eta1 <~ y13 0.4542 0.0186 24.3816 0.0000 [ 0.4034; 0.4998 ]
#> eta2 <~ y21 0.3058 0.0294 10.4070 0.0000 [ 0.2355; 0.3875 ]
#> eta2 <~ y22 0.4473 0.0207 21.5898 0.0000 [ 0.3871; 0.4942 ]
#> eta2 <~ y23 0.4735 0.0219 21.6550 0.0000 [ 0.4195; 0.5325 ]
#> eta3 <~ y31 0.4400 0.0149 29.5423 0.0000 [ 0.4013; 0.4783 ]
#> eta3 <~ y32 0.3521 0.0181 19.4721 0.0000 [ 0.3017; 0.3952 ]
#> eta3 <~ y33 0.3999 0.0208 19.2066 0.0000 [ 0.3503; 0.4580 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_standard_t
#> Total effect Estimate Std. error t-stat. p-value 99%
#> eta2 ~ eta1 0.6713 0.0450 14.9256 0.0000 [ 0.5512; 0.7838 ]
#> eta3 ~ eta1 0.6634 0.0428 15.5039 0.0000 [ 0.5462; 0.7675 ]
#> eta3 ~ eta2 0.3052 0.0808 3.7746 0.0002 [ 0.1139; 0.5320 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_standard_t
#> Indirect effect Estimate Std. error t-stat. p-value 99%
#> eta3 ~ eta1 0.2049 0.0516 3.9693 0.0001 [ 0.0837; 0.3506 ]
#> ________________________________________________________________________________
# Extract the loading including both confidence intervals
res_summarize$Estimates$Path_estimates
#> Name Construct_type Estimate Std_err t_stat p_value
#> 1 eta2 ~ eta1 Common factor 0.6713334 0.04497877 14.925563 2.247316e-50
#> 2 eta3 ~ eta1 Common factor 0.4585068 0.08163369 5.616636 1.947107e-08
#> 3 eta3 ~ eta2 Common factor 0.3051511 0.08084418 3.774559 1.602911e-04
#> CI_standard_t.99%L CI_standard_t.99%U CI_standard_t.95%L CI_standard_t.95%U
#> 1 0.5511944 0.7837992 0.5791257 0.7558679
#> 2 0.2286396 0.6508030 0.2793332 0.6001095
#> 3 0.1138769 0.5319574 0.1640802 0.4817541
#> CI_percentile.99%L CI_percentile.99%U CI_percentile.95%L CI_percentile.95%U
#> 1 0.5638533 0.7602275 0.5796621 0.7421987
#> 2 0.2925006 0.6696826 0.2950961 0.6608253
#> 3 0.1085743 0.4604986 0.1141781 0.4427171