![[Stable]](figures/lifecycle-stable.svg)
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.
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.
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.
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
#> ________________________________________________________________________________
#> ----------------------------------- 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 = 1986141089
#>
#> 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 0.0445 15.0897 0.0000 [ 0.6085; 0.7613 ]
#> eta3 ~ eta1 0.4585 0.0958 4.7881 0.0000 [ 0.2518; 0.6339 ]
#> eta3 ~ eta2 0.3052 0.1049 2.9099 0.0036 [ 0.1078; 0.5033 ]
#>
#> Estimated loadings:
#> ===================
#> CI_percentile
#> Loading Estimate Std. error t-stat. p-value 95%
#> eta1 =~ y11 0.6631 0.0420 15.7863 0.0000 [ 0.5625; 0.7320 ]
#> eta1 =~ y12 0.6493 0.0349 18.5981 0.0000 [ 0.5916; 0.7120 ]
#> eta1 =~ y13 0.7613 0.0296 25.7107 0.0000 [ 0.7208; 0.8164 ]
#> eta2 =~ y21 0.5165 0.0540 9.5667 0.0000 [ 0.3760; 0.5941 ]
#> eta2 =~ y22 0.7554 0.0328 23.0283 0.0000 [ 0.6868; 0.8121 ]
#> eta2 =~ y23 0.7997 0.0341 23.4575 0.0000 [ 0.7403; 0.8601 ]
#> eta3 =~ y31 0.8223 0.0311 26.4258 0.0000 [ 0.7641; 0.8671 ]
#> eta3 =~ y32 0.6581 0.0358 18.4066 0.0000 [ 0.6030; 0.7288 ]
#> eta3 =~ y33 0.7474 0.0328 22.7637 0.0000 [ 0.6848; 0.7960 ]
#>
#> Estimated weights:
#> ==================
#> CI_percentile
#> Weight Estimate Std. error t-stat. p-value 95%
#> eta1 <~ y11 0.3956 0.0207 19.1235 0.0000 [ 0.3526; 0.4269 ]
#> eta1 <~ y12 0.3873 0.0201 19.2954 0.0000 [ 0.3610; 0.4242 ]
#> eta1 <~ y13 0.4542 0.0177 25.6592 0.0000 [ 0.4259; 0.4890 ]
#> eta2 <~ y21 0.3058 0.0272 11.2622 0.0000 [ 0.2343; 0.3456 ]
#> eta2 <~ y22 0.4473 0.0240 18.6068 0.0000 [ 0.4065; 0.4960 ]
#> eta2 <~ y23 0.4735 0.0186 25.4414 0.0000 [ 0.4437; 0.5093 ]
#> eta3 <~ y31 0.4400 0.0177 24.8581 0.0000 [ 0.4093; 0.4734 ]
#> eta3 <~ y32 0.3521 0.0164 21.4878 0.0000 [ 0.3283; 0.3834 ]
#> eta3 <~ y33 0.3999 0.0161 24.8624 0.0000 [ 0.3664; 0.4259 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_percentile
#> Total effect Estimate Std. error t-stat. p-value 95%
#> eta2 ~ eta1 0.6713 0.0445 15.0897 0.0000 [ 0.6085; 0.7613 ]
#> eta3 ~ eta1 0.6634 0.0448 14.7974 0.0000 [ 0.5948; 0.7607 ]
#> eta3 ~ eta2 0.3052 0.1049 2.9099 0.0036 [ 0.1078; 0.5033 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_percentile
#> Indirect effect Estimate Std. error t-stat. p-value 95%
#> eta3 ~ eta1 0.2049 0.0671 3.0549 0.0023 [ 0.0818; 0.3594 ]
#> ________________________________________________________________________________
# 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 0.04200290 15.786289 3.866704e-56
#> 2 eta1 =~ y12 Common factor 0.6492779 0.03491094 18.598121 3.327666e-77
#> 3 eta1 =~ y13 Common factor 0.7613458 0.02961200 25.710715 8.870993e-146
#> 4 eta2 =~ y21 Common factor 0.5164548 0.05398443 9.566737 1.103331e-21
#> 5 eta2 =~ y22 Common factor 0.7553877 0.03280262 23.028276 2.428614e-117
#> 6 eta2 =~ y23 Common factor 0.7996637 0.03408986 23.457522 1.107766e-121
#> 7 eta3 =~ y31 Common factor 0.8222773 0.03111648 26.425781 6.928218e-154
#> 8 eta3 =~ y32 Common factor 0.6580689 0.03575175 18.406619 1.162573e-75
#> 9 eta3 =~ y33 Common factor 0.7474241 0.03283407 22.763674 1.050628e-114
#> CI_percentile.95%L CI_percentile.95%U
#> 1 0.5625161 0.7319642
#> 2 0.5915687 0.7120039
#> 3 0.7208341 0.8163570
#> 4 0.3760159 0.5941054
#> 5 0.6868413 0.8121032
#> 6 0.7403213 0.8600539
#> 7 0.7640804 0.8670574
#> 8 0.6030466 0.7288495
#> 9 0.6847907 0.7960287
## 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 = 1986141089
#>
#> 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 0.0445 15.0897 0.0000 [ 0.5546; 0.7847 ]
#> eta3 ~ eta1 0.4585 0.0958 4.7881 0.0000 [ 0.2133; 0.7086 ]
#> eta3 ~ eta2 0.3052 0.1049 2.9099 0.0036 [ 0.0342; 0.5765 ]
#>
#> Estimated loadings:
#> ===================
#> CI_standard_t
#> Loading Estimate Std. error t-stat. p-value 99%
#> eta1 =~ y11 0.6631 0.0420 15.7863 0.0000 [ 0.5682; 0.7854 ]
#> eta1 =~ y12 0.6493 0.0349 18.5981 0.0000 [ 0.5515; 0.7320 ]
#> eta1 =~ y13 0.7613 0.0296 25.7107 0.0000 [ 0.6842; 0.8373 ]
#> eta2 =~ y21 0.5165 0.0540 9.5667 0.0000 [ 0.3918; 0.6710 ]
#> eta2 =~ y22 0.7554 0.0328 23.0283 0.0000 [ 0.6736; 0.8432 ]
#> eta2 =~ y23 0.7997 0.0341 23.4575 0.0000 [ 0.7103; 0.8866 ]
#> eta3 =~ y31 0.8223 0.0311 26.4258 0.0000 [ 0.7475; 0.9084 ]
#> eta3 =~ y32 0.6581 0.0358 18.4066 0.0000 [ 0.5565; 0.7414 ]
#> eta3 =~ y33 0.7474 0.0328 22.7637 0.0000 [ 0.6758; 0.8456 ]
#>
#> Estimated weights:
#> ==================
#> CI_standard_t
#> Weight Estimate Std. error t-stat. p-value 99%
#> eta1 <~ y11 0.3956 0.0207 19.1235 0.0000 [ 0.3491; 0.4561 ]
#> eta1 <~ y12 0.3873 0.0201 19.2954 0.0000 [ 0.3295; 0.4333 ]
#> eta1 <~ y13 0.4542 0.0177 25.6592 0.0000 [ 0.4064; 0.4979 ]
#> eta2 <~ y21 0.3058 0.0272 11.2622 0.0000 [ 0.2421; 0.3825 ]
#> eta2 <~ y22 0.4473 0.0240 18.6068 0.0000 [ 0.3824; 0.5067 ]
#> eta2 <~ y23 0.4735 0.0186 25.4414 0.0000 [ 0.4201; 0.5163 ]
#> eta3 <~ y31 0.4400 0.0177 24.8581 0.0000 [ 0.3942; 0.4857 ]
#> eta3 <~ y32 0.3521 0.0164 21.4878 0.0000 [ 0.3025; 0.3873 ]
#> eta3 <~ y33 0.3999 0.0161 24.8624 0.0000 [ 0.3628; 0.4460 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_standard_t
#> Total effect Estimate Std. error t-stat. p-value 99%
#> eta2 ~ eta1 0.6713 0.0445 15.0897 0.0000 [ 0.5546; 0.7847 ]
#> eta3 ~ eta1 0.6634 0.0448 14.7974 0.0000 [ 0.5515; 0.7833 ]
#> eta3 ~ eta2 0.3052 0.1049 2.9099 0.0036 [ 0.0342; 0.5765 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_standard_t
#> Indirect effect Estimate Std. error t-stat. p-value 99%
#> eta3 ~ eta1 0.2049 0.0671 3.0549 0.0023 [ 0.0331; 0.3799 ]
#> ________________________________________________________________________________
# 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 0.04448939 15.089743 1.891773e-51
#> 2 eta3 ~ eta1 Common factor 0.4585068 0.09575954 4.788105 1.683633e-06
#> 3 eta3 ~ eta2 Common factor 0.3051511 0.10486687 2.909891 3.615553e-03
#> CI_standard_t.99%L CI_standard_t.99%U CI_standard_t.95%L CI_standard_t.95%U
#> 1 0.55459181 0.7846658 0.58221920 0.7570384
#> 2 0.21334902 0.7085633 0.27281457 0.6490977
#> 3 0.03419795 0.5765102 0.09931905 0.5113891
#> CI_percentile.99%L CI_percentile.99%U CI_percentile.95%L CI_percentile.95%U
#> 1 0.59938741 0.7774667 0.6085326 0.7612785
#> 2 0.24321915 0.6684780 0.2517503 0.6339424
#> 3 0.09331947 0.5659510 0.1077846 0.5033347