![[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 = 480202620
#>
#> 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 0.0462 14.5223 0.0000 [ 0.6090; 0.7701 ]
#> eta3 ~ eta1 0.4585 0.0871 5.2666 0.0000 [ 0.3096; 0.6655 ]
#> eta3 ~ eta2 0.3052 0.0855 3.5681 0.0004 [ 0.1232; 0.4468 ]
#>
#> Estimated loadings:
#> ===================
#> CI_percentile
#> Loading Estimate Std. error t-stat. p-value 95%
#> eta1 =~ y11 0.6631 0.0350 18.9421 0.0000 [ 0.5994; 0.7262 ]
#> eta1 =~ y12 0.6493 0.0434 14.9734 0.0000 [ 0.5410; 0.7066 ]
#> eta1 =~ y13 0.7613 0.0289 26.3674 0.0000 [ 0.7011; 0.8085 ]
#> eta2 =~ y21 0.5165 0.0596 8.6603 0.0000 [ 0.3588; 0.5928 ]
#> eta2 =~ y22 0.7554 0.0388 19.4875 0.0000 [ 0.6775; 0.8091 ]
#> eta2 =~ y23 0.7997 0.0393 20.3504 0.0000 [ 0.7444; 0.8740 ]
#> eta3 =~ y31 0.8223 0.0346 23.7368 0.0000 [ 0.7771; 0.8800 ]
#> eta3 =~ y32 0.6581 0.0367 17.9229 0.0000 [ 0.5975; 0.7239 ]
#> eta3 =~ y33 0.7474 0.0319 23.4188 0.0000 [ 0.6919; 0.8172 ]
#>
#> Estimated weights:
#> ==================
#> CI_percentile
#> Weight Estimate Std. error t-stat. p-value 95%
#> eta1 <~ y11 0.3956 0.0195 20.3270 0.0000 [ 0.3621; 0.4335 ]
#> eta1 <~ y12 0.3873 0.0205 18.9133 0.0000 [ 0.3415; 0.4157 ]
#> eta1 <~ y13 0.4542 0.0209 21.7040 0.0000 [ 0.4223; 0.5004 ]
#> eta2 <~ y21 0.3058 0.0281 10.8671 0.0000 [ 0.2423; 0.3483 ]
#> eta2 <~ y22 0.4473 0.0237 18.9021 0.0000 [ 0.4173; 0.5105 ]
#> eta2 <~ y23 0.4735 0.0252 18.7918 0.0000 [ 0.4348; 0.5226 ]
#> eta3 <~ y31 0.4400 0.0177 24.8070 0.0000 [ 0.4108; 0.4704 ]
#> eta3 <~ y32 0.3521 0.0179 19.6834 0.0000 [ 0.3209; 0.3785 ]
#> eta3 <~ y33 0.3999 0.0153 26.1396 0.0000 [ 0.3711; 0.4252 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_percentile
#> Total effect Estimate Std. error t-stat. p-value 95%
#> eta2 ~ eta1 0.6713 0.0462 14.5223 0.0000 [ 0.6090; 0.7701 ]
#> eta3 ~ eta1 0.6634 0.0459 14.4523 0.0000 [ 0.5968; 0.7525 ]
#> eta3 ~ eta2 0.3052 0.0855 3.5681 0.0004 [ 0.1232; 0.4468 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_percentile
#> Indirect effect Estimate Std. error t-stat. p-value 95%
#> eta3 ~ eta1 0.2049 0.0576 3.5543 0.0004 [ 0.0834; 0.2865 ]
#> ________________________________________________________________________________
# 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 0.03500505 18.942118 5.129172e-80
#> 2 eta1 =~ y12 Common factor 0.6492779 0.04336196 14.973446 1.094971e-50
#> 3 eta1 =~ y13 Common factor 0.7613458 0.02887447 26.367440 3.238891e-153
#> 4 eta2 =~ y21 Common factor 0.5164548 0.05963452 8.660333 4.703897e-18
#> 5 eta2 =~ y22 Common factor 0.7553877 0.03876259 19.487543 1.400470e-84
#> 6 eta2 =~ y23 Common factor 0.7996637 0.03929480 20.350370 4.607869e-92
#> 7 eta3 =~ y31 Common factor 0.8222773 0.03464141 23.736833 1.502830e-124
#> 8 eta3 =~ y32 Common factor 0.6580689 0.03671670 17.922877 7.817852e-72
#> 9 eta3 =~ y33 Common factor 0.7474241 0.03191549 23.418849 2.746715e-121
#> CI_percentile.95%L CI_percentile.95%U
#> 1 0.5993841 0.7261978
#> 2 0.5410282 0.7066233
#> 3 0.7011089 0.8085344
#> 4 0.3588476 0.5928176
#> 5 0.6775096 0.8091281
#> 6 0.7444123 0.8740411
#> 7 0.7771468 0.8799801
#> 8 0.5975400 0.7238565
#> 9 0.6919075 0.8171674
## 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 = 480202620
#>
#> 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 0.0462 14.5223 0.0000 [ 0.5406; 0.7797 ]
#> eta3 ~ eta1 0.4585 0.0871 5.2666 0.0000 [ 0.1921; 0.6423 ]
#> eta3 ~ eta2 0.3052 0.0855 3.5681 0.0004 [ 0.1269; 0.5692 ]
#>
#> Estimated loadings:
#> ===================
#> CI_standard_t
#> Loading Estimate Std. error t-stat. p-value 99%
#> eta1 =~ y11 0.6631 0.0350 18.9421 0.0000 [ 0.5675; 0.7485 ]
#> eta1 =~ y12 0.6493 0.0434 14.9734 0.0000 [ 0.5502; 0.7744 ]
#> eta1 =~ y13 0.7613 0.0289 26.3674 0.0000 [ 0.6925; 0.8418 ]
#> eta2 =~ y21 0.5165 0.0596 8.6603 0.0000 [ 0.3807; 0.6891 ]
#> eta2 =~ y22 0.7554 0.0388 19.4875 0.0000 [ 0.6478; 0.8483 ]
#> eta2 =~ y23 0.7997 0.0393 20.3504 0.0000 [ 0.7034; 0.9066 ]
#> eta3 =~ y31 0.8223 0.0346 23.7368 0.0000 [ 0.7324; 0.9116 ]
#> eta3 =~ y32 0.6581 0.0367 17.9229 0.0000 [ 0.5550; 0.7449 ]
#> eta3 =~ y33 0.7474 0.0319 23.4188 0.0000 [ 0.6654; 0.8305 ]
#>
#> Estimated weights:
#> ==================
#> CI_standard_t
#> Weight Estimate Std. error t-stat. p-value 99%
#> eta1 <~ y11 0.3956 0.0195 20.3270 0.0000 [ 0.3385; 0.4391 ]
#> eta1 <~ y12 0.3873 0.0205 18.9133 0.0000 [ 0.3389; 0.4448 ]
#> eta1 <~ y13 0.4542 0.0209 21.7040 0.0000 [ 0.3991; 0.5073 ]
#> eta2 <~ y21 0.3058 0.0281 10.8671 0.0000 [ 0.2421; 0.3876 ]
#> eta2 <~ y22 0.4473 0.0237 18.9021 0.0000 [ 0.3776; 0.4999 ]
#> eta2 <~ y23 0.4735 0.0252 18.7918 0.0000 [ 0.4071; 0.5374 ]
#> eta3 <~ y31 0.4400 0.0177 24.8070 0.0000 [ 0.3964; 0.4881 ]
#> eta3 <~ y32 0.3521 0.0179 19.6834 0.0000 [ 0.3035; 0.3960 ]
#> eta3 <~ y33 0.3999 0.0153 26.1396 0.0000 [ 0.3629; 0.4420 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_standard_t
#> Total effect Estimate Std. error t-stat. p-value 99%
#> eta2 ~ eta1 0.6713 0.0462 14.5223 0.0000 [ 0.5406; 0.7797 ]
#> eta3 ~ eta1 0.6634 0.0459 14.4523 0.0000 [ 0.5299; 0.7672 ]
#> eta3 ~ eta2 0.3052 0.0855 3.5681 0.0004 [ 0.1269; 0.5692 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_standard_t
#> Indirect effect Estimate Std. error t-stat. p-value 99%
#> eta3 ~ eta1 0.2049 0.0576 3.5543 0.0004 [ 0.0823; 0.3804 ]
#> ________________________________________________________________________________
# 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 0.04622762 14.522345 8.746567e-48
#> 2 eta3 ~ eta1 Common factor 0.4585068 0.08705866 5.266642 1.389418e-07
#> 3 eta3 ~ eta2 Common factor 0.3051511 0.08552290 3.568063 3.596297e-04
#> CI_standard_t.99%L CI_standard_t.99%U CI_standard_t.95%L CI_standard_t.95%U
#> 1 0.5406227 0.7796859 0.5693295 0.7509790
#> 2 0.1920959 0.6423141 0.2461583 0.5882517
#> 3 0.1268923 0.5691685 0.1800010 0.5160598
#> CI_percentile.99%L CI_percentile.99%U CI_percentile.95%L CI_percentile.95%U
#> 1 0.55930898 0.7798836 0.6089777 0.7700964
#> 2 0.29518710 0.6714690 0.3096364 0.6654923
#> 3 0.09895171 0.4526183 0.1232244 0.4468158