![[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 |
|---|---|
| .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 |
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 = -558195079
#>
#> 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 0.0410 16.3695 0.0000 [ 0.5927; 0.7366 ]
#> eta3 ~ eta1 0.4585 0.0733 6.2558 0.0000 [ 0.3552; 0.6342 ]
#> eta3 ~ eta2 0.3052 0.0767 3.9766 0.0001 [ 0.1484; 0.4213 ]
#>
#> Estimated loadings:
#> ===================
#> CI_percentile
#> Loading Estimate Std. error t-stat. p-value 95%
#> eta1 =~ y11 0.6631 0.0403 16.4725 0.0000 [ 0.5884; 0.7330 ]
#> eta1 =~ y12 0.6493 0.0407 15.9419 0.0000 [ 0.5676; 0.7241 ]
#> eta1 =~ y13 0.7613 0.0286 26.6159 0.0000 [ 0.7065; 0.8167 ]
#> eta2 =~ y21 0.5165 0.0545 9.4806 0.0000 [ 0.3943; 0.6170 ]
#> eta2 =~ y22 0.7554 0.0273 27.6630 0.0000 [ 0.7006; 0.7899 ]
#> eta2 =~ y23 0.7997 0.0322 24.8130 0.0000 [ 0.7314; 0.8451 ]
#> eta3 =~ y31 0.8223 0.0374 22.0119 0.0000 [ 0.7499; 0.8849 ]
#> eta3 =~ y32 0.6581 0.0384 17.1484 0.0000 [ 0.5964; 0.7162 ]
#> eta3 =~ y33 0.7474 0.0360 20.7872 0.0000 [ 0.6838; 0.8042 ]
#>
#> Estimated weights:
#> ==================
#> CI_percentile
#> Weight Estimate Std. error t-stat. p-value 95%
#> eta1 <~ y11 0.3956 0.0216 18.3453 0.0000 [ 0.3492; 0.4309 ]
#> eta1 <~ y12 0.3873 0.0184 21.0415 0.0000 [ 0.3512; 0.4229 ]
#> eta1 <~ y13 0.4542 0.0163 27.7905 0.0000 [ 0.4271; 0.4848 ]
#> eta2 <~ y21 0.3058 0.0281 10.8850 0.0000 [ 0.2508; 0.3558 ]
#> eta2 <~ y22 0.4473 0.0193 23.1887 0.0000 [ 0.4083; 0.4737 ]
#> eta2 <~ y23 0.4735 0.0193 24.5675 0.0000 [ 0.4319; 0.5064 ]
#> eta3 <~ y31 0.4400 0.0191 22.9954 0.0000 [ 0.4056; 0.4757 ]
#> eta3 <~ y32 0.3521 0.0183 19.2614 0.0000 [ 0.3228; 0.3865 ]
#> eta3 <~ y33 0.3999 0.0187 21.3924 0.0000 [ 0.3667; 0.4402 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_percentile
#> Total effect Estimate Std. error t-stat. p-value 95%
#> eta2 ~ eta1 0.6713 0.0410 16.3695 0.0000 [ 0.5927; 0.7366 ]
#> eta3 ~ eta1 0.6634 0.0391 16.9829 0.0000 [ 0.6073; 0.7496 ]
#> eta3 ~ eta2 0.3052 0.0767 3.9766 0.0001 [ 0.1484; 0.4213 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_percentile
#> Indirect effect Estimate Std. error t-stat. p-value 95%
#> eta3 ~ eta1 0.2049 0.0511 4.0128 0.0001 [ 0.1001; 0.2725 ]
#> ________________________________________________________________________________
# 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 0.04025314 16.472501 5.782893e-61
#> 2 eta1 =~ y12 Common factor 0.6492779 0.04072780 15.941885 3.244171e-57
#> 3 eta1 =~ y13 Common factor 0.7613458 0.02860492 26.615900 4.443776e-156
#> 4 eta2 =~ y21 Common factor 0.5164548 0.05447481 9.480617 2.527848e-21
#> 5 eta2 =~ y22 Common factor 0.7553877 0.02730675 27.663034 1.945241e-168
#> 6 eta2 =~ y23 Common factor 0.7996637 0.03222755 24.813045 6.483085e-136
#> 7 eta3 =~ y31 Common factor 0.8222773 0.03735607 22.011883 2.215928e-107
#> 8 eta3 =~ y32 Common factor 0.6580689 0.03837485 17.148441 6.456142e-66
#> 9 eta3 =~ y33 Common factor 0.7474241 0.03595602 20.787174 5.654382e-96
#> CI_percentile.95%L CI_percentile.95%U
#> 1 0.5884194 0.7329541
#> 2 0.5675513 0.7241018
#> 3 0.7064633 0.8166599
#> 4 0.3943234 0.6169772
#> 5 0.7006250 0.7898886
#> 6 0.7314200 0.8450935
#> 7 0.7499043 0.8849036
#> 8 0.5964364 0.7161620
#> 9 0.6837883 0.8042473
## 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 = -558195079
#>
#> 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 0.0410 16.3695 0.0000 [ 0.5662; 0.7783 ]
#> eta3 ~ eta1 0.4585 0.0733 6.2558 0.0000 [ 0.2571; 0.6361 ]
#> eta3 ~ eta2 0.3052 0.0767 3.9766 0.0001 [ 0.1143; 0.5111 ]
#>
#> Estimated loadings:
#> ===================
#> CI_standard_t
#> Loading Estimate Std. error t-stat. p-value 99%
#> eta1 =~ y11 0.6631 0.0403 16.4725 0.0000 [ 0.5658; 0.7739 ]
#> eta1 =~ y12 0.6493 0.0407 15.9419 0.0000 [ 0.5398; 0.7504 ]
#> eta1 =~ y13 0.7613 0.0286 26.6159 0.0000 [ 0.6788; 0.8268 ]
#> eta2 =~ y21 0.5165 0.0545 9.4806 0.0000 [ 0.3761; 0.6578 ]
#> eta2 =~ y22 0.7554 0.0273 27.6630 0.0000 [ 0.6864; 0.8276 ]
#> eta2 =~ y23 0.7997 0.0322 24.8130 0.0000 [ 0.7169; 0.8836 ]
#> eta3 =~ y31 0.8223 0.0374 22.0119 0.0000 [ 0.7239; 0.9171 ]
#> eta3 =~ y32 0.6581 0.0384 17.1484 0.0000 [ 0.5629; 0.7614 ]
#> eta3 =~ y33 0.7474 0.0360 20.7872 0.0000 [ 0.6573; 0.8432 ]
#>
#> Estimated weights:
#> ==================
#> CI_standard_t
#> Weight Estimate Std. error t-stat. p-value 99%
#> eta1 <~ y11 0.3956 0.0216 18.3453 0.0000 [ 0.3457; 0.4572 ]
#> eta1 <~ y12 0.3873 0.0184 21.0415 0.0000 [ 0.3393; 0.4345 ]
#> eta1 <~ y13 0.4542 0.0163 27.7905 0.0000 [ 0.4089; 0.4934 ]
#> eta2 <~ y21 0.3058 0.0281 10.8850 0.0000 [ 0.2336; 0.3789 ]
#> eta2 <~ y22 0.4473 0.0193 23.1887 0.0000 [ 0.3978; 0.4975 ]
#> eta2 <~ y23 0.4735 0.0193 24.5675 0.0000 [ 0.4235; 0.5232 ]
#> eta3 <~ y31 0.4400 0.0191 22.9954 0.0000 [ 0.3882; 0.4872 ]
#> eta3 <~ y32 0.3521 0.0183 19.2614 0.0000 [ 0.3061; 0.4006 ]
#> eta3 <~ y33 0.3999 0.0187 21.3924 0.0000 [ 0.3519; 0.4486 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_standard_t
#> Total effect Estimate Std. error t-stat. p-value 99%
#> eta2 ~ eta1 0.6713 0.0410 16.3695 0.0000 [ 0.5662; 0.7783 ]
#> eta3 ~ eta1 0.6634 0.0391 16.9829 0.0000 [ 0.5564; 0.7584 ]
#> eta3 ~ eta2 0.3052 0.0767 3.9766 0.0001 [ 0.1143; 0.5111 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_standard_t
#> Indirect effect Estimate Std. error t-stat. p-value 99%
#> eta3 ~ eta1 0.2049 0.0511 4.0128 0.0001 [ 0.0788; 0.3428 ]
#> ________________________________________________________________________________
# 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 0.04101114 16.369537 3.156076e-60
#> 2 eta3 ~ eta1 Common factor 0.4585068 0.07329314 6.255794 3.954995e-10
#> 3 eta3 ~ eta2 Common factor 0.3051511 0.07673741 3.976563 6.991861e-05
#> CI_standard_t.99%L CI_standard_t.99%U CI_standard_t.95%L CI_standard_t.95%U
#> 1 0.5661866 0.7782731 0.5916540 0.7528056
#> 2 0.2571046 0.6361353 0.3026188 0.5906211
#> 3 0.1142520 0.5110946 0.1619050 0.4634415
#> CI_percentile.99%L CI_percentile.99%U CI_percentile.95%L CI_percentile.95%U
#> 1 0.5914787 0.7438465 0.5926634 0.7366162
#> 2 0.3522830 0.6343465 0.3551566 0.6342166
#> 3 0.1437393 0.4306219 0.1483531 0.4212881