![[Stable]](figures/lifecycle-stable.svg)
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.
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 = -565493332
#>
#> 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 0.0490 13.7030 0.0000 [ 0.5860; 0.7444 ]
#> eta3 ~ eta1 0.4585 0.0788 5.8194 0.0000 [ 0.3141; 0.5953 ]
#> eta3 ~ eta2 0.3052 0.0744 4.1021 0.0000 [ 0.1922; 0.4322 ]
#>
#> Estimated loadings:
#> ===================
#> CI_percentile
#> Loading Estimate Std. error t-stat. p-value 95%
#> eta1 =~ y11 0.6631 0.0425 15.5928 0.0000 [ 0.5901; 0.7404 ]
#> eta1 =~ y12 0.6493 0.0378 17.1949 0.0000 [ 0.5628; 0.6938 ]
#> eta1 =~ y13 0.7613 0.0319 23.8413 0.0000 [ 0.7195; 0.8351 ]
#> eta2 =~ y21 0.5165 0.0502 10.2855 0.0000 [ 0.4553; 0.6171 ]
#> eta2 =~ y22 0.7554 0.0403 18.7562 0.0000 [ 0.6851; 0.8301 ]
#> eta2 =~ y23 0.7997 0.0419 19.1059 0.0000 [ 0.7210; 0.8693 ]
#> eta3 =~ y31 0.8223 0.0362 22.7154 0.0000 [ 0.7326; 0.8719 ]
#> eta3 =~ y32 0.6581 0.0385 17.1045 0.0000 [ 0.5722; 0.7189 ]
#> eta3 =~ y33 0.7474 0.0360 20.7739 0.0000 [ 0.6836; 0.8137 ]
#>
#> Estimated weights:
#> ==================
#> CI_percentile
#> Weight Estimate Std. error t-stat. p-value 95%
#> eta1 <~ y11 0.3956 0.0228 17.3629 0.0000 [ 0.3504; 0.4316 ]
#> eta1 <~ y12 0.3873 0.0218 17.8044 0.0000 [ 0.3468; 0.4335 ]
#> eta1 <~ y13 0.4542 0.0175 26.0223 0.0000 [ 0.4240; 0.4882 ]
#> eta2 <~ y21 0.3058 0.0267 11.4340 0.0000 [ 0.2677; 0.3584 ]
#> eta2 <~ y22 0.4473 0.0235 18.9994 0.0000 [ 0.4079; 0.4928 ]
#> eta2 <~ y23 0.4735 0.0218 21.6727 0.0000 [ 0.4334; 0.5180 ]
#> eta3 <~ y31 0.4400 0.0179 24.5546 0.0000 [ 0.4071; 0.4653 ]
#> eta3 <~ y32 0.3521 0.0168 20.9882 0.0000 [ 0.3159; 0.3819 ]
#> eta3 <~ y33 0.3999 0.0211 18.9150 0.0000 [ 0.3709; 0.4452 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_percentile
#> Total effect Estimate Std. error t-stat. p-value 95%
#> eta2 ~ eta1 0.6713 0.0490 13.7030 0.0000 [ 0.5860; 0.7444 ]
#> eta3 ~ eta1 0.6634 0.0395 16.7995 0.0000 [ 0.5751; 0.7453 ]
#> eta3 ~ eta2 0.3052 0.0744 4.1021 0.0000 [ 0.1922; 0.4322 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_percentile
#> Indirect effect Estimate Std. error t-stat. p-value 95%
#> eta3 ~ eta1 0.2049 0.0524 3.9128 0.0001 [ 0.1324; 0.2965 ]
#> ________________________________________________________________________________
# 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 0.04252413 15.59279 8.149530e-55
#> 2 eta1 =~ y12 Common factor 0.6492779 0.03775981 17.19495 2.897233e-66
#> 3 eta1 =~ y13 Common factor 0.7613458 0.03193385 23.84134 1.245328e-125
#> 4 eta2 =~ y21 Common factor 0.5164548 0.05021180 10.28553 8.189341e-25
#> 5 eta2 =~ y22 Common factor 0.7553877 0.04027400 18.75621 1.722407e-78
#> 6 eta2 =~ y23 Common factor 0.7996637 0.04185434 19.10588 2.256130e-81
#> 7 eta3 =~ y31 Common factor 0.8222773 0.03619915 22.71537 3.157635e-114
#> 8 eta3 =~ y32 Common factor 0.6580689 0.03847348 17.10448 1.374187e-65
#> 9 eta3 =~ y33 Common factor 0.7474241 0.03597900 20.77390 7.455222e-96
#> CI_percentile.95%L CI_percentile.95%U
#> 1 0.5901045 0.7404429
#> 2 0.5628051 0.6937956
#> 3 0.7195227 0.8351205
#> 4 0.4552694 0.6170906
#> 5 0.6851124 0.8300545
#> 6 0.7209759 0.8693144
#> 7 0.7325563 0.8718536
#> 8 0.5722481 0.7189005
#> 9 0.6836148 0.8137411
## 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 = -565493332
#>
#> 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 0.0490 13.7030 0.0000 [ 0.5410; 0.7944 ]
#> eta3 ~ eta1 0.4585 0.0788 5.8194 0.0000 [ 0.2589; 0.6664 ]
#> eta3 ~ eta2 0.3052 0.0744 4.1021 0.0000 [ 0.1121; 0.4968 ]
#>
#> Estimated loadings:
#> ===================
#> CI_standard_t
#> Loading Estimate Std. error t-stat. p-value 99%
#> eta1 =~ y11 0.6631 0.0425 15.5928 0.0000 [ 0.5565; 0.7764 ]
#> eta1 =~ y12 0.6493 0.0378 17.1949 0.0000 [ 0.5498; 0.7451 ]
#> eta1 =~ y13 0.7613 0.0319 23.8413 0.0000 [ 0.6666; 0.8317 ]
#> eta2 =~ y21 0.5165 0.0502 10.2855 0.0000 [ 0.3762; 0.6358 ]
#> eta2 =~ y22 0.7554 0.0403 18.7562 0.0000 [ 0.6574; 0.8657 ]
#> eta2 =~ y23 0.7997 0.0419 19.1059 0.0000 [ 0.6949; 0.9113 ]
#> eta3 =~ y31 0.8223 0.0362 22.7154 0.0000 [ 0.7355; 0.9227 ]
#> eta3 =~ y32 0.6581 0.0385 17.1045 0.0000 [ 0.5611; 0.7600 ]
#> eta3 =~ y33 0.7474 0.0360 20.7739 0.0000 [ 0.6562; 0.8423 ]
#>
#> Estimated weights:
#> ==================
#> CI_standard_t
#> Weight Estimate Std. error t-stat. p-value 99%
#> eta1 <~ y11 0.3956 0.0228 17.3629 0.0000 [ 0.3421; 0.4599 ]
#> eta1 <~ y12 0.3873 0.0218 17.8044 0.0000 [ 0.3333; 0.4458 ]
#> eta1 <~ y13 0.4542 0.0175 26.0223 0.0000 [ 0.4056; 0.4959 ]
#> eta2 <~ y21 0.3058 0.0267 11.4340 0.0000 [ 0.2307; 0.3690 ]
#> eta2 <~ y22 0.4473 0.0235 18.9994 0.0000 [ 0.3901; 0.5118 ]
#> eta2 <~ y23 0.4735 0.0218 21.6727 0.0000 [ 0.4191; 0.5321 ]
#> eta3 <~ y31 0.4400 0.0179 24.5546 0.0000 [ 0.3941; 0.4868 ]
#> eta3 <~ y32 0.3521 0.0168 20.9882 0.0000 [ 0.3077; 0.3944 ]
#> eta3 <~ y33 0.3999 0.0211 18.9150 0.0000 [ 0.3432; 0.4525 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_standard_t
#> Total effect Estimate Std. error t-stat. p-value 99%
#> eta2 ~ eta1 0.6713 0.0490 13.7030 0.0000 [ 0.5410; 0.7944 ]
#> eta3 ~ eta1 0.6634 0.0395 16.7995 0.0000 [ 0.5636; 0.7678 ]
#> eta3 ~ eta2 0.3052 0.0744 4.1021 0.0000 [ 0.1121; 0.4968 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_standard_t
#> Indirect effect Estimate Std. error t-stat. p-value 99%
#> eta3 ~ eta1 0.2049 0.0524 3.9128 0.0001 [ 0.0677; 0.3385 ]
#> ________________________________________________________________________________
# 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 0.04899161 13.70303 9.737505e-43
#> 2 eta3 ~ eta1 Common factor 0.4585068 0.07878895 5.81943 5.904860e-09
#> 3 eta3 ~ eta2 Common factor 0.3051511 0.07438900 4.10210 4.094168e-05
#> CI_standard_t.99%L CI_standard_t.99%U CI_standard_t.95%L CI_standard_t.95%U
#> 1 0.5410079 0.7943649 0.5714311 0.7639416
#> 2 0.2589001 0.6663520 0.3078271 0.6174250
#> 3 0.1121268 0.4968247 0.1583215 0.4506300
#> CI_percentile.99%L CI_percentile.99%U CI_percentile.95%L CI_percentile.95%U
#> 1 0.5416007 0.7482282 0.5860081 0.7443534
#> 2 0.2795634 0.6212660 0.3140517 0.5952637
#> 3 0.1467875 0.4547769 0.1922014 0.4322451