stable
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 = 397658049
#>
#> 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 0.0468 14.3447 0.0000 [ 0.5717; 0.7616 ]
#> eta3 ~ eta1 0.4585 0.0878 5.2214 0.0000 [ 0.2839; 0.5585 ]
#> eta3 ~ eta2 0.3052 0.0924 3.3011 0.0010 [ 0.1670; 0.4823 ]
#>
#> Estimated loadings:
#> ===================
#> CI_percentile
#> Loading Estimate Std. error t-stat. p-value 95%
#> eta1 =~ y11 0.6631 0.0440 15.0858 0.0000 [ 0.5978; 0.7535 ]
#> eta1 =~ y12 0.6493 0.0354 18.3259 0.0000 [ 0.5833; 0.6981 ]
#> eta1 =~ y13 0.7613 0.0411 18.5323 0.0000 [ 0.6834; 0.8194 ]
#> eta2 =~ y21 0.5165 0.0422 12.2258 0.0000 [ 0.4580; 0.6130 ]
#> eta2 =~ y22 0.7554 0.0291 25.9477 0.0000 [ 0.6724; 0.7898 ]
#> eta2 =~ y23 0.7997 0.0367 21.7679 0.0000 [ 0.7347; 0.8605 ]
#> eta3 =~ y31 0.8223 0.0333 24.7060 0.0000 [ 0.7548; 0.8805 ]
#> eta3 =~ y32 0.6581 0.0453 14.5296 0.0000 [ 0.5527; 0.7363 ]
#> eta3 =~ y33 0.7474 0.0401 18.6346 0.0000 [ 0.6766; 0.8217 ]
#>
#> Estimated weights:
#> ==================
#> CI_percentile
#> Weight Estimate Std. error t-stat. p-value 95%
#> eta1 <~ y11 0.3956 0.0264 14.9987 0.0000 [ 0.3592; 0.4494 ]
#> eta1 <~ y12 0.3873 0.0181 21.3752 0.0000 [ 0.3505; 0.4185 ]
#> eta1 <~ y13 0.4542 0.0209 21.7098 0.0000 [ 0.4099; 0.4828 ]
#> eta2 <~ y21 0.3058 0.0223 13.7439 0.0000 [ 0.2772; 0.3704 ]
#> eta2 <~ y22 0.4473 0.0202 22.1906 0.0000 [ 0.4159; 0.4873 ]
#> eta2 <~ y23 0.4735 0.0182 25.9943 0.0000 [ 0.4387; 0.4993 ]
#> eta3 <~ y31 0.4400 0.0196 22.4588 0.0000 [ 0.4039; 0.4717 ]
#> eta3 <~ y32 0.3521 0.0214 16.4759 0.0000 [ 0.3018; 0.3956 ]
#> eta3 <~ y33 0.3999 0.0200 20.0095 0.0000 [ 0.3668; 0.4426 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_percentile
#> Total effect Estimate Std. error t-stat. p-value 95%
#> eta2 ~ eta1 0.6713 0.0468 14.3447 0.0000 [ 0.5717; 0.7616 ]
#> eta3 ~ eta1 0.6634 0.0365 18.1712 0.0000 [ 0.5847; 0.7041 ]
#> eta3 ~ eta2 0.3052 0.0924 3.3011 0.0010 [ 0.1670; 0.4823 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_percentile
#> Indirect effect Estimate Std. error t-stat. p-value 95%
#> eta3 ~ eta1 0.2049 0.0614 3.3387 0.0008 [ 0.1176; 0.3250 ]
#> ________________________________________________________________________________
# 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 0.04395315 15.08583 2.007242e-51
#> 2 eta1 =~ y12 Common factor 0.6492779 0.03542948 18.32593 5.139947e-75
#> 3 eta1 =~ y13 Common factor 0.7613458 0.04108204 18.53233 1.132793e-76
#> 4 eta2 =~ y21 Common factor 0.5164548 0.04224290 12.22584 2.262417e-34
#> 5 eta2 =~ y22 Common factor 0.7553877 0.02911197 25.94767 1.931865e-148
#> 6 eta2 =~ y23 Common factor 0.7996637 0.03673593 21.76789 4.676380e-105
#> 7 eta3 =~ y31 Common factor 0.8222773 0.03328255 24.70596 9.228959e-135
#> 8 eta3 =~ y32 Common factor 0.6580689 0.04529153 14.52962 7.865053e-48
#> 9 eta3 =~ y33 Common factor 0.7474241 0.04010953 18.63458 1.684835e-77
#> CI_percentile.95%L CI_percentile.95%U
#> 1 0.5977550 0.7534560
#> 2 0.5833276 0.6981269
#> 3 0.6833655 0.8193574
#> 4 0.4579580 0.6130144
#> 5 0.6724143 0.7897722
#> 6 0.7347326 0.8604703
#> 7 0.7547625 0.8804972
#> 8 0.5527013 0.7362800
#> 9 0.6765880 0.8217103
## 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 = 397658049
#>
#> 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 0.0468 14.3447 0.0000 [ 0.5468; 0.7888 ]
#> eta3 ~ eta1 0.4585 0.0878 5.2214 0.0000 [ 0.2534; 0.7075 ]
#> eta3 ~ eta2 0.3052 0.0924 3.3011 0.0010 [ 0.0435; 0.5216 ]
#>
#> Estimated loadings:
#> ===================
#> CI_standard_t
#> Loading Estimate Std. error t-stat. p-value 99%
#> eta1 =~ y11 0.6631 0.0440 15.0858 0.0000 [ 0.5407; 0.7680 ]
#> eta1 =~ y12 0.6493 0.0354 18.3259 0.0000 [ 0.5626; 0.7459 ]
#> eta1 =~ y13 0.7613 0.0411 18.5323 0.0000 [ 0.6560; 0.8685 ]
#> eta2 =~ y21 0.5165 0.0422 12.2258 0.0000 [ 0.4002; 0.6187 ]
#> eta2 =~ y22 0.7554 0.0291 25.9477 0.0000 [ 0.6852; 0.8357 ]
#> eta2 =~ y23 0.7997 0.0367 21.7679 0.0000 [ 0.7198; 0.9098 ]
#> eta3 =~ y31 0.8223 0.0333 24.7060 0.0000 [ 0.7405; 0.9126 ]
#> eta3 =~ y32 0.6581 0.0453 14.5296 0.0000 [ 0.5416; 0.7759 ]
#> eta3 =~ y33 0.7474 0.0401 18.6346 0.0000 [ 0.6366; 0.8440 ]
#>
#> Estimated weights:
#> ==================
#> CI_standard_t
#> Weight Estimate Std. error t-stat. p-value 99%
#> eta1 <~ y11 0.3956 0.0264 14.9987 0.0000 [ 0.3231; 0.4595 ]
#> eta1 <~ y12 0.3873 0.0181 21.3752 0.0000 [ 0.3445; 0.4382 ]
#> eta1 <~ y13 0.4542 0.0209 21.7098 0.0000 [ 0.4019; 0.5101 ]
#> eta2 <~ y21 0.3058 0.0223 13.7439 0.0000 [ 0.2411; 0.3562 ]
#> eta2 <~ y22 0.4473 0.0202 22.1906 0.0000 [ 0.3935; 0.4977 ]
#> eta2 <~ y23 0.4735 0.0182 25.9943 0.0000 [ 0.4307; 0.5249 ]
#> eta3 <~ y31 0.4400 0.0196 22.4588 0.0000 [ 0.3924; 0.4937 ]
#> eta3 <~ y32 0.3521 0.0214 16.4759 0.0000 [ 0.2981; 0.4086 ]
#> eta3 <~ y33 0.3999 0.0200 20.0095 0.0000 [ 0.3453; 0.4487 ]
#>
#> ------------------------------------ Effects -----------------------------------
#>
#> Estimated total effects:
#> ========================
#> CI_standard_t
#> Total effect Estimate Std. error t-stat. p-value 99%
#> eta2 ~ eta1 0.6713 0.0468 14.3447 0.0000 [ 0.5468; 0.7888 ]
#> eta3 ~ eta1 0.6634 0.0365 18.1712 0.0000 [ 0.5755; 0.7643 ]
#> eta3 ~ eta2 0.3052 0.0924 3.3011 0.0010 [ 0.0435; 0.5216 ]
#>
#> Estimated indirect effects:
#> ===========================
#> CI_standard_t
#> Indirect effect Estimate Std. error t-stat. p-value 99%
#> eta3 ~ eta1 0.2049 0.0614 3.3387 0.0008 [ 0.0308; 0.3481 ]
#> ________________________________________________________________________________
# 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 0.04680008 14.344706 1.149867e-46
#> 2 eta3 ~ eta1 Common factor 0.4585068 0.08781266 5.221420 1.775566e-07
#> 3 eta3 ~ eta2 Common factor 0.3051511 0.09243853 3.301125 9.629803e-04
#> CI_standard_t.99%L CI_standard_t.99%U CI_standard_t.95%L CI_standard_t.95%U
#> 1 0.54677081 0.7887944 0.5758331 0.7597321
#> 2 0.25337746 0.7074950 0.3079081 0.6529643
#> 3 0.04351151 0.5215514 0.1009148 0.4641481
#> CI_percentile.99%L CI_percentile.99%U CI_percentile.95%L CI_percentile.95%U
#> 1 0.5654905 0.7772779 0.5717030 0.7616300
#> 2 0.2643837 0.5729385 0.2838706 0.5585133
#> 3 0.1584966 0.4888904 0.1670366 0.4823349