The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
This vignette demonstrates how to estimate a traditional linear PLS-SEM using continuous indicators.
tpb <- '
# Outer Model (Based on Hagger et al., 2007)
ATT =~ att1 + att2 + att3 + att4 + att5
SN =~ sn1 + sn2
PBC =~ pbc1 + pbc2 + pbc3
INT =~ int1 + int2 + int3
BEH =~ b1 + b2
# Inner Model (Based on Steinmetz et al., 2011)
INT ~ ATT + SN + PBC
BEH ~ INT + PBC
'fit_tpb <- pls(
tpb,
data = modsem::TPB,
bootstrap = TRUE,
boot.R = 50
)
summary(fit_tpb)
#> plssem (0.1.1) ended normally after 3 iterations
#>
#> Estimator PLSc
#> Link PROBIT
#>
#> Number of observations 2000
#> Number of iterations 3
#> Number of latent variables 5
#> Number of observed variables 15
#>
#> Fit Measures:
#> Chi-Square 106.316
#> Degrees of Freedom 82
#> SRMR 0.008
#> RMSEA 0.012
#>
#> R-squared (indicators):
#> att1 0.847
#> att2 0.825
#> att3 0.805
#> att4 0.745
#> att5 0.845
#> sn1 0.817
#> sn2 0.863
#> pbc1 0.856
#> pbc2 0.859
#> pbc3 0.787
#> int1 0.816
#> int2 0.827
#> int3 0.742
#> b1 0.762
#> b2 0.821
#>
#> R-squared (latents):
#> INT 0.367
#> BEH 0.210
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> ATT =~
#> att1 0.921 0.014 63.649 0.000
#> att2 0.908 0.019 48.805 0.000
#> att3 0.897 0.018 49.160 0.000
#> att4 0.863 0.016 55.429 0.000
#> att5 0.919 0.017 53.038 0.000
#> SN =~
#> sn1 0.904 0.013 70.784 0.000
#> sn2 0.929 0.011 86.653 0.000
#> PBC =~
#> pbc1 0.925 0.010 91.495 0.000
#> pbc2 0.927 0.011 81.877 0.000
#> pbc3 0.887 0.011 77.623 0.000
#> INT =~
#> int1 0.903 0.012 77.663 0.000
#> int2 0.909 0.013 67.504 0.000
#> int3 0.861 0.012 70.663 0.000
#> BEH =~
#> b1 0.873 0.019 46.302 0.000
#> b2 0.906 0.017 54.914 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> INT ~
#> ATT 0.243 0.028 8.681 0.000
#> SN 0.201 0.028 7.157 0.000
#> PBC 0.240 0.033 7.186 0.000
#> BEH ~
#> PBC 0.308 0.029 10.514 0.000
#> INT 0.210 0.029 7.258 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> ATT ~~
#> SN 0.633 0.013 47.657 0.000
#> PBC 0.692 0.012 59.871 0.000
#> SN ~~
#> PBC 0.696 0.013 52.366 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> ATT 1.000
#> SN 1.000
#> PBC 1.000
#> .INT 0.633 0.019 32.845 0.000
#> .BEH 0.790 0.018 43.350 0.000
#> .att1 0.153 0.027 5.709 0.000
#> .att2 0.175 0.034 5.195 0.000
#> .att3 0.195 0.033 5.933 0.000
#> .att4 0.255 0.027 9.414 0.000
#> .att5 0.155 0.032 4.868 0.000
#> .sn1 0.183 0.023 7.917 0.000
#> .sn2 0.137 0.020 6.851 0.000
#> .pbc1 0.144 0.019 7.661 0.000
#> .pbc2 0.141 0.021 6.706 0.000
#> .pbc3 0.213 0.020 10.499 0.000
#> .int1 0.184 0.021 8.746 0.000
#> .int2 0.173 0.025 7.062 0.000
#> .int3 0.258 0.021 12.366 0.000
#> .b1 0.238 0.033 7.248 0.000
#> .b2 0.179 0.030 5.944 0.000These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.