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,
sample = 50
)
summary(fit_tpb)
#> plssem (0.1.0) ended normally after 3 iterations
#>
#> Estimator PLSc
#> Link LINEAR
#>
#> Number of observations 2000
#> Number of iterations 3
#> Number of latent variables 5
#> Number of observed variables 15
#>
#> 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 0.013 72.312 0.000
#> att2 0.908 0.015 61.880 0.000
#> att3 0.897 0.015 58.854 0.000
#> att4 0.863 0.017 51.331 0.000
#> att5 0.919 0.015 62.925 0.000
#> SN =~
#> sn1 0.904 0.010 89.350 0.000
#> sn2 0.929 0.011 84.809 0.000
#> PBC =~
#> pbc1 0.925 0.012 79.987 0.000
#> pbc2 0.927 0.012 75.373 0.000
#> pbc3 0.887 0.012 71.665 0.000
#> INT =~
#> int1 0.903 0.011 85.575 0.000
#> int2 0.909 0.012 74.604 0.000
#> int3 0.861 0.013 66.596 0.000
#> BEH =~
#> b1 0.873 0.016 53.012 0.000
#> b2 0.906 0.018 50.421 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> INT ~
#> ATT 0.243 0.028 8.747 0.000
#> SN 0.201 0.031 6.491 0.000
#> PBC 0.240 0.030 7.870 0.000
#> BEH ~
#> PBC 0.308 0.027 11.548 0.000
#> INT 0.210 0.028 7.597 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> ATT ~~
#> SN 0.633 0.014 43.787 0.000
#> PBC 0.692 0.012 56.523 0.000
#> SN ~~
#> PBC 0.696 0.014 50.524 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> ATT 1.000
#> SN 1.000
#> PBC 1.000
#> .INT 0.633 0.021 30.509 0.000
#> .BEH 0.790 0.022 35.866 0.000
#> .att1 0.153 0.023 6.521 0.000
#> .att2 0.175 0.027 6.584 0.000
#> .att3 0.195 0.027 7.154 0.000
#> .att4 0.255 0.029 8.789 0.000
#> .att5 0.155 0.027 5.741 0.000
#> .sn1 0.183 0.018 9.987 0.000
#> .sn2 0.137 0.020 6.727 0.000
#> .pbc1 0.144 0.021 6.696 0.000
#> .pbc2 0.141 0.023 6.202 0.000
#> .pbc3 0.213 0.022 9.686 0.000
#> .int1 0.184 0.019 9.658 0.000
#> .int2 0.173 0.022 7.852 0.000
#> .int3 0.258 0.022 11.561 0.000
#> .b1 0.238 0.029 8.239 0.000
#> .b2 0.179 0.033 5.492 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.