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 = 500
)
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 71.473 0.000
#> att2 0.908 0.014 63.104 0.000
#> att3 0.897 0.017 51.518 0.000
#> att4 0.863 0.019 46.190 0.000
#> att5 0.919 0.015 61.775 0.000
#> SN =~
#> sn1 0.904 0.012 72.699 0.000
#> sn2 0.929 0.012 77.041 0.000
#> PBC =~
#> pbc1 0.925 0.011 82.157 0.000
#> pbc2 0.927 0.012 79.213 0.000
#> pbc3 0.887 0.013 69.598 0.000
#> INT =~
#> int1 0.903 0.011 81.911 0.000
#> int2 0.909 0.012 78.758 0.000
#> int3 0.861 0.013 68.127 0.000
#> BEH =~
#> b1 0.873 0.015 58.618 0.000
#> b2 0.906 0.014 63.946 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> INT ~
#> ATT 0.243 0.030 8.099 0.000
#> SN 0.201 0.030 6.712 0.000
#> PBC 0.240 0.033 7.337 0.000
#> BEH ~
#> PBC 0.308 0.025 12.263 0.000
#> INT 0.210 0.027 7.792 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> ATT ~~
#> SN 0.633 0.014 44.370 0.000
#> PBC 0.692 0.012 55.931 0.000
#> SN ~~
#> PBC 0.696 0.014 51.437 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> ATT 1.000
#> SN 1.000
#> PBC 1.000
#> .INT 0.633 0.018 34.319 0.000
#> .BEH 0.790 0.019 40.930 0.000
#> .att1 0.153 0.024 6.443 0.000
#> .att2 0.175 0.026 6.714 0.000
#> .att3 0.195 0.031 6.228 0.000
#> .att4 0.255 0.032 7.902 0.000
#> .att5 0.155 0.027 5.683 0.000
#> .sn1 0.183 0.022 8.143 0.000
#> .sn2 0.137 0.022 6.083 0.000
#> .pbc1 0.144 0.021 6.910 0.000
#> .pbc2 0.141 0.022 6.512 0.000
#> .pbc3 0.213 0.023 9.413 0.000
#> .int1 0.184 0.020 9.245 0.000
#> .int2 0.173 0.021 8.253 0.000
#> .int3 0.258 0.022 11.885 0.000
#> .b1 0.238 0.026 9.138 0.000
#> .b2 0.179 0.026 6.948 0.000