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.
It is possible to estimate models with second order construcst with
the pls() function, using the two-stage approach. Here we
see an example using the TPB_2SO dataset, from the
modsem package. The model below contains two second order
latent variables, INT (intention) which is a second order
latent variable of ATT (attitude) and SN
(subjective norm), and PBC (perceived behavioural control)
which is a second order latent variable of PC (perceived
control) and PB (perceived behaviour).
library(modsem)
#> This is modsem (1.0.20). Please report any bugs!
#>
#> Attaching package: 'modsem'
#> The following object is masked from 'package:plssem':
#>
#> parameter_estimates
tpb_2so <- '
# First order latent variables
ATT =~ att1 + att2 + att3
SN =~ sn1 + sn2 + sn3
PB =~ pb1 + pb2 + pb3
PC =~ pc1 + pc2 + pc3
BEH =~ b1 + b2
# Higher order latent variables
INT =~ ATT + SN
PBC =~ PC + PB
# Structural model
BEH ~ PBC + INT + INT:PBC
'
fit <- pls(tpb_2so, data = TPB_2SO, bootstrap = TRUE, boot.R = 50)
summary(fit)
#> plssem (0.1.2) ended normally after 5 iterations
#> Estimator PLSc
#> Link LINEAR
#>
#> Number of observations 2000
#> Number of iterations 5
#> Number of latent variables 7
#> Number of observed variables 14
#>
#> Fit Measures:
#> Chi-Square 179.935
#> Degrees of Freedom 70
#> SRMR 0.010
#> RMSEA 0.028
#>
#> R-squared (indicators):
#> att1 0.907
#> att2 0.879
#> att3 0.842
#> sn1 0.818
#> sn2 0.786
#> sn3 0.729
#> pb1 0.894
#> pb2 0.866
#> pb3 0.820
#> pc1 0.938
#> pc2 0.848
#> pc3 0.894
#>
#> R-squared (latents):
#> ATT 0.735
#> SN 0.605
#> PC 0.665
#> PB 0.423
#> BEH 0.198
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> ATT =~
#> att1 0.952 0.008 124.706 0.000
#> att2 0.937 0.007 125.141 0.000
#> att3 0.918 0.009 100.061 0.000
#> SN =~
#> sn1 0.904 0.009 100.818 0.000
#> sn2 0.886 0.011 83.952 0.000
#> sn3 0.854 0.009 91.111 0.000
#> PB =~
#> pb1 0.946 0.010 91.965 0.000
#> pb2 0.931 0.013 74.335 0.000
#> pb3 0.906 0.013 72.012 0.000
#> PC =~
#> pc1 0.969 0.009 106.231 0.000
#> pc2 0.921 0.009 98.812 0.000
#> pc3 0.945 0.011 84.821 0.000
#> INT =~
#> ATT 0.877 0.040 21.956 0.000
#> SN 0.814 0.040 20.151 0.000
#> PBC =~
#> PC 0.831 0.052 15.854 0.000
#> PB 0.668 0.044 15.300 0.000
#>
#> Composites:
#> Estimate Std.Error z.value P(>|z|)
#> BEH <~
#> b1 0.913 0.021 43.330 0.000
#> b2 0.847 0.023 37.592 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> BEH ~
#> INT 0.251 0.020 12.578 0.000
#> PBC 0.289 0.026 11.147 0.000
#> INT:PBC 0.250 0.028 9.091 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> INT ~~
#> PBC 0.035 0.026 1.327 0.185
#> INT:PBC -0.006 0.048 -0.123 0.902
#> PBC ~~
#> INT:PBC -0.108 0.058 -1.863 0.062
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> INT 1.000
#> PBC 1.000
#> .BEH 0.802 0.023 35.569 0.000
#> INT:PBC 1.006 0.072 14.037 0.000
#> .att1 0.093 0.015 6.376 0.000
#> .att2 0.121 0.014 8.652 0.000
#> .att3 0.158 0.017 9.381 0.000
#> .sn1 0.182 0.016 11.221 0.000
#> .sn2 0.214 0.019 11.433 0.000
#> .sn3 0.271 0.016 16.947 0.000
#> .pb1 0.106 0.020 5.390 0.000
#> .pb2 0.134 0.023 5.743 0.000
#> .pb3 0.180 0.023 7.887 0.000
#> .pc1 0.062 0.018 3.495 0.000
#> .pc2 0.152 0.017 8.880 0.000
#> .pc3 0.106 0.021 5.044 0.000
#> b1 0.167 0.038 4.338 0.000
#> b2 0.283 0.038 7.408 0.000
#> .ATT 0.265 0.062 4.240 0.000
#> .SN 0.395 0.049 8.121 0.000
#> .PC 0.335 0.085 3.949 0.000
#> .PB 0.577 0.054 10.732 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.