A simulated dataset.
m <- '
X =~ x1 + x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
Y ~ X + Z + X:Z
'
fit <- pls(m, oneIntOrdered)
summary(fit)
#> Warning: Fit measures for MC-PLSc models are under development!
#> Traditional fit criteria will likely be too strict.
#> Resampling MC-PLSc Model (R = 1000000)...
#> plssem (0.1.2) ended normally after 15 iterations
#> Estimator MCOrdPLSc
#> Link PROBIT
#>
#> Number of observations 2000
#> Number of iterations 15
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 20.706
#> Degrees of Freedom 21
#> SRMR 0.011
#> RMSEA 0.000
#>
#> R-squared (indicators):
#> x1 0.931
#> x2 0.899
#> x3 0.906
#> z1 0.936
#> z2 0.903
#> z3 0.912
#> y1 0.971
#> y2 0.953
#> y3 0.962
#>
#> R-squared (latents):
#> Y 0.551
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.931
#> x2 0.899
#> x3 0.906
#> Z =~
#> z1 0.936
#> z2 0.903
#> z3 0.912
#> Y =~
#> y1 0.971
#> y2 0.953
#> y3 0.962
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.417
#> Z 0.358
#> X:Z 0.448
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.192
#> X:Z -0.012
#> Z ~~
#> X:Z -0.013
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.449
#> X:Z 1.000
#> .x1 0.069
#> .x2 0.101
#> .x3 0.094
#> .z1 0.064
#> .z2 0.097
#> .z3 0.088
#> .y1 0.029
#> .y2 0.047
#> .y3 0.038
#>