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)...
#> Warning: Calculation of fit measures failed, message:
#> argument "parTable" is missing, with no default
#> plssem (0.1.2) ended normally after 53 iterations
#>
#> Estimator MCOrdPLSc
#> Link PROBIT
#>
#> Number of observations 2000
#> Number of iterations 53
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square NA
#> Degrees of Freedom NA
#> SRMR NA
#> RMSEA NA
#>
#> R-squared (indicators):
#> x1 0.930
#> x2 0.900
#> x3 0.905
#> z1 0.935
#> z2 0.901
#> z3 0.912
#> y1 0.972
#> y2 0.952
#> y3 0.962
#>
#> R-squared (latents):
#> Y 0.564
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.930
#> x2 0.900
#> x3 0.905
#> Z =~
#> z1 0.935
#> z2 0.901
#> z3 0.912
#> Y =~
#> y1 0.972
#> y2 0.952
#> y3 0.962
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.416
#> Z 0.358
#> X:Z 0.448
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.194
#> X:Z 0.013
#> Z ~~
#> X:Z 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.436
#> X:Z 1.000
#> .x1 0.070
#> .x2 0.100
#> .x3 0.095
#> .z1 0.065
#> .z2 0.099
#> .z3 0.088
#> .y1 0.028
#> .y2 0.048
#> .y3 0.038
#>