A simulated dataset.
# \donttest{
syntax <- '
f =~ y1 + y2 + y3
f ~ rv(s1)*x1 + rv(s2)*x2 + x3
'
fit <- pls(syntax, data = randomSlopesOrdered, cluster = "cluster")
summary(fit)
#> plssem (0.2.0) ended normally after 2 iterations
#> Estimator OrdPLS
#> Link PROBIT
#>
#> Number of observations 3000
#> Number of iterations 2
#> Number of latent variables 1
#> Number of observed variables 6
#>
#> Fit Measures:
#> Chi-Square 554.042
#> Degrees of Freedom 6
#> SRMR 0.056
#> RMSEA 0.175
#>
#> R-squared [indicators]:
#> y1 0.751
#> y2 0.621
#> y3 0.750
#>
#> R-squared [latents]:
#> f 0.167
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> f =~
#> y1 0.867
#> y2 0.788
#> y3 0.866
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> f ~
#> x1 0.277
#> x2 0.251
#> x3 0.089
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> x1 ~~
#> x2 -0.016
#> x3 0.005
#> x2 ~~
#> x3 0.010
#>
#> Thresholds:
#> Estimate Std.Error z.value P(>|z|)
#> y1|t1 -2.713
#> y1|t2 -1.758
#> y1|t3 -0.435
#> y1|t4 0.342
#> y1|t5 1.319
#> y1|t6 2.366
#> y2|t1 -3.004
#> y2|t2 -1.906
#> y2|t3 -0.981
#> y2|t4 0.208
#> y2|t5 1.024
#> y2|t6 2.197
#> y3|t1 -2.207
#> y3|t2 -1.265
#> y3|t3 0.008
#> y3|t4 0.699
#> y3|t5 1.825
#> y3|t6 2.681
#> x1|t1 -2.352
#> x1|t2 -1.341
#> x1|t3 -0.663
#> x1|t4 0.485
#> x1|t5 1.504
#> x1|t6 2.713
#> x2|t1 -2.748
#> x2|t2 -1.972
#> x2|t3 -1.018
#> x2|t4 -0.122
#> x2|t5 0.798
#> x2|t6 2.008
#> x3|t1 -3.209
#> x3|t2 -2.014
#> x3|t3 -0.851
#> x3|t4 -0.148
#> x3|t5 1.008
#> x3|t6 2.144
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .f 0.833
#> x1 1.000
#> x2 1.000
#> x3 1.000
#> .y1 0.249
#> .y2 0.379
#> .y3 0.250
#>
# }