A simulated dataset.
# \donttest{
syntax <- '
f =~ y1 + y2 + y3
f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'
fit <- pls(syntax, data = randomInterceptsOrdered)
#> Warning: plssem->mcpls():
#> Base fit is inadmissible! The MC-PLS algorithm might not converge to a
#> proper solution!
summary(fit)
#> plssem->fitMeasures():
#> Fit measures for MC-PLSc models are under development! Traditional fit
#> criteria will likely be too strict.
#> plssem->fitMeasures():
#> Resampling MC-PLSc Model (R = 1000000)...
#> plssem (0.1.4) did NOT END NORMALLY after 60 iterations
#> Estimator MCOrdPLSc-MLM
#> Link PROBIT
#>
#> Number of observations 10000
#> Number of iterations 60
#> Number of latent variables 1
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 9.692
#> Degrees of Freedom 10
#> SRMR 0.003
#> RMSEA 0.000
#>
#> R-squared (indicators):
#> y1 0.876
#> y2 0.785
#> y3 0.819
#>
#> R-squared (latents):
#> f 0.123
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> f =~
#> y1 0.936
#> y2 0.886
#> y3 0.905
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> f ~
#> x1 0.241
#> x2 0.161
#> x3 0.080
#> w1 0.122
#> w2 0.077
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> x1 ~~
#> x2 0.110
#> x3 0.011
#> w1 0.002
#> w2 -0.001
#> x2 ~~
#> x3 0.099
#> w1 0.000
#> w2 0.002
#> x3 ~~
#> w1 -0.002
#> w2 0.003
#> w1 ~~
#> w2 -0.027
#>
#> Thresholds:
#> Estimate Std.Error z.value P(>|z|)
#> y1|t1 -2.593
#> y1|t2 -1.768
#> y1|t3 -0.422
#> y1|t4 0.318
#> y1|t5 1.327
#> y1|t6 2.365
#> y2|t1 -2.912
#> y2|t2 -1.889
#> y2|t3 -0.983
#> y2|t4 0.178
#> y2|t5 1.036
#> y2|t6 2.219
#> y3|t1 -2.244
#> y3|t2 -1.262
#> y3|t3 0.006
#> y3|t4 0.686
#> y3|t5 1.805
#> y3|t6 2.889
#> x1|t1 -2.360
#> x1|t2 -1.349
#> x1|t3 -0.672
#> x1|t4 0.493
#> x1|t5 1.503
#> x1|t6 2.570
#> x2|t1 -2.900
#> x2|t2 -2.023
#> x2|t3 -1.015
#> x2|t4 -0.110
#> x2|t5 0.809
#> x2|t6 1.905
#> x3|t1 -2.857
#> x3|t2 -1.970
#> x3|t3 -0.842
#> x3|t4 -0.143
#> x3|t5 1.007
#> x3|t6 2.222
#> w1|t1 -2.796
#> w1|t2 -2.021
#> w1|t3 -0.963
#> w1|t4 0.122
#> w1|t5 1.243
#> w1|t6 2.552
#> w2|t1 -1.981
#> w2|t2 -0.945
#> w2|t3 -0.125
#> w2|t4 0.743
#> w2|t5 1.805
#> w2|t6 2.484
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .f 0.877
#> x1 1.000
#> x2 1.000
#> x3 1.000
#> w1 1.000
#> w2 1.000
#> .y1 0.124
#> .y2 0.215
#> .y3 0.181
#> f~1 0.626
#>
# }