A simulated dataset.
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 (0.1.3) did NOT END NORMALLY after 58 iterations
#> Estimator MCOrdPLSc-MLM
#> Link PROBIT
#>
#> Number of observations 10000
#> Number of iterations 58
#> Number of latent variables 1
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 11.186
#> Degrees of Freedom 10
#> SRMR 0.003
#> RMSEA 0.003
#>
#> R-squared (indicators):
#> y1 0.879
#> y2 0.788
#> y3 0.816
#>
#> R-squared (latents):
#> f 0.122
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> f =~
#> y1 0.938
#> y2 0.888
#> y3 0.903
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> f ~
#> x1 0.241
#> x2 0.159
#> x3 0.082
#> w1 0.123
#> w2 0.078
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> x1 ~~
#> x2 0.111
#> x3 0.010
#> w1 0.002
#> w2 0.001
#> x2 ~~
#> x3 0.100
#> w1 -0.001
#> w2 0.003
#> x3 ~~
#> w1 -0.003
#> w2 0.003
#> w1 ~~
#> w2 -0.026
#>
#> Thresholds:
#> Estimate Std.Error z.value P(>|z|)
#> y1|t1 -2.519
#> y1|t2 -1.733
#> y1|t3 -0.445
#> y1|t4 0.319
#> y1|t5 1.335
#> y1|t6 2.437
#> y2|t1 -2.793
#> y2|t2 -1.883
#> y2|t3 -0.978
#> y2|t4 0.170
#> y2|t5 1.064
#> y2|t6 2.250
#> y3|t1 -2.163
#> y3|t2 -1.258
#> y3|t3 -0.010
#> y3|t4 0.691
#> y3|t5 1.825
#> y3|t6 2.901
#> x1|t1 -2.375
#> x1|t2 -1.347
#> x1|t3 -0.676
#> x1|t4 0.490
#> x1|t5 1.517
#> x1|t6 2.570
#> x2|t1 -2.914
#> x2|t2 -2.048
#> x2|t3 -0.995
#> x2|t4 -0.115
#> x2|t5 0.808
#> x2|t6 1.905
#> x3|t1 -2.969
#> x3|t2 -1.953
#> x3|t3 -0.836
#> x3|t4 -0.145
#> x3|t5 0.992
#> x3|t6 2.249
#> w1|t1 -2.774
#> w1|t2 -2.006
#> w1|t3 -0.962
#> w1|t4 0.113
#> w1|t5 1.253
#> w1|t6 2.551
#> w2|t1 -1.973
#> w2|t2 -0.946
#> w2|t3 -0.119
#> w2|t4 0.746
#> w2|t5 1.802
#> w2|t6 2.442
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .f 0.878
#> x1 1.000
#> x2 1.000
#> x3 1.000
#> w1 1.000
#> w2 1.000
#> .y1 0.121
#> .y2 0.212
#> .y3 0.184
#> f~1 0.615
#>