A simulated dataset.
syntax <- '
f =~ y1 + y2 + y3
f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'
fit <- pls(syntax, data = randomIntercepts)
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) ended normally after 62 iterations
#> Estimator MCPLSc-MLM
#> Link LINEAR
#>
#> Number of observations 10000
#> Number of iterations 62
#> Number of latent variables 1
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 28.964
#> Degrees of Freedom 10
#> SRMR 0.003
#> RMSEA 0.014
#>
#> R-squared (indicators):
#> y1 0.894
#> y2 0.784
#> y3 0.814
#>
#> R-squared (latents):
#> f 0.123
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> f =~
#> y1 0.946
#> y2 0.885
#> y3 0.902
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> f ~
#> x1 0.237
#> x2 0.162
#> x3 0.075
#> w1 0.129
#> w2 0.092
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> x1 ~~
#> x2 0.107
#> x3 0.004
#> w1 0.000
#> w2 -0.001
#> x2 ~~
#> x3 0.096
#> w1 -0.001
#> w2 0.002
#> x3 ~~
#> w1 0.000
#> w2 0.000
#> w1 ~~
#> w2 -0.041
#>
#> 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 0.106
#> .y2 0.216
#> .y3 0.186
#> f~1 0.614
#>