The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
This vignette shows examples of multilevel random slopes and intercept models, with both continuous and ordinal data.
slopes_model <- "
X =~ x1 + x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
W =~ w1 + w2 + w3
Y ~ X + Z + (1 + X + Z | cluster)
W ~ X + Z + (1 + X + Z | cluster)
"fit_slopes_cont <- pls(
slopes_model,
data = randomSlopes,
bootstrap = TRUE,
boot.R = 50
)
summary(fit_slopes_cont)
#> plssem (0.1.1) ended normally after 3 iterations
#>
#> Estimator PLSc-MLM
#> Link PROBIT
#>
#> Number of observations 5000
#> Number of iterations 3
#> Number of latent variables 4
#> Number of observed variables 18
#>
#> Fit Measures:
#> Chi-Square 127.210
#> Degrees of Freedom 49
#> SRMR 0.008
#> RMSEA 0.018
#>
#> R-squared (indicators):
#> x1 0.860
#> x2 0.683
#> x3 0.772
#> z1 0.839
#> z2 0.694
#> z3 0.759
#> y1 0.838
#> y2 0.726
#> y3 0.750
#> w1 0.840
#> w2 0.695
#> w3 0.771
#>
#> R-squared (latents):
#> Y 0.542
#> W 0.553
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.927 0.010 96.515 0.000
#> x2 0.826 0.010 79.841 0.000
#> x3 0.879 0.007 118.521 0.000
#> Z =~
#> z1 0.916 0.009 106.571 0.000
#> z2 0.833 0.011 74.956 0.000
#> z3 0.871 0.009 97.399 0.000
#> Y =~
#> y1 0.915 0.009 105.080 0.000
#> y2 0.852 0.013 65.547 0.000
#> y3 0.866 0.013 67.926 0.000
#> W =~
#> w1 0.916 0.011 84.423 0.000
#> w2 0.834 0.013 62.526 0.000
#> w3 0.878 0.011 83.457 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.293 0.022 13.346 0.000
#> Z 0.444 0.041 10.747 0.000
#> W ~
#> X 0.393 0.030 13.077 0.000
#> Z 0.248 0.054 4.591 0.000
#>
#> Intercepts:
#> Estimate Std.Error z.value P(>|z|)
#> .Y 0.010 0.005 2.149 0.032
#> .W 0.010 0.008 1.312 0.190
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.175 0.013 13.799 0.000
#> Y~X ~~
#> Y~1 -0.004 0.005 -0.707 0.480
#> Y~Z ~~
#> Y~1 -0.025 0.011 -2.337 0.019
#> Y~X 0.012 0.009 1.413 0.158
#> W~X ~~
#> W~1 0.003 0.012 0.263 0.793
#> W~Z ~~
#> W~1 0.009 0.014 0.645 0.519
#> W~X 0.013 0.012 1.120 0.263
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.458 0.028 16.444 0.000
#> .W 0.447 0.031 14.645 0.000
#> .x1 0.140 0.018 7.836 0.000
#> .x2 0.317 0.017 18.641 0.000
#> .x3 0.228 0.013 17.494 0.000
#> .z1 0.161 0.016 10.218 0.000
#> .z2 0.306 0.019 16.521 0.000
#> .z3 0.241 0.016 15.538 0.000
#> .y1 0.162 0.016 10.185 0.000
#> .y2 0.274 0.022 12.375 0.000
#> .y3 0.250 0.022 11.442 0.000
#> .w1 0.160 0.020 8.065 0.000
#> .w2 0.305 0.022 13.799 0.000
#> .w3 0.229 0.018 12.426 0.000
#> Y~1 0.086 0.021 4.087 0.000
#> Y~X 0.018 0.007 2.747 0.006
#> Y~Z 0.105 0.015 6.773 0.000
#> W~1 0.059 0.012 4.977 0.000
#> W~X 0.095 0.014 6.676 0.000
#> W~Z 0.144 0.027 5.270 0.000fit_slopes_ord <- pls(
slopes_model,
data = randomSlopesOrdered,
bootstrap = TRUE,
boot.R = 50,
ordered = colnames(randomSlopesOrdered) # explicitly specify variables as ordered
)
summary(fit_slopes_ord)
#> plssem (0.1.1) ended normally after 3 iterations
#>
#> Estimator OrdPLSc-MLM
#> Link PROBIT
#>
#> Number of observations 5000
#> Number of iterations 3
#> Number of latent variables 4
#> Number of observed variables 18
#>
#> Fit Measures:
#> Chi-Square 263.372
#> Degrees of Freedom 49
#> SRMR 0.010
#> RMSEA 0.030
#>
#> R-squared (indicators):
#> x1 0.870
#> x2 0.669
#> x3 0.789
#> z1 0.841
#> z2 0.714
#> z3 0.758
#> y1 0.844
#> y2 0.711
#> y3 0.754
#> w1 0.835
#> w2 0.681
#> w3 0.785
#>
#> R-squared (latents):
#> Y 0.539
#> W 0.550
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.933 0.013 74.052 0.000
#> x2 0.818 0.011 74.996 0.000
#> x3 0.888 0.011 83.460 0.000
#> Z =~
#> z1 0.917 0.013 72.433 0.000
#> z2 0.845 0.012 72.353 0.000
#> z3 0.871 0.013 67.845 0.000
#> Y =~
#> y1 0.918 0.011 80.060 0.000
#> y2 0.843 0.013 65.426 0.000
#> y3 0.869 0.014 63.020 0.000
#> W =~
#> w1 0.914 0.015 62.284 0.000
#> w2 0.825 0.019 43.446 0.000
#> w3 0.886 0.014 63.524 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.290 0.020 14.553 0.000
#> Z 0.451 0.032 13.963 0.000
#> W ~
#> X 0.391 0.042 9.281 0.000
#> Z 0.245 0.061 4.008 0.000
#>
#> Intercepts:
#> Estimate Std.Error z.value P(>|z|)
#> .Y 0.011 0.005 2.500 0.012
#> .W 0.009 0.007 1.370 0.171
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.168 0.012 13.759 0.000
#> Y~X ~~
#> Y~1 -0.007 0.004 -1.526 0.127
#> Y~Z ~~
#> Y~1 -0.025 0.013 -1.902 0.057
#> Y~X 0.012 0.010 1.277 0.202
#> W~X ~~
#> W~1 0.004 0.010 0.392 0.695
#> W~Z ~~
#> W~1 0.010 0.014 0.697 0.486
#> W~X 0.016 0.009 1.803 0.071
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.461 0.025 18.742 0.000
#> .W 0.450 0.040 11.276 0.000
#> .x1 0.130 0.023 5.590 0.000
#> .x2 0.331 0.018 18.508 0.000
#> .x3 0.211 0.019 11.156 0.000
#> .z1 0.159 0.023 6.800 0.000
#> .z2 0.286 0.020 14.542 0.000
#> .z3 0.242 0.022 10.839 0.000
#> .y1 0.156 0.021 7.436 0.000
#> .y2 0.289 0.022 13.316 0.000
#> .y3 0.246 0.024 10.263 0.000
#> .w1 0.165 0.027 6.208 0.000
#> .w2 0.319 0.031 10.240 0.000
#> .w3 0.215 0.025 8.708 0.000
#> Y~1 0.084 0.023 3.693 0.000
#> Y~X 0.018 0.005 3.806 0.000
#> Y~Z 0.101 0.015 6.807 0.000
#> W~1 0.058 0.012 4.732 0.000
#> W~X 0.099 0.016 6.375 0.000
#> W~Z 0.142 0.026 5.494 0.000intercepts_model <- '
f =~ y1 + y2 + y3
f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'fit_intercepts_cont <- pls(
intercepts_model,
data = randomIntercepts,
bootstrap = TRUE,
boot.R = 50
)
summary(fit_intercepts_cont)
#> plssem (0.1.1) ended normally after 2 iterations
#>
#> Estimator PLSc-MLM
#> Link PROBIT
#>
#> Number of observations 10000
#> Number of iterations 2
#> Number of latent variables 1
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 24.372
#> Degrees of Freedom 10
#> SRMR 0.003
#> RMSEA 0.012
#>
#> R-squared (indicators):
#> y1 0.891
#> y2 0.785
#> y3 0.814
#>
#> R-squared (latents):
#> f 0.744
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> f =~
#> y1 0.944 0.009 99.515 0.000
#> y2 0.886 0.009 98.843 0.000
#> y3 0.902 0.009 95.623 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> f ~
#> x1 0.238 0.008 28.898 0.000
#> x2 0.162 0.007 23.392 0.000
#> x3 0.077 0.006 13.149 0.000
#> w1 0.128 0.038 3.378 0.001
#> w2 0.091 0.033 2.746 0.006
#>
#> Intercepts:
#> Estimate Std.Error z.value P(>|z|)
#> .f -0.013 0.008 -1.636 0.102
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> x1 ~~
#> x2 0.104 0.011 9.068 0.000
#> x3 0.004 0.010 0.426 0.670
#> w1 0.000
#> w2 0.000
#> x2 ~~
#> x3 0.097 0.010 9.333 0.000
#> w1 0.000
#> w2 0.000
#> x3 ~~
#> w1 0.000
#> w2 0.000
#> w1 ~~
#> w2 -0.041 0.050 -0.819 0.413
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .f 0.256 0.014 17.816 0.000
#> x1 1.000
#> x2 1.000
#> x3 1.000
#> w1 1.000
#> w2 1.000
#> .y1 0.109 0.018 6.056 0.000
#> .y2 0.215 0.016 13.486 0.000
#> .y3 0.186 0.017 10.985 0.000
#> f~1 0.621 0.021 30.153 0.000fit_intercepts_ord <- pls(
intercepts_model,
data = randomInterceptsOrdered,
bootstrap = TRUE,
boot.R = 50,
ordered = colnames(randomInterceptsOrdered) # explicitly specify variables as ordered
)
summary(fit_intercepts_ord)
#> plssem (0.1.1) ended normally after 2 iterations
#>
#> Estimator OrdPLSc-MLM
#> Link PROBIT
#>
#> Number of observations 10000
#> Number of iterations 2
#> Number of latent variables 1
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 25.508
#> Degrees of Freedom 10
#> SRMR 0.003
#> RMSEA 0.012
#>
#> R-squared (indicators):
#> y1 0.885
#> y2 0.788
#> y3 0.809
#>
#> R-squared (latents):
#> f 0.723
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> f =~
#> y1 0.941 0.012 78.652 0.000
#> y2 0.888 0.013 69.164 0.000
#> y3 0.899 0.014 63.554 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> f ~
#> x1 0.241 0.009 27.424 0.000
#> x2 0.158 0.008 20.447 0.000
#> x3 0.080 0.007 11.671 0.000
#> w1 0.121 0.044 2.726 0.006
#> w2 0.081 0.043 1.897 0.058
#>
#> Intercepts:
#> Estimate Std.Error z.value P(>|z|)
#> .f -0.012 0.008 -1.422 0.155
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> x1 ~~
#> x2 0.110 0.011 10.357 0.000
#> x3 0.012 0.013 0.899 0.369
#> w1 0.001 0.004 0.214 0.831
#> w2 0.001 0.003 0.361 0.718
#> x2 ~~
#> x3 0.099 0.011 8.741 0.000
#> w1 -0.003 0.003 -1.012 0.311
#> w2 -0.001 0.003 -0.455 0.649
#> x3 ~~
#> w1 -0.003 0.003 -1.034 0.301
#> w2 0.003 0.003 1.071 0.284
#> w1 ~~
#> w2 -0.025 0.054 -0.454 0.650
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> .f 0.277 0.015 18.173 0.000
#> x1 1.000
#> x2 1.000
#> x3 1.000
#> w1 1.000
#> w2 1.000
#> .y1 0.115 0.023 5.067 0.000
#> .y2 0.212 0.023 9.284 0.000
#> .y3 0.191 0.025 7.543 0.000
#> f~1 0.601 0.021 28.497 0.000These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.