This vignette shows how to estimate interaction models, with both continuous and ordered (categorical) data.
fit_cont <- pls(
m,
data = modsem::oneInt,
bootstrap = TRUE,
boot.R = 50
)
summary(fit_cont)
#> plssem (0.1.3) ended normally after 3 iterations
#> Estimator PLSc
#> Link LINEAR
#>
#> Number of observations 2000
#> Number of iterations 3
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 56.757
#> Degrees of Freedom 24
#> SRMR 0.006
#> RMSEA 0.026
#>
#> R-squared (indicators):
#> x1 0.863
#> x2 0.819
#> x3 0.809
#> z1 0.830
#> z2 0.827
#> z3 0.843
#> y1 0.934
#> y2 0.919
#> y3 0.923
#>
#> R-squared (latents):
#> Y 0.604
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.929 0.012 75.834 0.000
#> x2 0.905 0.015 61.806 0.000
#> x3 0.899 0.013 70.530 0.000
#> Z =~
#> z1 0.911 0.011 79.973 0.000
#> z2 0.909 0.015 61.911 0.000
#> z3 0.918 0.012 77.681 0.000
#> Y =~
#> y1 0.966 0.006 174.278 0.000
#> y2 0.959 0.008 116.493 0.000
#> y3 0.961 0.006 147.962 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.423 0.020 21.233 0.000
#> Z 0.361 0.017 21.096 0.000
#> X:Z 0.452 0.017 27.255 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.201 0.023 8.869 0.000
#> X:Z 0.018 0.040 0.455 0.649
#> Z ~~
#> X:Z 0.060 0.048 1.267 0.205
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000 0.022 45.244 0.000
#> Z 1.000 0.033 30.396 0.000
#> .Y 0.396 0.017 23.234 0.000
#> X:Z 1.013 0.061 16.594 0.000
#> .x1 0.137 0.023 6.034 0.000
#> .x2 0.181 0.026 6.834 0.000
#> .x3 0.191 0.023 8.333 0.000
#> .z1 0.170 0.021 8.181 0.000
#> .z2 0.173 0.027 6.493 0.000
#> .z3 0.157 0.022 7.226 0.000
#> .y1 0.066 0.011 6.167 0.000
#> .y2 0.081 0.016 5.157 0.000
#> .y3 0.077 0.012 6.201 0.000
fit_ord <- pls(
m,
data = oneIntOrdered,
bootstrap = TRUE,
boot.R = 50,
ordered = colnames(oneIntOrdered) # explicitly specify variables as ordered
)
summary(fit_ord)
#> plssem (0.1.3) ended normally after 53 iterations
#> Estimator MCOrdPLSc
#> Link PROBIT
#>
#> Number of observations 2000
#> Number of iterations 53
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 20.103
#> Degrees of Freedom 24
#> SRMR 0.012
#> RMSEA 0.000
#>
#> R-squared (indicators):
#> x1 0.866
#> x2 0.809
#> x3 0.819
#> z1 0.876
#> z2 0.812
#> z3 0.828
#> y1 0.944
#> y2 0.907
#> y3 0.925
#>
#> R-squared (latents):
#> Y 0.564
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.931 0.011 86.281 0.000
#> x2 0.899 0.008 106.071 0.000
#> x3 0.905 0.007 127.506 0.000
#> Z =~
#> z1 0.936 0.008 113.824 0.000
#> z2 0.901 0.007 126.624 0.000
#> z3 0.910 0.010 87.731 0.000
#> Y =~
#> y1 0.971 0.004 253.736 0.000
#> y2 0.952 0.006 166.008 0.000
#> y3 0.962 0.004 226.279 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.418 0.016 26.246 0.000
#> Z 0.356 0.020 17.577 0.000
#> X:Z 0.447 0.018 24.546 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.195 0.019 10.061 0.000
#> X:Z -0.003
#> Z ~~
#> X:Z -0.008
#>
#> Thresholds:
#> Estimate Std.Error z.value P(>|z|)
#> x1|t1 -2.189 0.083 -26.235 0.000
#> x1|t2 -0.836 0.036 -23.275 0.000
#> x1|t3 0.088 0.031 2.837 0.005
#> x1|t4 0.892 0.034 26.318 0.000
#> x1|t5 1.861 0.063 29.517 0.000
#> x2|t1 -2.560 0.063 -40.683 0.000
#> x2|t2 -1.601 0.050 -32.104 0.000
#> x2|t3 -0.417 0.033 -12.810 0.000
#> x2|t4 0.423 0.030 13.867 0.000
#> x2|t5 1.298 0.040 32.216 0.000
#> x2|t6 2.474 0.059 41.864 0.000
#> x3|t1 -2.370 0.061 -38.963 0.000
#> x3|t2 -1.246 0.043 -28.749 0.000
#> x3|t3 -0.079 0.029 -2.757 0.006
#> x3|t4 0.751 0.030 24.678 0.000
#> x3|t5 2.087 0.074 28.184 0.000
#> x3|t6 2.729 0.062 43.756 0.000
#> y1|t1 -2.746 0.088 -31.037 0.000
#> y1|t2 -1.490 0.037 -40.021 0.000
#> y1|t3 -0.688 0.032 -21.630 0.000
#> y1|t4 0.509 0.039 13.144 0.000
#> y1|t5 1.607 0.061 26.236 0.000
#> y1|t6 2.539 0.173 14.705 0.000
#> y2|t1 -3.082 0.079 -38.962 0.000
#> y2|t2 -1.642 0.062 -26.667 0.000
#> y2|t3 -0.994 0.036 -27.530 0.000
#> y2|t4 0.287 0.037 7.845 0.000
#> y2|t5 1.090 0.051 21.498 0.000
#> y2|t6 2.301 0.136 16.943 0.000
#> y3|t1 -1.678 0.044 -38.553 0.000
#> y3|t2 -0.845 0.029 -29.003 0.000
#> y3|t3 0.309 0.033 9.227 0.000
#> y3|t4 1.363 0.047 28.704 0.000
#> y3|t5 2.181 0.121 18.074 0.000
#> z1|t1 -2.020 0.071 -28.356 0.000
#> z1|t2 -0.775 0.032 -23.972 0.000
#> z1|t3 0.287 0.024 11.760 0.000
#> z1|t4 0.945 0.038 24.936 0.000
#> z1|t5 2.286 0.109 21.032 0.000
#> z1|t6 3.214 0.054 59.935 0.000
#> z2|t1 -2.868 0.067 -42.991 0.000
#> z2|t2 -1.598 0.050 -31.911 0.000
#> z2|t3 -0.747 0.033 -22.980 0.000
#> z2|t4 0.245 0.027 8.926 0.000
#> z2|t5 1.218 0.039 31.264 0.000
#> z2|t6 2.289 0.164 13.985 0.000
#> z3|t1 -3.307 0.044 -75.617 0.000
#> z3|t2 -1.966 0.074 -26.429 0.000
#> z3|t3 -1.281 0.046 -27.754 0.000
#> z3|t4 -0.197 0.029 -6.890 0.000
#> z3|t5 1.001 0.032 30.936 0.000
#> z3|t6 1.676 0.046 36.261 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.436 0.029 14.905 0.000
#> X:Z 1.034
#> .x1 0.134 0.020 6.670 0.000
#> .x2 0.191 0.015 12.533 0.000
#> .x3 0.181 0.013 14.060 0.000
#> .z1 0.124 0.015 8.085 0.000
#> .z2 0.188 0.013 14.646 0.000
#> .z3 0.172 0.019 9.083 0.000
#> .y1 0.056 0.007 7.560 0.000
#> .y2 0.093 0.011 8.533 0.000
#> .y3 0.075 0.008 9.129 0.000