This vignette shows how to estimate interaction models, with both continuous and ordered (categorical) data.
fit_cont <- pls(
m,
data = modsem::oneInt,
bootstrap = TRUE,
sample = 500
)
summary(fit_cont)
#> plssem (0.1.0) 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
#>
#> 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 0.013 72.389 0.000
#> x2 0.905 0.014 64.833 0.000
#> x3 0.899 0.014 63.814 0.000
#> Z =~
#> z1 0.911 0.014 63.683 0.000
#> z2 0.909 0.016 58.088 0.000
#> z3 0.918 0.015 60.891 0.000
#> Y =~
#> y1 0.966 0.006 157.741 0.000
#> y2 0.959 0.007 136.924 0.000
#> y3 0.961 0.007 135.412 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.423 0.018 24.040 0.000
#> Z 0.361 0.018 19.869 0.000
#> X:Z 0.452 0.017 26.402 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.201 0.023 8.852 0.000
#> X:Z 0.018 0.030 0.595 0.552
#> Z ~~
#> X:Z 0.060 0.037 1.617 0.106
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.396 0.020 19.935 0.000
#> X:Z 1.013 0.048 21.041 0.000
#> .x1 0.137 0.024 5.765 0.000
#> .x2 0.181 0.025 7.153 0.000
#> .x3 0.191 0.025 7.540 0.000
#> .z1 0.170 0.026 6.550 0.000
#> .z2 0.173 0.028 6.078 0.000
#> .z3 0.157 0.028 5.680 0.000
#> .y1 0.066 0.012 5.589 0.000
#> .y2 0.081 0.013 6.044 0.000
#> .y3 0.077 0.014 5.677 0.000
fit_ord <- pls(
m,
data = oneIntOrdered,
bootstrap = TRUE,
sample = 500,
ordered = colnames(oneIntOrdered) # explicitly specify variables as ordered
)
summary(fit_ord)
#> plssem (0.1.0) ended normally after 18 iterations
#>
#> Estimator OrdPLSc
#> Link PROBIT-CEXP
#>
#> Number of observations 2000
#> Number of iterations 18
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> R-squared (indicators):
#> x1 0.810
#> x2 0.753
#> x3 0.729
#> z1 0.798
#> z2 0.755
#> z3 0.750
#> y1 0.848
#> y2 0.838
#> y3 0.848
#>
#> R-squared (latents):
#> Y 0.592
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.900 0.016 54.759 0.000
#> x2 0.867 0.017 49.969 0.000
#> x3 0.854 0.019 46.088 0.000
#> Z =~
#> z1 0.894 0.019 47.522 0.000
#> z2 0.869 0.019 45.544 0.000
#> z3 0.866 0.019 46.386 0.000
#> Y =~
#> y1 0.921 0.011 82.269 0.000
#> y2 0.916 0.013 70.685 0.000
#> y3 0.921 0.011 83.027 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.419 0.019 21.548 0.000
#> Z 0.354 0.020 18.024 0.000
#> X:Z 0.448 0.020 22.873 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.195 0.024 8.270 0.000
#> X:Z 0.016 0.032 0.515 0.607
#> Z ~~
#> X:Z 0.070 0.036 1.961 0.050
#>
#> Thresholds:
#> Estimate Std.Error z.value P(>|z|)
#> x1|t1 -2.183 0.077 -28.536 0.000
#> x1|t2 -0.827 0.032 -25.698 0.000
#> x1|t3 0.077 0.028 2.758 0.006
#> x1|t4 0.895 0.032 28.141 0.000
#> x1|t5 1.859 0.061 30.509 0.000
#> x2|t1 -2.576 0.123 -20.951 0.000
#> x2|t2 -1.594 0.046 -34.934 0.000
#> x2|t3 -0.425 0.029 -14.407 0.000
#> x2|t4 0.418 0.028 14.718 0.000
#> x2|t5 1.302 0.036 35.702 0.000
#> x2|t6 2.484 0.108 23.104 0.000
#> x3|t1 -2.387 0.092 -25.850 0.000
#> x3|t2 -1.248 0.037 -33.302 0.000
#> x3|t3 -0.083 0.027 -3.056 0.002
#> x3|t4 0.749 0.030 24.888 0.000
#> x3|t5 2.086 0.066 31.433 0.000
#> x3|t6 2.697 0.130 20.700 0.000
#> z1|t1 -2.024 0.063 -31.871 0.000
#> z1|t2 -0.782 0.032 -24.813 0.000
#> z1|t3 0.285 0.027 10.482 0.000
#> z1|t4 0.938 0.034 27.525 0.000
#> z1|t5 2.273 0.081 28.226 0.000
#> z1|t6 3.291 2.958 1.112 0.266
#> z2|t1 -2.878 0.664 -4.334 0.000
#> z2|t2 -1.594 0.422 -3.779 0.000
#> z2|t3 -0.749 0.494 -1.516 0.130
#> z2|t4 0.242 0.487 0.497 0.619
#> z2|t5 1.213 0.598 2.030 0.042
#> z2|t6 2.308 2.477 0.932 0.351
#> z3|t1 -3.291 0.737 -4.462 0.000
#> z3|t2 -1.960 0.601 -3.260 0.001
#> z3|t3 -1.273 0.787 -1.618 0.106
#> z3|t4 -0.204 0.727 -0.281 0.779
#> z3|t5 0.999 1.460 0.684 0.494
#> z3|t6 1.675 2.005 0.835 0.404
#> y1|t1 -2.796 0.871 -3.208 0.001
#> y1|t2 -1.527 0.819 -1.864 0.062
#> y1|t3 -0.682 0.892 -0.764 0.445
#> y1|t4 0.515 0.886 0.581 0.561
#> y1|t5 1.595 1.783 0.894 0.371
#> y1|t6 2.505 2.249 1.114 0.265
#> y2|t1 -2.965 1.073 -2.762 0.006
#> y2|t2 -1.666 1.039 -1.603 0.109
#> y2|t3 -0.996 1.079 -0.923 0.356
#> y2|t4 0.296 1.191 0.248 0.804
#> y2|t5 1.082 1.607 0.673 0.501
#> y2|t6 2.301 1.634 1.408 0.159
#> y3|t1 -1.689 1.071 -1.577 0.115
#> y3|t2 -0.849 1.004 -0.845 0.398
#> y3|t3 0.322 0.667 0.482 0.629
#> y3|t4 1.349 0.563 2.397 0.017
#> y3|t5 2.178 0.573 3.802 0.000
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.408 0.021 19.077 0.000
#> X:Z 1.022 0.049 21.041 0.000
#> .x1 0.190 0.030 6.432 0.000
#> .x2 0.247 0.030 8.233 0.000
#> .x3 0.271 0.032 8.564 0.000
#> .z1 0.202 0.034 6.009 0.000
#> .z2 0.245 0.033 7.379 0.000
#> .z3 0.250 0.032 7.714 0.000
#> .y1 0.152 0.021 7.384 0.000
#> .y2 0.162 0.024 6.818 0.000
#> .y3 0.152 0.020 7.437 0.000