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 0.012 75.691 0.000
#> x2 0.905 0.014 64.736 0.000
#> x3 0.899 0.013 67.269 0.000
#> Z =~
#> z1 0.911 0.014 65.903 0.000
#> z2 0.909 0.016 57.739 0.000
#> z3 0.918 0.014 66.178 0.000
#> Y =~
#> y1 0.966 0.006 154.021 0.000
#> y2 0.959 0.007 128.850 0.000
#> y3 0.961 0.007 133.048 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.423 0.018 23.803 0.000
#> Z 0.361 0.017 20.908 0.000
#> X:Z 0.452 0.017 26.011 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.201 0.023 8.791 0.000
#> X:Z 0.018 0.031 0.592 0.554
#> Z ~~
#> X:Z 0.060 0.037 1.645 0.100
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.396 0.020 19.996 0.000
#> X:Z 1.013 0.049 20.707 0.000
#> .x1 0.137 0.023 6.025 0.000
#> .x2 0.181 0.025 7.159 0.000
#> .x3 0.191 0.024 7.944 0.000
#> .z1 0.170 0.025 6.773 0.000
#> .z2 0.173 0.029 6.025 0.000
#> .z3 0.157 0.025 6.179 0.000
#> .y1 0.066 0.012 5.463 0.000
#> .y2 0.081 0.014 5.681 0.000
#> .y3 0.077 0.014 5.584 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 59 iterations
#>
#> Estimator MCOrdPLSc
#> Link PROBIT
#>
#> Number of observations 2000
#> Number of iterations 59
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> R-squared (indicators):
#> x1 0.931
#> x2 0.899
#> x3 0.906
#> z1 0.936
#> z2 0.901
#> z3 0.912
#> y1 0.972
#> y2 0.952
#> y3 0.962
#>
#> R-squared (latents):
#> Y 0.475
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.931 0.007 129.287 0.000
#> x2 0.899 0.008 113.260 0.000
#> x3 0.906 0.008 119.087 0.000
#> Z =~
#> z1 0.936 0.007 134.885 0.000
#> z2 0.901 0.008 113.906 0.000
#> z3 0.912 0.007 125.291 0.000
#> Y =~
#> y1 0.972 0.004 261.184 0.000
#> y2 0.952 0.005 182.991 0.000
#> y3 0.962 0.004 216.187 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.416 0.019 21.706 0.000
#> Z 0.356 0.020 17.951 0.000
#> X:Z 0.447 0.020 22.598 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.196 0.025 7.981 0.000
#> X:Z -0.018 0.010 -1.745 0.081
#> Z ~~
#> X:Z -0.008 0.010 -0.759 0.448
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.525 0.018 29.155 0.000
#> X:Z 1.020 0.035 28.924 0.000
#> .x1 0.069 0.007 9.636 0.000
#> .x2 0.101 0.008 12.658 0.000
#> .x3 0.094 0.008 12.401 0.000
#> .z1 0.064 0.007 9.235 0.000
#> .z2 0.099 0.008 12.532 0.000
#> .z3 0.088 0.007 12.112 0.000
#> .y1 0.028 0.004 7.577 0.000
#> .y2 0.048 0.005 9.242 0.000
#> .y3 0.038 0.004 8.564 0.000