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This vignette shows how to estimate interaction models, with both continuous and ordered (categorical) data.
m <- '
X =~ x1 + x2 + x3
Z =~ z1 + z2 + z3
Y =~ y1 + y2 + y3
Y ~ X + Z + X:Z
'fit_cont <- pls(
m,
data = modsem::oneInt,
bootstrap = TRUE,
boot.R = 50
)
summary(fit_cont)
#> plssem (0.1.1) ended normally after 3 iterations
#>
#> Estimator PLSc
#> Link PROBIT
#>
#> 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 21
#> SRMR 0.006
#> RMSEA 0.029
#>
#> 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 74.628 0.000
#> x2 0.905 0.014 62.846 0.000
#> x3 0.899 0.014 64.168 0.000
#> Z =~
#> z1 0.911 0.012 75.965 0.000
#> z2 0.909 0.016 58.630 0.000
#> z3 0.918 0.015 59.250 0.000
#> Y =~
#> y1 0.966 0.006 158.761 0.000
#> y2 0.959 0.008 119.612 0.000
#> y3 0.961 0.007 143.590 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.423 0.018 23.768 0.000
#> Z 0.361 0.018 19.679 0.000
#> X:Z 0.452 0.017 26.844 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.201 0.026 7.670 0.000
#> X:Z 0.018 0.027 0.666 0.506
#> Z ~~
#> X:Z 0.060 0.037 1.633 0.102
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.396 0.019 21.127 0.000
#> X:Z 1.013 0.046 22.195 0.000
#> .x1 0.137 0.023 5.960 0.000
#> .x2 0.181 0.026 6.964 0.000
#> .x3 0.191 0.025 7.592 0.000
#> .z1 0.170 0.022 7.768 0.000
#> .z2 0.173 0.028 6.158 0.000
#> .z3 0.157 0.028 5.528 0.000
#> .y1 0.066 0.012 5.640 0.000
#> .y2 0.081 0.015 5.263 0.000
#> .y3 0.077 0.013 6.012 0.000fit_ord <- pls(
m,
data = oneIntOrdered,
bootstrap = TRUE,
boot.R = 50,
ordered = colnames(oneIntOrdered) # explicitly specify variables as ordered
)
summary(fit_ord)
#> plssem (0.1.1) ended normally after 67 iterations
#>
#> Estimator MCOrdPLSc
#> Link PROBIT
#>
#> Number of observations 2000
#> Number of iterations 67
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> Fit Measures:
#> Chi-Square 21.265
#> Degrees of Freedom 21
#> SRMR 0.012
#> RMSEA 0.003
#>
#> R-squared (indicators):
#> x1 0.931
#> x2 0.899
#> x3 0.906
#> z1 0.935
#> z2 0.902
#> z3 0.912
#> y1 0.972
#> y2 0.952
#> y3 0.962
#>
#> R-squared (latents):
#> Y 0.552
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.931 0.007 140.249 0.000
#> x2 0.899 0.007 125.717 0.000
#> x3 0.906 0.007 134.623 0.000
#> Z =~
#> z1 0.935 0.007 137.175 0.000
#> z2 0.902 0.008 115.795 0.000
#> z3 0.912 0.007 137.624 0.000
#> Y =~
#> y1 0.972 0.005 206.485 0.000
#> y2 0.952 0.005 194.440 0.000
#> y3 0.962 0.004 236.331 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.415 0.021 19.877 0.000
#> Z 0.357 0.022 16.365 0.000
#> X:Z 0.448 0.017 25.748 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.194 0.026 7.372 0.000
#> X:Z -0.004 0.014 -0.269 0.788
#> Z ~~
#> X:Z -0.009 0.012 -0.758 0.449
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.448 0.028 16.050 0.000
#> X:Z 1.000
#> .x1 0.069 0.007 10.457 0.000
#> .x2 0.101 0.007 14.126 0.000
#> .x3 0.094 0.007 14.034 0.000
#> .z1 0.065 0.007 9.496 0.000
#> .z2 0.098 0.008 12.623 0.000
#> .z3 0.088 0.007 13.315 0.000
#> .y1 0.028 0.005 6.027 0.000
#> .y2 0.048 0.005 9.796 0.000
#> .y3 0.038 0.004 9.400 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.