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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,
sample = 50
)
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 0.015 62.948 0.000
#> x2 0.905 0.013 70.005 0.000
#> x3 0.899 0.014 65.703 0.000
#> Z =~
#> z1 0.911 0.013 71.112 0.000
#> z2 0.909 0.015 62.539 0.000
#> z3 0.918 0.015 61.573 0.000
#> Y =~
#> y1 0.966 0.006 161.293 0.000
#> y2 0.959 0.007 133.159 0.000
#> y3 0.961 0.007 130.698 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.423 0.018 23.205 0.000
#> Z 0.361 0.017 20.967 0.000
#> X:Z 0.452 0.017 26.519 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.201 0.022 9.225 0.000
#> X:Z 0.018 0.031 0.590 0.555
#> Z ~~
#> X:Z 0.060 0.039 1.561 0.119
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.396 0.017 22.929 0.000
#> X:Z 1.013 0.046 22.024 0.000
#> .x1 0.137 0.027 5.009 0.000
#> .x2 0.181 0.023 7.710 0.000
#> .x3 0.191 0.025 7.778 0.000
#> .z1 0.170 0.023 7.322 0.000
#> .z2 0.173 0.026 6.547 0.000
#> .z3 0.157 0.027 5.723 0.000
#> .y1 0.066 0.012 5.716 0.000
#> .y2 0.081 0.014 5.881 0.000
#> .y3 0.077 0.014 5.479 0.000fit_ord <- pls(
m,
data = oneIntOrdered,
bootstrap = TRUE,
sample = 50,
ordered = colnames(oneIntOrdered) # explicitly specify variables as ordered
)
summary(fit_ord)
#> plssem (0.1.0) ended normally after 44 iterations
#>
#> Estimator MCOrdPLSc
#> Link PROBIT
#>
#> Number of observations 2000
#> Number of iterations 44
#> Number of latent variables 3
#> Number of observed variables 9
#>
#> R-squared (indicators):
#> x1 0.930
#> x2 0.899
#> x3 0.906
#> z1 0.935
#> z2 0.901
#> z3 0.912
#> y1 0.972
#> y2 0.951
#> y3 0.962
#>
#> R-squared (latents):
#> Y 0.558
#>
#> Latent Variables:
#> Estimate Std.Error z.value P(>|z|)
#> X =~
#> x1 0.930 0.008 120.685 0.000
#> x2 0.899 0.009 105.738 0.000
#> x3 0.906 0.008 106.666 0.000
#> Z =~
#> z1 0.935 0.007 137.890 0.000
#> z2 0.901 0.009 102.039 0.000
#> z3 0.912 0.009 105.202 0.000
#> Y =~
#> y1 0.972 0.004 268.018 0.000
#> y2 0.951 0.005 178.269 0.000
#> y3 0.962 0.005 206.572 0.000
#>
#> Regressions:
#> Estimate Std.Error z.value P(>|z|)
#> Y ~
#> X 0.417 0.024 17.534 0.000
#> Z 0.357 0.023 15.846 0.000
#> X:Z 0.445 0.021 21.072 0.000
#>
#> Covariances:
#> Estimate Std.Error z.value P(>|z|)
#> X ~~
#> Z 0.192 0.024 8.146 0.000
#> X:Z -0.011 0.010 -1.053 0.292
#> Z ~~
#> X:Z 0.015 0.009 1.743 0.081
#>
#> Variances:
#> Estimate Std.Error z.value P(>|z|)
#> X 1.000
#> Z 1.000
#> .Y 0.442 0.030 14.960 0.000
#> X:Z 1.028 0.018 55.700 0.000
#> .x1 0.070 0.008 9.065 0.000
#> .x2 0.101 0.009 11.866 0.000
#> .x3 0.094 0.008 11.127 0.000
#> .z1 0.065 0.007 9.548 0.000
#> .z2 0.099 0.009 11.208 0.000
#> .z3 0.088 0.009 10.173 0.000
#> .y1 0.028 0.004 7.792 0.000
#> .y2 0.049 0.005 9.089 0.000
#> .y3 0.038 0.005 8.073 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.