A simulated dataset.

Examples


m <- '
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3

  Y ~ X + Z + X:Z
'

fit <- pls(m, oneIntOrdered)
summary(fit)
#> plssem->fitMeasures():  
#>    Fit measures for MC-PLSc models are under development! Traditional fit 
#>    criteria will likely be too strict.
#> plssem->fitMeasures():  
#>    Resampling MC-PLSc Model (R = 1000000)...
#> plssem (0.1.4) 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                                    20.231
#>   Degrees of Freedom                                24
#>   SRMR                                           0.012
#>   RMSEA                                          0.000
#> 
#> R-squared (indicators):
#>   x1                                             0.866
#>   x2                                             0.809
#>   x3                                             0.821
#>   z1                                             0.875
#>   z2                                             0.812
#>   z3                                             0.830
#>   y1                                             0.944
#>   y2                                             0.907
#>   y3                                             0.925
#> 
#> R-squared (latents):
#>   Y                                              0.575
#> 
#> Latent Variables:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>   X =~          
#>     x1              0.930                             
#>     x2              0.900                             
#>     x3              0.906                             
#>   Z =~          
#>     z1              0.936                             
#>     z2              0.901                             
#>     z3              0.911                             
#>   Y =~          
#>     y1              0.971                             
#>     y2              0.952                             
#>     y3              0.962                             
#> 
#> Regressions:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>   Y ~           
#>     X               0.417                             
#>     Z               0.357                             
#>     X:Z             0.447                             
#> 
#> Covariances:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>   X ~~          
#>     Z               0.193                             
#>     X:Z             0.013                             
#>   Z ~~          
#>     X:Z             0.014                             
#> 
#> Thresholds:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>     x1|t1          -2.191                             
#>     x1|t2          -0.827                             
#>     x1|t3           0.076                             
#>     x1|t4           0.892                             
#>     x1|t5           1.862                             
#>     x2|t1          -2.557                             
#>     x2|t2          -1.579                             
#>     x2|t3          -0.433                             
#>     x2|t4           0.407                             
#>     x2|t5           1.311                             
#>     x2|t6           2.490                             
#>     x3|t1          -2.361                             
#>     x3|t2          -1.244                             
#>     x3|t3          -0.097                             
#>     x3|t4           0.754                             
#>     x3|t5           2.123                             
#>     x3|t6           2.761                             
#>     z1|t1          -2.050                             
#>     z1|t2          -0.776                             
#>     z1|t3           0.283                             
#>     z1|t4           0.929                             
#>     z1|t5           2.296                             
#>     z1|t6           3.254                             
#>     z2|t1          -2.918                             
#>     z2|t2          -1.594                             
#>     z2|t3          -0.736                             
#>     z2|t4           0.237                             
#>     z2|t5           1.218                             
#>     z2|t6           2.319                             
#>     z3|t1          -3.355                             
#>     z3|t2          -1.970                             
#>     z3|t3          -1.265                             
#>     z3|t4          -0.201                             
#>     z3|t5           0.992                             
#>     z3|t6           1.677                             
#>     y1|t1          -2.819                             
#>     y1|t2          -1.506                             
#>     y1|t3          -0.675                             
#>     y1|t4           0.498                             
#>     y1|t5           1.611                             
#>     y1|t6           2.535                             
#>     y2|t1          -2.894                             
#>     y2|t2          -1.637                             
#>     y2|t3          -0.998                             
#>     y2|t4           0.291                             
#>     y2|t5           1.084                             
#>     y2|t6           2.310                             
#>     y3|t1          -1.671                             
#>     y3|t2          -0.845                             
#>     y3|t3           0.312                             
#>     y3|t4           1.345                             
#>     y3|t5           2.187                             
#> 
#> Variances:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>     X               1.000                             
#>     Z               1.000                             
#>    .Y               0.425                             
#>     X:Z             1.038                             
#>    .x1              0.134                             
#>    .x2              0.191                             
#>    .x3              0.179                             
#>    .z1              0.125                             
#>    .z2              0.188                             
#>    .z3              0.170                             
#>    .y1              0.056                             
#>    .y2              0.093                             
#>    .y3              0.075                             
#>