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 (0.1.3) 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                                    19.849
#>   Degrees of Freedom                                24
#>   SRMR                                           0.011
#>   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                                              0.572
#> 
#> 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             0.000                             
#> 
#> Thresholds:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>     x1|t1          -2.189                             
#>     x1|t2          -0.823                             
#>     x1|t3           0.078                             
#>     x1|t4           0.891                             
#>     x1|t5           1.876                             
#>     x2|t1          -2.538                             
#>     x2|t2          -1.600                             
#>     x2|t3          -0.424                             
#>     x2|t4           0.411                             
#>     x2|t5           1.296                             
#>     x2|t6           2.526                             
#>     x3|t1          -2.362                             
#>     x3|t2          -1.267                             
#>     x3|t3          -0.079                             
#>     x3|t4           0.744                             
#>     x3|t5           2.100                             
#>     x3|t6           2.654                             
#>     z1|t1          -2.002                             
#>     z1|t2          -0.788                             
#>     z1|t3           0.289                             
#>     z1|t4           0.946                             
#>     z1|t5           2.265                             
#>     z1|t6           3.287                             
#>     z2|t1          -2.830                             
#>     z2|t2          -1.589                             
#>     z2|t3          -0.751                             
#>     z2|t4           0.238                             
#>     z2|t5           1.224                             
#>     z2|t6           2.313                             
#>     z3|t1          -3.287                             
#>     z3|t2          -1.954                             
#>     z3|t3          -1.273                             
#>     z3|t4          -0.207                             
#>     z3|t5           0.996                             
#>     z3|t6           1.692                             
#>     y1|t1          -2.741                             
#>     y1|t2          -1.496                             
#>     y1|t3          -0.686                             
#>     y1|t4           0.503                             
#>     y1|t5           1.606                             
#>     y1|t6           2.598                             
#>     y2|t1          -2.971                             
#>     y2|t2          -1.648                             
#>     y2|t3          -0.983                             
#>     y2|t4           0.284                             
#>     y2|t5           1.080                             
#>     y2|t6           2.346                             
#>     y3|t1          -1.678                             
#>     y3|t2          -0.848                             
#>     y3|t3           0.319                             
#>     y3|t4           1.351                             
#>     y3|t5           2.216                             
#> 
#> Variances:
#>                  Estimate  Std.Error  z.value  P(>|z|)
#>     X               1.000                             
#>     Z               1.000                             
#>    .Y               0.428                             
#>     X:Z             1.039                             
#>    .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                             
#>