Model Prediction Partial Least Square Model

Question

I am following the procedure explained Hair et al (2021) to run a partial least square model (seminR).

So far, it worked well. However, when using the predict function, I get the following error:

Parallel encountered this ERROR: Must subset columns with a valid subscript vector. x Subscript endogenous_items must be a simple vector, not a matrix. r in summary.connection(connection) : invalid connection

I have recently started working with r. My dataset is an excel table with 579 obs. of 47 variables. How can I solve these problems? Thank you very much in advance.

That's my code:

  composite("EA", multi_items("EA_", 1:3))`
  composite("DB", multi_items("DB_", 1:5)),
  composite("LTB", multi_items("LTB_", 1:5)), 
  composite("SN", multi_items("SN_", 1:3)),
  composite("PBC", multi_items("PBC_", 1:3)),
  composite("SE", multi_items("SE_", 1:8)),
  composite("INT", multi_items("INT_", 1:2)),
  composite("B", multi_items("B_", 1:2)))

Create structural model
`final_sm_ext <- relationships(
paths(from = c("DB", "LTB", "SN", "PBC", "SE", "INT"), to = c("B")),
paths(from = c("EA"), to = c("INT")))`
  

bike_final_model_ext <- estimate_pls('data = PLSdata,
                                 measurement_model = final_mm_ext,
                                 structural_model = final_sm_ext,
                                 inner_weights = path_weighting,
                                 missing = mean_replacement,
                                 missing_value = "-99")


summary_bike_final_model_ext <- summary(bike_final_model_ext)

predict_bike_final_model_ext <- `predict_pls( model = bike_final_model_ext, 
  technique = predict_DA, noFolds = 10, reps = 10)```