Individually trains word embeddings from several text variables to several numeric or categorical variables. It is possible to have word embeddings from one text variable and several numeric/categprical variables; or vice verse, word embeddings from several text variables to one numeric/categorical variable. It is not possible to mix numeric and categorical variables.

textTrainLists(
  x,
  y,
  force_train_method = "automatic",
  save_output = "all",
  method_cor = "pearson",
  model = "regression",
  eval_measure = "rmse",
  p_adjust_method = "holm",
  ...
)

Arguments

x	Word embeddings from textEmbed (or textEmbedLayerAggreation).
y	Tibble with several numeric or categorical variables to predict. Please note that you cannot mix numeric and categorical variables.
force_train_method	Default is automatic; see also "regression" and "random_forest".
save_output	Option not to save all output; default "all". see also "only_results" and "only_results_predictions".
method_cor	Default "Pearson".
model	Type of model to use in regression; default is "regression"; see also "logistic". (To set different random forest algorithms see extremely_randomised_splitrule parameter in textTrainRandomForest)
eval_measure	Type of evaluative measure to assess models on.
p_adjust_method	Method to adjust/correct p-values for multiple comparisons (default = "holm"; see also "none", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr").
...	Arguments from textTrainRegression or textTrainRandomForest the textTrain function.

Value

Correlations between predicted and observed values.

Examples

if (FALSE) {
wordembeddings <- wordembeddings4[1:2]
ratings_data <- Language_based_assessment_data_8[5:6]
results <- textTrainLists(
  wordembeddings,
  ratings_data
)
results
comment(results)
}

Arguments

Value

See also

Examples