text (version 0.9.50)

textTrainLists: 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.

Description

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.

Usage

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.

See Also

see textTrain textTrainRegression textTrainRandomForest

Examples

# NOT RUN {
word_embeddings <- word_embeddings_4[1:2]
ratings_data <- Language_based_assessment_data_8[5:6]
results <- textTrainLists(
  word_embeddings,
  ratings_data
)
results
comment(results)
# }