`R/2_2_textTrainRegression.R`

`textTrainRegression.Rd`

Train word embeddings to a numeric variable.

```
textTrainRegression(
x,
y,
x_append = NULL,
append_first = FALSE,
cv_method = "validation_split",
outside_folds = 10,
outside_strata_y = "y",
outside_breaks = 4,
inside_folds = 3/4,
inside_strata_y = "y",
inside_breaks = 4,
model = "regression",
eval_measure = "default",
preprocess_step_center = TRUE,
preprocess_step_scale = TRUE,
preprocess_PCA = NA,
penalty = 10^seq(-16, 16),
mixture = c(0),
first_n_predictors = NA,
impute_missing = FALSE,
method_cor = "pearson",
model_description = "Consider writing a description of your model here",
multi_cores = "multi_cores_sys_default",
save_output = "all",
seed = 2020,
...
)
```

- x
Word embeddings from textEmbed (or textEmbedLayerAggregation). If several word embedding are provided in a list they will be concatenated.

- y
Numeric variable to predict.

- x_append
Variables to be appended after the word embeddings (x); if wanting to preappend them before the word embeddings use the option first = TRUE. If not wanting to train with word embeddings, set x = NULL.

- append_first
(boolean) Option to add variables before or after all word embeddings.

- cv_method
Cross-validation method to use within a pipeline of nested outer and inner loops of folds (see nested_cv in rsample). Default is using cv_folds in the outside folds and "validation_split" using rsample::validation_split in the inner loop to achieve a development and assessment set (note that for validation_split the inside_folds should be a proportion, e.g., inside_folds = 3/4); whereas "cv_folds" uses rsample::vfold_cv to achieve n-folds in both the outer and inner loops.

- outside_folds
Number of folds for the outer folds (default = 10).

- outside_strata_y
Variable to stratify according (default y; can set to NULL).

- outside_breaks
The number of bins wanted to stratify a numeric stratification variable in the outer cross-validation loop.

- inside_folds
The proportion of data to be used for modeling/analysis; (default proportion = 3/4). For more information see validation_split in rsample.

- inside_strata_y
Variable to stratify according (default y; can set to NULL).

- inside_breaks
The number of bins wanted to stratify a numeric stratification variable in the inner cross-validation loop.

- model
Type of model. Default is "regression"; see also "logistic" for classification.

- eval_measure
Type of evaluative measure to select models from. Default = "rmse" for regression and "bal_accuracy" for logistic. For regression use "rsq" or "rmse"; and for classification use "accuracy", "bal_accuracy", "sens", "spec", "precision", "kappa", "f_measure", or "roc_auc",(for more details see the yardstick package).

- preprocess_step_center
normalizes dimensions to have a mean of zero; default is set to TRUE. For more info see (step_center in recipes).

- preprocess_step_scale
normalize dimensions to have a standard deviation of one. For more info see (step_scale in recipes).

- preprocess_PCA
Pre-processing threshold for PCA (to skip this step set it to NA). Can select amount of variance to retain (e.g., .90 or as a grid c(0.80, 0.90)); or number of components to select (e.g., 10). Default is "min_halving", which is a function that selects the number of PCA components based on number of participants and feature (word embedding dimensions) in the data. The formula is: preprocess_PCA = round(max(min(number_features/2), number_participants/2), min(50, number_features))).

- penalty
hyper parameter that is tuned

- mixture
A number between 0 and 1 (inclusive) that reflects the proportion of L1 regularization (i.e. lasso) in the model (for more information see the linear_reg-function in the parsnip-package). When mixture = 1, it is a pure lasso model while mixture = 0 indicates that ridge regression is being used (specific engines only).

- first_n_predictors
by default this setting is turned off (i.e., NA). To use this method, set it to the highest number of predictors you want to test. Then the X first dimensions are used in training, using a sequence from Kjell et al., 2019 paper in Psychological Methods. Adding 1, then multiplying by 1.3 and finally rounding to the nearest integer (e.g., 1, 3, 5, 8). This option is currently only possible for one embedding at the time.

- impute_missing
default FALSE (can be set to TRUE if something else than word_embeddings are trained).

- method_cor
Type of correlation used in evaluation (default "pearson"; can set to "spearman" or "kendall").

- model_description
Text to describe your model (optional; good when sharing the model with others).

- multi_cores
If TRUE it enables the use of multiple cores if the computer system allows for it (i.e., only on unix, not windows). Hence it makes the analyses considerably faster to run. Default is "multi_cores_sys_default", where it automatically uses TRUE for Mac and Linux and FALSE for Windows.

- save_output
Option not to save all output; default "all". see also "only_results" and "only_results_predictions".

- seed
Set different seed.

- ...
For example settings in yardstick::accuracy to set event_level (e.g., event_level = "second").

A (one-sided) correlation test between predicted and observed values; tibble of predicted values, as well as information about the model (preprossing_recipe, final_model and model_description).

```
# \donttest{
results <- textTrainRegression(
x = word_embeddings_4$texts$harmonytext,
y = Language_based_assessment_data_8$hilstotal,
multi_cores = FALSE # This is FALSE due to CRAN testing and Windows machines.
)
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 2 breaks instead.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Warning: The number of observations in each quantile is below the recommended threshold of 20.
#> • Stratification will use 1 breaks instead.
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> Error in purrr::map(.x = results_nested_resampling$inner_resamples, .f = summarize_tune_results, model = model, eval_measure = eval_measure, penalty = penalty, mixture = mixture, preprocess_PCA = preprocess_PCA, variable_name_index_pca = variable_name_index_pca, first_n_predictors = first_n_predictors, preprocess_step_center = preprocess_step_center, preprocess_step_scale = preprocess_step_scale, impute_missing = impute_missing): ℹ In index: 1.
#> Caused by error in `map()`:
#> ℹ In index: 1.
#> Caused by error in `purrr::pmap()`:
#> ℹ In index: 1.
#> Caused by error in `check_installs()`:
#> ! This engine requires some package installs: 'glmnet'
# }
```