(experimental) Compute cross-validated correlations for different sample-sizes of a data set. The cross-validation process can be repeated several times to enhance the reliability of the evaluation.

(experimental) Compute cross-validated correlations for different sample-sizes of a data set. The cross-validation process can be repeated several times to enhance the reliability of the evaluation.

textTrainN(
  x,
  y,
  sample_percents = c(25, 50, 75, 100),
  handle_word_embeddings = "individually",
  n_cross_val = 1,
  sampling_strategy = "subsets",
  use_same_penalty_mixture = TRUE,
  model = "regression",
  penalty = 10^seq(-16, 16),
  mixture = c(0),
  seed = 2024,
  ...
)

Arguments

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.

sample_percents

(numeric) Numeric vector that specifies the percentages of the total number of data points to include in each sample (default = c(25,50,75,100), i.e., correlations are evaluated for 25 each new sample.

handle_word_embeddings

Determine whether to use a list of word embeddings or an individual word_embedding (default = "individually", also "concatenate"). If a list of word embeddings are provided, then they will be concatenated.

n_cross_val

(numeric) Value that determines the number of times to repeat the cross-validation (i.e., number of tests). (default = 1, i.e., cross-validation is only performed once). Warning: The training process gets proportionately slower to the number of cross-validations, resulting in a time complexity that increases with a factor of n (n cross-validations).

sampling_strategy

Sample a "random" sample for each subset from all data or sample a "subset" from the larger subsets (i.e., each subset contain the same data).

use_same_penalty_mixture

If TRUE it only searches the penalty and mixture search grid once, and then use the same thereafter; if FALSE, it searches the grid every time.

model

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

penalty

(numeric) Hyper parameter that is tuned (default = 10^seq(-16,16)).

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).

seed

(numeric) Set different seed (default = 2024).

...

Additional parameters from textTrainRegression.

Value

A tibble containing correlations for each sample. If n_cross_val > 1, correlations for each new cross-validation, along with standard-deviation, mean and standard error of correlation is included in the tibble. The information in the tibble is visualised via the textTrainNPlot function.

See also

See textTrainNPlot.

Examples

# Compute correlations for 25%, 50%, 75% and 100% of the data in word_embeddings and perform
# cross-validation thrice.

if (FALSE) { # \dontrun{
tibble_to_plot <- textTrainN(
  x = word_embeddings_4$texts$harmonytext,
  y = Language_based_assessment_data_8$hilstotal,
  sample_percents = c(25, 50, 75, 100),
  n_cross_val = 3
)

# tibble_to_plot contains correlation-coefficients for each cross_validation and
# standard deviation and mean value for each sample. The tibble can be plotted
# using the testTrainNPlot function.

# Examine tibble
tibble_to_plot
} # }