plot_predictor_words: Plot Predictor Words

In nlanderson9/languagePredictR: Predict Outcomes from Natural Language

Description

This function plots predictive words from the results of the assess_models function

Usage

plot_predictor_words(
  ...,
  topX,
  colors = c("blue", "orange"),
  plot_titles,
  model_names,
  xaxis_range,
  standard_xaxis = TRUE,
  flip_graphs = FALSE,
  print_individual = TRUE,
  print_summary = TRUE
)

Arguments

...	Output(s) of the language_model function
topX	The number of most-predictive words to plot
colors	A two-element vector containing the colors of the plotted bars. Defaults to c("blue", "orange")
plot_titles	A vector of titles for the plots
model_names	A vector of names for the individual models
xaxis_range	A maximum value for the x-axis
standard_xaxis	If TRUE, the x-axis on all graphs will be the same. If FALSE, it will adjust to fit each individual graph. Defaults to TRUE.
flip_graphs	Flips the graphs horizontally. Defaults to FALSE (low-value outcome variable on the left, high-value outcome variable on the right)
print_individual	If TRUE, prints an individual graph for each model. Defaults to TRUE.
print_summary	If TRUE, prints a summary graph with all models. Defaults to TRUE.

Value

Nothing (this function plots a series of graphs)

See Also

language_model

Examples

strong_movie_review_data$cleanText = clean_text(strong_movie_review_data$text)
mild_movie_review_data$cleanText = clean_text(mild_movie_review_data$text)

# Using language to predict "Positive" vs. "Negative" reviews
# Only for strong reviews (ratings of 1 or 10)
movie_model_strong = language_model(strong_movie_review_data,
                                     outcome = "valence",
                                     outcomeType = "binary",
                                     text = "cleanText",
                                     progressBar = FALSE)

# Using language to predict "Positive" vs. "Negative" reviews
# Only for mild reviews (ratings of 4 or 7)
movie_model_mild = language_model(mild_movie_review_data,
                                     outcome = "valence",
                                     outcomeType = "binary",
                                     text = "cleanText",
                                     progressBar = FALSE)

# Analyze ROC curves
plot_predictor_words(movie_model_strong, movie_model_mild)

nlanderson9/languagePredictR documentation built on June 10, 2021, 11 a.m.