predict_lkt: Predict for LKT Models

In LKT: Logistic Knowledge Tracing

Predict for LKT Models

Description

Generates predictions and evaluates logistic regression models tailored for learning data, specifically designed for Logistic Knowledge Tracing (LKT) models. This function provides flexibility in returning either just the predicted probabilities or both the predictions and key evaluation statistics.

Usage

predict_lkt(
  modelob,
  data,
  fold = NULL,
  return_stats = FALSE,
  min_pred_limit = 1e-05,
  max_pred_limit = 0.99999
)

Arguments

modelob	An LKT model object containing necessary model coefficients and predictors for generating predictions.
data	A dataset including predictor variables, the outcome variable CF..ansbin., and fold information.
fold	Optional. Numeric vector specifying which folds to include for prediction. If NULL or empty, uses all data.
return_stats	Logical. If TRUE, returns both predictions and evaluation statistics (Log-Likelihood, AUC, RMSE, R^2). If FALSE, returns only the predictions.
min_pred_limit	Minimum prediction limit. Default is 0.00001.
max_pred_limit	Maximum prediction limit. Default is 0.99999.

Value

If return_stats is FALSE, returns a list containing:

• predictions: The predicted probabilities for each observation in the specified fold(s).

If return_stats is TRUE, returns a list containing:

• predictions: The predicted probabilities for each observation in the specified fold(s).

• LL: Log-Likelihood of the model given the actual outcomes.

• AUC: Area Under the ROC Curve.

• RMSE: Root Mean Squared Error.

• R2: R-squared value, indicating the proportion of variance explained by the model.

LKT documentation built on July 3, 2024, 5:11 p.m.