etextlogitp

etextlogitp is a function used in statistical software for fitting logistic regression models to binary outcomes using predictors that include text-derived features. The name is commonly encountered in documentation for niche econometrics and text analytics toolkits and may refer to an implementation that combines an extended text feature set with a logistic probability model. In typical use, etextlogitp accepts a design matrix with numeric predictors and text-derived features such as term frequencies or TF-IDF scores, along with a binary response y. The function estimates the probability p that y equals 1 through a logit link, often under regularization to handle high-dimensional feature spaces. Options commonly include L1 and L2 penalties, cross-validation to select tuning parameters, and calibration routines to improve probability estimates. Output typically includes estimated coefficients, standard errors, z or Wald statistics, p-values, the fitted probabilities, and model fit measures such as log-likelihood, AIC, or BIC. Some implementations provide predict methods to generate class labels, confidence intervals for probabilities, and feature importance summaries. As with logistic regression, etextlogitp assumes independence of observations and a correct specification of the link function. The technique is particularly used in text classification tasks where the predictor set is large and sparse, but limitations include potential overfitting in small samples and interpretability challenges for high-dimensional features.