EnTraineR (version 1.0.0)

trainer_LinearModel: Trainer: Interpret FactoMineR::LinearModel with an LLM-ready prompt

Description

Builds an English-only, audience-tailored prompt to interpret a FactoMineR::LinearModel result. Handles model selection (AIC/BIC) and instructs how to interpret deviation contrasts (sum-to-zero) for factors. Works for ANOVA, ANCOVA, and multiple regression.

Usage

trainer_LinearModel(
  lm_obj,
  introduction = NULL,
  alpha = 0.05,
  t_test = NULL,
  audience = c("beginner", "applied", "advanced"),
  summary_only = FALSE,
  llm_model = "llama3",
  generate = FALSE
)

Value

Character prompt or list.

Arguments

lm_obj

An object returned by FactoMineR::LinearModel(...).

introduction

Optional character string giving the study context.

alpha

Numeric significance level (default 0.05).

t_test

Optional character vector to filter the T-test section by factor names and/or interactions (e.g. "FactorA" or "FactorA:FactorB").

audience

One of c("beginner","applied","advanced").

summary_only

Logical; if TRUE, return a 3-bullet executive summary.

llm_model

Character model name for the generator (e.g., "llama3").

generate

Logical; if TRUE, call the generator.

Examples

# --- Example 1: multiple regression with selection (ham) -------------------
data(ham)
if (requireNamespace("FactoMineR", quietly = TRUE)) {
  intro_ham <- "A sensory analysis institute wants to know if it's possible to predict
  the overall liking of a ham from its sensory description.
  A trained panel used the following attributes to describe 21 hams:
  Juiciness, Crispy, Tenderness, Pasty, Fibrous, Salty, Sweet, Meaty,
  Seasoned, Metallic, Ammoniated, Fatty, Braised, Lactic.
  Afterward, an Overall Liking score was assigned to each of the hams."
  # collapse whitespace safely without extra packages
  intro_ham <- gsub("\n", " ", intro_ham)
  intro_ham <- gsub("\\s+", " ", intro_ham)

  res <- FactoMineR::LinearModel(`Overall liking` ~ ., data = ham, selection = "bic")
  pr  <- trainer_LinearModel(res, introduction = intro_ham, audience = "advanced",
                             generate = FALSE)
  cat(pr)
}

# --- Example 2: interaction with a categorical factor (deforestation) ------
data(deforestation)
if (requireNamespace("FactoMineR", quietly = TRUE)) {
  intro_flume <- "The study's goal is to determine how river deforestation affects
  the relationship between water and air temperature.
  The dataset contains maximum air and water temperatures measured over
  28 ten-day periods before deforestation and 28 periods after deforestation.
  The main objective is to understand if and how the link between air and
  water temperature changes after deforestation."
  intro_flume <- gsub("\n", " ", intro_flume)
  intro_flume <- gsub("\\s+", " ", intro_flume)

  res <- FactoMineR::LinearModel(Temp_water ~ Temp_air * Deforestation,
                                 data = deforestation, selection = "none")
  pr  <- trainer_LinearModel(res, introduction = intro_flume, audience = "advanced",
                             generate = FALSE)
  cat(pr)
}