NaileR (version 1.2.3)

nail_qda: Interpret QDA data

Description

Generate an LLM response to analyze QDA data.

Usage

nail_qda(
  dataset,
  formul,
  firstvar,
  lastvar = length(colnames(dataset)),
  introduction = NULL,
  request = NULL,
  conclusion = NULL,
  model = "llama3",
  isolate.groups = FALSE,
  drop.negative = FALSE,
  proba = 0.05,
  generate = FALSE
)

Value

A data frame, or a list of data frames, containing the LLM's prompt and response (if generate = TRUE).

Arguments

dataset

a data frame made up of at least two categorical variables (product, panelist) and a set of quantitative variables (sensory attributes).

formul

the analyis of variance model to be evaluated for each sensory attribute.

firstvar

the index of the first sensory attribute.

lastvar

the index of the last sensory attribute.

introduction

the introduction for the LLM prompt.

request

the request for the LLM prompt.

conclusion

the conclusion for the LLM prompt.

model

the model name ('llama3' by default).

isolate.groups

a boolean that indicates whether to give the LLM a single prompt, or one prompt per product.

drop.negative

a boolean that indicates whether to drop negative v.test values for interpretation (keeping only positive v.tests).

proba

the significance threshold considered to characterize the products (by default 0.05).

generate

a boolean that indicates whether to generate the LLM response. If FALSE, the function only returns the prompt.

Details

This function directly sends a prompt to an LLM. Therefore, to get a consistent answer, we highly recommend to customize the parameters introduction and request and add all relevant information on your data for the LLM. We also recommend renaming the columns with clear, unshortened and unambiguous names.

Additionally, if isolate.groups = TRUE, you will need an introduction and a request that take into account the fact that only one group is analyzed at a time.

Examples

if (FALSE) {
# Processing time is often longer than ten seconds
# because the function uses a large language model.

### Example 1: QDA data on chocolates with isolate.groups = FALSE ###
library(NaileR)
library(SensoMineR)
data(chocolates)

intro_sensochoc <- "Six chocolates were measured according
to sensory attributes by a trained panel.
I will give you the results from this study.
You will have to identify what sets these chocolates apart."
intro_sensochoc <- gsub('\n', ' ', intro_sensochoc) |>
stringr::str_squish()

req_sensochoc <- "Please explain what makes each chocolate different
and provide a sensory profile of each chocolate, as well as a name."
req_sensochoc <- gsub('\n', ' ', req_sensochoc) |>
stringr::str_squish()

res_nail_qda <- nail_qda(sensochoc,
                         formul="~Product+Panelist",
                         firstvar = 5,
                         introduction = intro_sensochoc,
                         request = req_sensochoc,
                         model = 'llama3',
                         isolate.groups = FALSE,
                         drop.negative = FALSE,
                         proba = 0.05,
                         generate = TRUE)

cat(res_nail_qda$prompt)
cat(res_nail_qda$response)

### Example 2: QDA data on chocolates with isolate.groups = TRUE ###
library(NaileR)
library(SensoMineR)
data(chocolates)

intro_sensochoc <- "A chocolate was measured according
to sensory attributes by a trained panel.
I will give you the results from this study.
You will have to identify the characteristics of this chocolate."
intro_sensochoc <- gsub('\n', ' ', intro_sensochoc) |>
stringr::str_squish()

req_sensochoc <- "Please provide a detailed sensory profile for this chocolate,
as well as a name."
req_sensochoc <- gsub('\n', ' ', req_sensochoc) |>
stringr::str_squish()

res_nail_qda <- nail_qda(sensochoc,
                         formul="~Product+Panelist",
                         firstvar = 5,
                         introduction = intro_sensochoc,
                         request = req_sensochoc,
                         model = 'llama3',
                         isolate.groups = TRUE,
                         drop.negative = FALSE,
                         proba = 0.05,
                         generate = TRUE)

cat(res_nail_qda[[1]]$prompt)
cat(res_nail_qda[[1]]$response)
}