Mean_Variance: Mean Variance Estimates

In samplr: Compare Human Performance to Sampling Algorithms

Mean Variance Estimates

Description

Estimates number of samples and prior parameters of the Bayesian Sampler using the Mean/Variance relationship as shown by \insertCitesundh2023UnifiedExplanationVariabilitysamplr. For consistency with the Bayesian Sampler function we call beta the prior parameter, and b0 and b1 slope and intercept respectively.

Usage

Mean_Variance(rawData, idCol)

Arguments

rawData	Dataframe with the following column variables for N repetitions of each unique query: participant ID ('id'), response query 1, response query 2, ... , response query N
idCol	Name of the 'ID' column.

Value

A dataframe with values for the intercept (b0) and slope (b1) of the estimated regression, as well as estimates for N, d, and beta (termed b in the paper) for each participant.

References

\insertAllCited

Examples

library(dplyr)
library(tidyr)
library(magrittr)
library(samplrData)
pct_to_prob <- function(x){x/100}
data <- sundh2023.meanvariance.e3 %>%
  group_by(ID, querydetail) %>% 
  mutate(iteration = LETTERS[1:n()]) %>% 
  pivot_wider(id_cols = c(ID, querydetail), 
      values_from = estimate, names_from = iteration) %>% 
  mutate(across(where(is.numeric), pct_to_prob)) %>% 
  ungroup %>% 
  select(-querydetail)
head(data)
head(Mean_Variance(data, "ID"))

samplr documentation built on April 4, 2025, 12:30 a.m.