Estimates number of samples and prior parameters of the Bayesian Sampler using the Mean/Variance relationship as shown by (Sundh et al. 2023) . For consistency with the Bayesian Sampler function we call beta the prior parameter, and b0 and b1 slope and intercept respectively.
Mean_Variance(rawData, idCol)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
Name of the 'ID' column.
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.
Sundh J, Zhu J, Chater N, Sanborn A (2023). “A Unified Explanation of Variability and Bias in Human Probability Judgments: How Computational Noise Explains the Mean Variance Signature.” Journal of Experimental Psychology: General, 152(10), 2842--2860. doi:10.1037/xge0001414 .
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
library(tidyr)
library(magrittr)
#>
#> Attaching package: ‘magrittr’
#> The following object is masked from ‘package:tidyr’:
#>
#> extract
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)
#> # A tibble: 6 × 4
#> ID A B C
#> <dbl> <dbl> <dbl> <dbl>
#> 1 101 0.9 0.3 0.2
#> 2 101 0.5 0.7 0.3
#> 3 101 0.9 0.1 0.1
#> 4 101 0.05 0.2 0.2
#> 5 101 0.8 0.8 0.7
#> 6 101 0.2 0.1 0.2
head(Mean_Variance(data, "ID"))
#> ID b0 b1 N d beta
#> 1 101 -0.04111800 0.5202392 1.922193 0.08652294 0.2011161
#> 2 101 -0.05357651 0.7175646 1.393603 0.08126903 0.1352381
#> 3 101 -0.01880811 0.2170622 4.606975 0.09583234 0.5461807
#> 4 101 -0.02522666 0.3277590 3.051022 0.08402777 0.3081583
#> 5 101 -0.02727728 0.4445454 2.249489 0.06567286 0.1700681
#> 6 101 -0.03305531 0.4535753 2.204705 0.07914042 0.2072916