Created
November 8, 2021 15:32
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| mod_1 <- brm(value ~ questionnaire * condition, | |
| data = idealised_data, | |
| seed = 42, | |
| cores = 4) | |
| mod_2 <- brm(value ~ questionnaire + condition, | |
| data = idealised_data, | |
| seed = 42, | |
| cores = 4) | |
| mod_3 <- brm(value ~ questionnaire, | |
| data = idealised_data, | |
| seed = 42, | |
| cores = 4) | |
| mod_4 <- brm(value ~ condition, | |
| data = idealised_data, | |
| seed = 42, | |
| cores = 4) | |
| all_waic <- waic(mod_1, | |
| mod_2, | |
| mod_3, | |
| mod_4) | |
| weight <- function(d_waic, all_d_waic){ | |
| num <- exp(-0.5 * d_waic) | |
| den <- sum(exp(-0.5 * all_d_waic[1]), | |
| exp(-0.5 * all_d_waic[2]), | |
| exp(-0.5 * all_d_waic[3]), | |
| exp(-0.5 * all_d_waic[4])) | |
| return(num / den) | |
| } | |
| all_waic <- c(749.7, 755.4, 754.5, 753.6) | |
| d_waic <- all_waic - all_waic[which.min(all_waic)] | |
| # model 1 is superior | |
| weight(d_waic[1], d_waic) | |
| weight(d_waic[2], d_waic) | |
| weight(d_waic[3], d_waic) | |
| weight(d_waic[4], d_waic) | |
| # compare model 1 to model 4 in terms of Kullback–Leibler discrepancy | |
| weight(d_waic[1], d_waic) / weight(d_waic[4], d_waic) | |
| # evidence ratio as the normalized probability that Model 1 is to be | |
| # preferred over Model 4 | |
| weight(d_waic[1], d_waic) / (weight(d_waic[1], d_waic) + | |
| weight(d_waic[4], d_waic)) |
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