Working with data from an adjusting amount procedure

library(tempodisco)

The idea of an adjusting amount procedure is to “titrate” an individual’s indifference point at a given delay. After running an experiment of this type, you may have a data table where each row corresponds to a different choice the participant made:

data("adj_amt_sim")
head(adj_amt_sim)
#>   del val_del val_imm imm_chosen trial_idx
#> 1   7     800     400      FALSE         1
#> 2   7     800     600      FALSE         2
#> 3   7     800     700      FALSE         3
#> 4   7     800     750      FALSE         4
#> 5   7     800     775      FALSE         5
#> 6  30     800     400      FALSE         6

In this case, you need some column to differentiate different “blocks” of the experiment, so that a separate indifference point can be computed for each block. In the data above, as is standard, each delay corresponded to a different block. We can run adj_amt_indiffs to compute the indifference points for these blocks:

scored <- adj_amt_indiffs(adj_amt_sim)
head(scored)
#>   del   indiff
#> 1   7 0.984375
#> 2  30 0.859375
#> 3  90 0.046875
#> 4 180 0.453125
#> 5 360 0.015625

By default, adj_amt_indiffs assumes that there is a column called del that differentiates between different blocks. You can specify this manually using the block_indic column. Similarly, adj_amt_indiffs assumes that the rows are already in chronological order within blocks, so that the final row corresponds to the last decision. To override this behaviour, we can use the order_indic argument to pass the name of a column that specifies the order in which decisions took place:

scored <- adj_amt_indiffs(adj_amt_sim, block_indic = 'del', order_indic = 'trial_idx')
head(scored)
#>   del   indiff
#> 1   7 0.984375
#> 2  30 0.859375
#> 3  90 0.046875
#> 4 180 0.453125
#> 5 360 0.015625

With our indifference points computed, we can fit an indifference point model to the data:

mod <- td_ipm(scored, discount_function = c('hyperbolic'))
plot(mod)