`R/functional_data.R`

`run_functional_t_test.Rd`

This allows running of a functional t-test for a given alpha on pupil data that has been fitted with b-splines. This is only appropriate for functional difference data, as it assumes we are dealing with condition A - condition B.

`run_functional_t_test(data, pupil, alpha = 0.05)`

- data
a Pupil_difference_data fitted with b-splines

- pupil
column name indicating pupil data to test

- alpha
an alpha level to be set for the t-test

A Pupil_test_data dataframe

```
Sdata <- make_pupillometryr_data(data = pupil_data,
subject = ID,
trial = Trial,
time = Time,
condition = Type)
regressed_data <- regress_data(data = Sdata, pupil1 = RPupil, pupil2 = LPupil)
mean_data <- calculate_mean_pupil_size(data = regressed_data, pupil1 = RPupil, pupil2 = LPupil)
base_data <- baseline_data(data = mean_data, pupil = mean_pupil, start = 0, stop = 100)
#> Baselining for each subject and trial. If this is not the intended behaviour you may wish to do this manually.
differences <- create_difference_data(data = base_data, pupil = mean_pupil)
#> Hard minus Easy -- relevel condition if this is not the intended outcome
spline_data <- create_functional_data(data = differences, pupil = mean_pupil, basis = 10, order = 4)
ft_data <- run_functional_t_test(data = spline_data, pupil = mean_pupil, alpha = 0.05)
#> critical value for n = 8 is 2.36462425159278
```