Calculate sample entropy for each trajectory as a measure of the complexity of movements along one specific dimension.

```
mt_sample_entropy(
data,
use = "tn_trajectories",
save_as = "measures",
dimension = "xpos",
m = 3,
r = NULL,
use_diff = TRUE,
verbose = FALSE
)
```

- data
a mousetrap data object created using one of the mt_import functions (see mt_example for details). Alternatively, a trajectory array can be provided directly (in this case

`use`

will be ignored).- use
a character string specifying which trajectory data should be used.

- save_as
a character string specifying where the calculated measures should be stored.

- dimension
a character string specifying the dimension based on which sample entropy should be calculated. By default (xpos), the x-positions are used.

- m
an integer passed on to the sample entropy function (see Details).

- r
a numeric value passed on to the sample entropy function (see Details).

- use_diff
logical indicating if the differences of the dimension values should be computed before calculating sample entropy (which is done by default, see Details).

- verbose
logical indicating whether function should report its progress.

A mousetrap data object (see mt_example).

If a data.frame with label specified in `save_as`

(by default
"measures") already exists, the sample entropy values are added as
additional column.

If not, an additional data.frame will be added.

If a trajectory array was provided directly as `data`

, only the
data.frame will be returned.

`mt_sample_entropy`

calculates the sample entropy for each trajectory as
a measure of its complexity. Hehman et al (2015) provide details on how
sample entropy can be calculated and applied in mouse-tracking (following
Dale et al., 2007). They apply the sample entropy measure to the
differences between adjacent x-positions
(which is also the default here, as in a standard mouse-tracking task with
buttons located in the top-left and right corners mostly the movements in the
horizontal direction are of interest). Besides, they recommend using the
time-normalized trajectories so all trajectories have the same length.

Sample entropy is computed by comparing windows of a fixed size (specified
using `m`

) across all recorded positions. Sample entropy is the
negative natural logarithm of the conditional probability that this window
remains similar across the trial (Hehman et al., 2015). A window is
considered to be similar to another if their distance is smaller than a
specified tolerance value (which can be specified using `r`

). Hehman et
al. (2015) use a tolerance value of 0.2 * standard deviation of all
differences between adjacent x-positions in the dataset (which is the default
implemented here).

Dale, R., Kehoe, C., & Spivey, M. J. (2007). Graded motor
responses in the time course of categorizing atypical exemplars.
*Memory & Cognition, 35*(1), 15-28.

Hehman, E., Stolier, R. M., & Freeman, J. B. (2015). Advanced mouse-tracking
analytic techniques for enhancing psychological science. *Group
Processes & Intergroup Relations, 18*(3), 384-401.

mt_measures for calculating other mouse-tracking measures.

```
# Calculate sample entropy based on time-normalized
# trajectories and merge results with other meausres
# derived from raw trajectories
mt_example <- mt_measures(mt_example)
mt_example <- mt_time_normalize(mt_example,
save_as="tn_trajectories", nsteps=101)
mt_example <- mt_sample_entropy(mt_example,
use="tn_trajectories", save_as="measures",
dimension="xpos", m=3)
```