mt_aggregate_per_subject: Aggregate mouse-tracking data per condition separately for each subject.

Description

mt_aggregate_per_subject can be used for aggregating mouse-tracking measures (or trajectories) per condition separately for each subject. One or more condition variables can be specified using use2_variables. Aggregation will be performed separately for each level of the condition variables. mt_aggregate_per_subject is a wrapper function for mt_reshape.

Usage

mt_aggregate_per_subject(
  data,
  use = "measures",
  use_variables = NULL,
  use2 = "data",
  use2_variables = NULL,
  subject_id,
  trajectories_long = TRUE,
  ...
)

Value

A data.frame containing the aggregated data.

Arguments

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 dataset should be aggregated. The corresponding data are selected from data using data[[use]]. Usually, this value corresponds to either "tn_trajectories" or "measures", depending on whether the time-normalized trajectories or derived measures should be aggregated.
use_variables: a character vector specifying the mouse-tracking variables to aggregate. If a data.frame with mouse-tracking measures is provided as data, this corresponds to the column names. If a trajectory array is provided, this argument should specify the labels of respective array dimensions. If unspecified, all variables will be aggregated.
use2: a character string specifying where the data containing the condition information can be found. Defaults to "data" as data[["data"]] usually contains all non mouse-tracking trial data. Alternatively, a data.frame can be provided directly.
use2_variables: a character string (or vector) specifying the variables (in data[[use2]]) across which the trajectories / measures will be aggregated. For each combination of levels of the grouping variable(s), aggregation will be performed separately using summarize_at.
subject_id: a character string specifying which column contains the subject identifier.
trajectories_long: logical indicating if the reshaped trajectories should be returned in long or wide format. If TRUE, every recorded position in a trajectory is placed in another row (whereby the order of the positions is logged in the variable mt_seq). If FALSE, every trajectory is saved in wide format and the respective positions are indexed by adding an integer to the corresponding label (e.g., xpos_1, xpos_2, ...). Only relevant if data[[use]] contains trajectories.
...: additional arguments passed on to mt_reshape (such as subset).

Author

Pascal J. Kieslich

Felix Henninger

Examples

Run this code

# Time-normalize trajectories
mt_example <- mt_time_normalize(mt_example)

# Aggregate time-normalized trajectories per condition
# separately per subject
average_trajectories <- mt_aggregate_per_subject(
  mt_example,
  use="tn_trajectories",
  use2_variables="Condition",
  subject_id="subject_nr"
)


# Calculate mouse-tracking measures
mt_example <- mt_measures(mt_example)

# Aggregate measures per condition
# separately per subject
average_measures <- mt_aggregate_per_subject(
  mt_example,
  use="measures",
  use_variables=c("MAD", "AD"),
  use2_variables="Condition",
  subject_id="subject_nr"
)