preprocess_data {bistablehistory}

Preprocesses time-series data for fitting

Description

Performs sanity checks (e.g., whether data can be used as a data.frame), computes duration of dominance phases (if necessary), assumes a single entry for any missing session, run, random_effect.

Usage

preprocess_data(
  data,
  state,
  duration = NULL,
  onset = NULL,
  random_effect = NULL,
  session = NULL,
  run = NULL
)

Arguments

data

A table with one or many time-series.

state

String, the name of the column that specifies perceptual state. The column type should be a factor with two or three levels (the third level is assumed to correspond to a transition/mixed phase) or should be convertible to a two level factor (as it would be impossible to infer the identity of transition/ mixed phase).

duration

String, name of the column with duration of individual perceptual dominance phases. Optional, you can specify onset instead.

onset

String, name of the column with onsets of the perceptual dominance states. Optional, used to compute duration of the dominance phases, if these are not provided explicitly via duration parameter.

random_effect

String, name of the column that identifies random effect, e.g. individual participants, stimuli for a single participant, etc. If omitted, no random effect is assumed. If specified and there is more than one level (participant, stimulus, etc.), it is used in a hierarchical model.

session

String, name of the column that identifies unique experimental session for which a mean dominance phase duration will be computed (see norm_tau parameter). Code assumes that session IDs are different within a participant but can be the same between them. If omitted, a single mean dominance duration based on the entire time series is used.

run

String, name of the column that identifies unique runs/blocks. If omitted, the data is assumed to belong to a single time series. Code assumes that run IDs are different within an experimental session but can be the same between the session. E.g. session A, runs 1, 2, 3.. and session B, runs 1, 2, 3 but not session A, runs 1, 2, 1.

Value

A tibble with columns

• state

• duration

• random

• irandom - integer, index of random values,

• session

• run

• session_tmean - numeric, mean duration of clear percepts for every combination of random and session.

• is_used - integer, whether computed history value needs to be used for linear model fitting.

• run_start - integer, 1 for the first row of the run time-series.

Examples

df <- preprocess_data(br_singleblock, state="State", duration="Duration")