Package 'pawscore'

Create a new pain model

Description

Create a new pain model based on a set of paw features, corresponding pain stimuli, and corresponding strain identity.

Usage

create_pain_model(
  paw.features,
  strains = NULL,
  pain.stimulus = NULL,
  strain.standards = jones2020.standards,
  feature.set = c("post.peak", "pre.peak")
)

Arguments

paw.features	the paw features returned by extract_features
strains	a vector containing strain information for each mouse
pain.stimulus	a vector containing stimulus information for each mouse
strain.standards	z-scores (centering and scaling) information by strain
feature.set	use either pre-peak or post-peak features

Value

pain model

---

Create new strain standards

Description

Creates a new reference centering and scaling for the paw features of the given strain or strains.

Usage

create_strain_standard(paw.features, strain)

Arguments

paw.features	list of extracted paw features
strain	character string or a vector of strain names, each of which is a character string, matching in length and order the list of paw features.

Value

list of pre-peak and post-peak strain standards, indexed by strain

---

Default parameters for extracting paw features

Description

Default parameters for extracting paw features

Usage

default_parameters()

Value

parameters used for Jones et al. (2020)

---

Default strain-based standards for paw features

Description

Default strain-based standards for paw features

Usage

default_standards()

Value

standards used for Jones et al. (2020)

---

Extract features for paw time series

Description

Extract features for paw time series

Usage

extract_features(
  x,
  y = NULL,
  parameters = default_parameters(),
  diagnostics = FALSE
)

Arguments

x	time series of horizontal paw movement. Alternatively, a two column matrix of x and y time series, respectively.
y	time series of vertical paw movement, or NULL if x is a two column matrix.
parameters	contains information about frames per second, filtering, windowing, and thresholds, for paw features (see default_parameters, or use set_parameters to modify the defaults).
diagnostics	set to TRUE will record intermediate values used when computing paw features. This information can be helpful for debugging parameter choices. The default, FALSE, is to not record these values.

Value

pre-peak and post-peak paw features (plus diagnostics, if enabled)

Examples

# example usage with a track from Jones et al. (2020)
track    <- jones2020.tracks[[1]]
features <- extract_features(track$time.series)

---

Jones et al. (2020) paw trajectory data

Description

Paw trajectory time series, strain, and stimulus information for the cohort 1 data used in Jones et al.

Usage

jones2020.tracks

Format

A list of paw trajectories, each containing:

id

A unique id for each mouse

strain

The corresponding mouse strain

stimulus

The stimulus used

time.series

The paw trajectory when stimulus was applied

Source

Jones et al. (2020) A machine-vision approach for automated pain measurement at millisecond timescales. eLife 9:e57258 doi:10.7554/eLife.57258

---

Identify pain class based on pain score

Description

Convenience function to convert pain scores to binary or trinary pain classifications, e.g. pain / non-pain or pain / low-pain / high-pain. Note that trinary classifications are only valid for pain models fit with trinary classes (e.g. Jones et al. 2020). Otherwise the scale of the score is arbitrary, and the boundary between low and high pain is not scaled to be 1.

Usage

pain_class(score, type = c("binary", "trinary"))

Arguments

score	pain scores generated by pain_score
type	binary or trinary (ternary) pain classifications

Value

one or more pain classes

---

Scoring pain from paw features

Description

Returns a pain score based on Jones et al. 2020 or the given pain.model. Pain scores are standardized so that increasingly positive (negative) values correspond to increasingly likely to be painful (unlikely to be painful) experiences. Pain scores based on pain models derived from trinary pain stimuli will additionally be scaled such that the transition between low and high pain is occurs at 1. Scores are not comparable across pain models.

Usage

pain_score(
  paw.features,
  strains = NULL,
  strain.standards = jones2020.standards,
  feature.set = c("post.peak", "pre.peak"),
  pain.model = NULL
)

Arguments

paw.features	the paw features returned by extract_features
strains	a vector containing strain information for each mouse
strain.standards	z-scores (centering and scaling) information by strain
feature.set	use either pre-peak or post-peak features
pain.model	a pain model returned by create_pain_model or NULL, in which case the model from Jones et al. 2020 is used.

Value

one or more pain scores as a vector

---

paws: Pain Assessment at Withdrawal Speeds

Description

Automated pain scoring from paw withdrawl tracking data based on Jones et al. (2020) A machine-vision approach for automated pain measurement at millisecond timescales. This R package takes paw trajectory data in response to a stimulus and provides an automated scoring of pain.

---

Convenience function for changing parameters

Description

Convenience function for changing parameters

Usage

set_parameters(..., based.on = default_parameters())

Arguments

...	name and value of parameters to set (e.g. fps=1500)
based.on	an existing complete set of parameters to modify (see default_parameters).

Value

the modified list of parameters

---