incR (version 2.1.0)

incRscan: Automated scoring of incubation

Description

This is the core algorithm of incR and classifies time points as 1's or 0's depending on whether or not the incubating individual is considered to be on the eggs. The algorithm uses night variation to daily calibrate itself to temperature variation when the incubating individual is assumed to be on the eggs. A major assumption of this algorithm is that there is a period of time in which temperature can be assumed to be constant or representative of time windows of constant incubation. This time window is defined by two arguments: lower.time and upper.time. The function is optimised to work using a data frame produced by incRprep.

Usage

incRscan(
  data,
  temp.name,
  lower.time,
  upper.time,
  sensitivity,
  temp.diff,
  temp.diff.threshold,
  maxNightVariation,
  env.temp
)

Value

The function returns a list with two objects. The first object, named incRscan_data, is the original data frame with an extra column named 'incR_score'. This variable is formed by 1's and 0's, representing whether the incubating individual is inside (1) or outside the nest (0).

The second object, named incRscan_threshold, is a data frame with one day per row. Four columns tell the user the thresholds employed to calculate the 'incR_score' column. A fifth column accounts for the ratio between temperature variation in the calibrating window and the variation in temperature between 11am and 3pm for each day morning. The lower this value the more clear the pattern between night and day variation and, therefore, stronger the signal in the data. This value may serve the user as an indication of the signal / noise ratio in the analysed data set.

Arguments

data

data frame for analysis. It must contained four columns named as follow: date, temp1, dec_time and index, where temp1 is the difference between the ith and i-1th temperature recordings; dec_time is time in decimal hours; and index is a running number from 1 to N, N being the total number of observations. incRprep returns a data frame with these variables and the correct names, ready to be passed through incRscan.

temp.name

(character object) name of the column containing temperature data in data.

lower.time

lower limit of time window for calibration (numeric).

upper.time

upper limit of time window for calibration (numeric).

sensitivity

ratio of reduction in temperature threshold. When nest temperature does not drop close to environmental temperatures, this value can be kept to 1. If nest temperature follows environmental temperature at any point, then adjustment of this value may be required to detect short on/off-bouts at lower nest temperatures (see details).

temp.diff

deprecated. Use temp.diff.threshold.

temp.diff.threshold

threshold for temperature difference between env.temp and an observation which triggers the sensitivity parameter.

maxNightVariation

maximum temperature variation between two consecutive points within the calibrating window that is considered normal of this period. If this variation value is surpassed, the calibrating window is discarded and a previous night is used for calibration.

env.temp

name of a column containing environmental temperatures.

Author

Pablo Capilla-Lasheras

Details

For further details about the calculation performed by incRscan, consult the package vignettes and the associated publications.

See Also

incRprep incRenv

Examples

# incR_procdata is a dataframe processed by incRprep and incRenv.
# it contains suitable information to run incRscan
data(incR_procdata)

incubation.analysis <- incRscan (data=incR_procdata, 
                                  temp.name="temperature",
                                  lower.time=22,
                                  upper.time=3,
                                  sensitivity=0.15,
                                  temp.diff.threshold=5,
                                  maxNightVariation=2,
                                  env.temp="env_temp")
inc.data <- incubation.analysis[[1]]
inc.thresholds <- incubation.analysis[[2]]