nondecreasing_length_sampler {Xcertainty}R Documentation

MCMC sampler for measurements of individuals with replicates but no age information.

Description

Build an MCMC sampler that uses calibration data to estimate measurements that are assumed to be non-decreasing in time. This sampler is well suited for when individuals have replicate measurements across time points but do not have age information. The model estimates changes in unique combinations of Subject/Measurement pairs over Timepoints.

Usage

nondecreasing_length_sampler(data, priors, package_only = FALSE)

Arguments

data

Photogrammetric data formatted for Xcertainty models, required to be an object with class obs.parsed, which can be obtained by running parse_observations()

priors

list with components that define the model's prior distribution. See help("flatten_data") for more details.

package_only

TRUE to return the formatted data used to build the sampler, otherwise FALSE to return the sampler

Value

outputs a function to run a sampler, the function arguments are:

niter

set the number of iterations

burn

set the number samples to discard

thin

set the thinning rate

Examples

# load example wide-format data
data("calibration")
data("whales")
data("whale_info")

# parse calibration study
calibration_data = parse_observations(
  x = calibration, 
  subject_col = 'CO.ID',
  meas_col = 'Lpix', 
  tlen_col = 'CO.L', 
  image_col = 'image', 
  barometer_col = 'Baro_Alt',
  laser_col = 'Laser_Alt', 
  flen_col = 'Focal_Length', 
  iwidth_col = 'Iw', 
  swidth_col = 'Sw',
  uas_col = 'uas'
)

# parse field study
whale_data = parse_observations(
  x = whales, 
  subject_col = 'whale_ID',
  meas_col = 'TL.pix', 
  image_col = 'Image', 
  barometer_col = 'AltitudeBarometer',
  laser_col = 'AltitudeLaser', 
  flen_col = 'FocalLength', 
  iwidth_col = 'ImageWidth', 
  swidth_col = 'SensorWidth', 
  uas_col = 'UAS',
  timepoint_col = 'year'
)

# build sampler
sampler_data = nondecreasing_length_sampler(
  data = combine_observations(calibration_data, whale_data),
  priors = list(
    image_altitude = c(min = 0.1, max = 130),
    altimeter_bias = rbind(
      data.frame(altimeter = 'Barometer', mean = 0, sd = 1e2),
      data.frame(altimeter = 'Laser', mean = 0, sd = 1e2)
    ),
    altimeter_variance = rbind(
      data.frame(altimeter = 'Barometer', shape = .01, rate = .01),
      data.frame(altimeter = 'Laser', shape = .01, rate = .01)
    ),
    altimeter_scaling = rbind(
      data.frame(altimeter = 'Barometer', mean = 1, sd = 1e1),
      data.frame(altimeter = 'Laser', mean = 1, sd = 1e1)
    ),
    pixel_variance = c(shape = .01, rate = .01),
    object_lengths = c(min = .01, max = 20)
  ),
  # set to false to return sampler function
  package_only = TRUE
)

[Package Xcertainty version 1.0.0 Index]