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Estimate measurement noise

Source: R/model_measurements.R
noise_estimate.Rd

This option estimates the unexplained variation in wastewater measurements using a constant coefficient of variation model.

If multiple measurements (replicates) per sample are provided, EpiSewer can also explicitly model variation before the replication stage.

For a non-constant coefficient of variation model, see noise_estimate_dPCR().

Usage

noise_estimate(
  replicates = FALSE,
  cv_prior_mu = 0,
  cv_prior_sigma = 1,
  pre_replicate_cv_prior_mu = 0,
  pre_replicate_cv_prior_sigma = 1,
  modeldata = modeldata_init()
)

Arguments

replicates

Should replicates be used to explicitly model variation before the replication stage?

cv_prior_mu

Prior (mean) on the coefficient of variation of concentration measurements. Note that when replicates=TRUE, this is only the CV after the replication stage (see details for more explanation).

cv_prior_sigma

Prior (standard deviation) on the coefficient of variation of concentration measurements.

pre_replicate_cv_prior_mu

Prior (mean) on the coefficient of variation of concentrations before the replication stage.

pre_replicate_cv_prior_sigma

Prior (standard deviation) on the coefficient of variation of concentrations before the replication stage.

modeldata

A modeldata object to which the above model specifications should be added. Default is an empty model given by modeldata_init(). Can also be an already partly specified model returned by other EpiSewer modeling functions.

Value

A modeldata object containing data and specifications of the model to be fitted. Can be passed on to other EpiSewer modeling functions to add further data and model specifications.

The modeldata object also includes information about parameter initialization (.init), meta data (.metainfo), and checks to be performed before model fitting (.checks).

Details

When replicates=TRUE, two coefficients of variation are estimated:

  • the CV before the replication stage (see pre_replicate_cv_prior_mu)

  • the CV after the replication stage (see cv_prior_mu)

The meaning of these CV estimates depends on the type of replicates. If the replicates are biological replicates (i.e. independently processed), then cv estimates the noise in the preprocessing and PCR, and pre_replicate_cv estimates the noise from anything before preprocessing (e.g. sampling noise and all other unexplained variation). In contrast, if the replicates are technical replicates (i.e. several PCR runs of the same preprocessed sample), then cv estimates only the PCR noise, and pre_replicate_cv estimates all other noise (including preprocessing noise.)

The priors of this component have the following functional form:

  • coefficient of variation of concentration measurements (cv): Truncated normal

  • coefficient of variation of concentration before the replication stage (pre_replicate_cv): Truncated normal

See also

Other noise models: noise_estimate_constant_var(), noise_estimate_dPCR()