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Estimate sample effects using a design matrix

Source: R/model_sampling.R
sample_effects_estimate_matrix.Rd

This option uses a log-linear regression model to estimate effects of sample covariates on the concentration. Concentrations can be influenced by sampling-related external factors, for example the time between sampling and shipping to the lab (age-of-sample effect), or different sampling or storage methods.

Usage

sample_effects_estimate_matrix(
  design_matrix,
  effect_prior_mu = 0,
  effect_prior_sigma = 1,
  modeldata = modeldata_init()
)

Arguments

design_matrix

A design matrix with different covariates potentially influencing sample concentration. The matrix must have one row for each modeled day. See stats::model.matrix() for construction of design matrices.

effect_prior_mu

Prior (mean) on the regression coefficients.

effect_prior_sigma

Prior (standard deviation) on the regression coefficients.

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

EpiSewer will fit a fixed-effects log-linear model, random effects are currently not supported.

The priors of this component have the following functional form:

  • regression coefficients: Normal

See also

Other sample effect models: sample_effects_estimate_weekday(), sample_effects_none()