Estimate a limit of detection model for digital PCR data

Pathogen concentrations below a certain threshold may not be detectable and thus erroneously measured as 0. This option adjusts for a limit of detection based on the statistical properties of digital PCR (dPCR) and includes zero measurements in the likelihood.

In effect, zero measurements provide a signal that the concentration in the respective sample was likely below the limit of detection, but we don't know what the exact concentration was.

Usage

LOD_estimate_dPCR(drop_prob = 1e-10, modeldata = modeldata_init())

Arguments

drop_prob

Probability for non-detection below which likelihood contributions of observed concentrations are dropped from LOD model. This avoids numerical issues of the LOD model at high concentrations (very small non-detection probabilities) that can otherwise affect sampling speed. Since these likelihood contributions will be virtually zero for almost all samples anyway, parameter estimates are practically not affected.

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

The limit of detection is modeled using a hurdle model. The model uses the number of partitions in the dPCR reaction and the conversion factor as defined and estimated by noise_estimate_dPCR(). It can therefore only be used together with noise = noise_estimate_dPCR() in model_measurements().

See also

Other LOD models: LOD_assume(), LOD_none()