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Calculates fitted and residual values for modeled outputs

Source: R/calc_fit_mod.R
calc_fit_mod.Rd

calc_fit_mod() takes antibody kinetic parameter estimates and calculates fitted and residual values. Fitted values correspond to the estimated assay value (ex. ELISA units etc.) at time since infection (TSI). Residual values are calculated as the difference between fitted and observed values.

Usage

calc_fit_mod(
  modeled_dat,
  original_data,
  strat = NA,
  min_value = 0.01,
  decay_type = "power"
)

Arguments

modeled_dat

A data.frame of modeled antibody kinetic parameter values.

original_data

A data.frame of the original input dataset.

strat

A character string specifying the stratification variable name, or NA if no stratification is used.

min_value

numeric; minimum value substituted in before taking log10() of fitted/observed values, to avoid -Inf from log10(0) when computing log_residual*.

decay_type

A character string specifying the decay function ("power" or "exponential"). Passed through to ab(). Default is "power".

Value

A data.frame attached as an attributes with the following values:

  • Subject = ID number specifying an individual

  • Iso_type = The modeled antigen_isotype

  • Stratification = The variable used to stratify the model ("None" when no stratification is used)

  • t = Time since infection

  • fitted = The median (across posterior draws) fitted value for a given t

  • residual = The median (across posterior draws) residual, calculated as the difference between observed and fitted values for a given t

  • residual_low, residual_high = The 2.5% and 97.5% quantiles (across posterior draws) of the residual, giving a precision interval around residual

  • log_residual, log_residual_low, log_residual_high = As residual, residual_low, and residual_high, but computed on the log10 scale (i.e. log10(observed) - log10(fitted), with values floored at min_value beforehand)