Estimate the projected Hill coefficient, efficacy, and potency

Estimate the projected shape parameter HS, efficacy E_inf and potency EC50 in the new dose-response curve of a drug after adding another drug to it by fitting a 2-parameter dose-response curve.

Usage

estimateProjParams(
  dose_to,
  combo_viability,
  dose_add,
  EC50_add,
  HS_add,
  E_inf_add = 0,
  residual = c("logcosh", "normal", "Cauchy"),
  show_Rsqr = TRUE,
  conc_as_log = FALSE,
  optim_only = FALSE,
  loss_args = list()
)

Arguments

dose_to

numeric a vector of concentrations of the drug being added to

combo_viability

numeric observed viability of two treatments; target for fitting curve.

dose_add

numeric a vector of concentrations of the drug added.

EC50_add

numeric relative EC50 of the drug added.

HS_add

numeric Hill coefficient of the drug added.

E_inf_add

numeric Efficacy of the drug added.

residual

character Method used to minimise residual in fitting curves. 3 methods available: logcosh, normal, Cauchy. The default method is logcosh. It minimises the logarithmic hyperbolic cosine loss of the residuals and provides the fastest estimation among the three methods, with fitting quality in between normal and Cauchy; recommanded when fitting large-scale datasets. The other two methods minimise residuals by considering the truncated probability distribution (as in their names) for the residual. Cauchy provides the best fitting quality but also takes the longest to run.

show_Rsqr

logical whether to show goodness-of-fit value in the result.

conc_as_log

logical indicates whether input concentrations are in log10 scale.

optim_only

logical(1) Should the fall back methods when optim fails

loss_args

list Additional argument to the loss function. These get passed to losss via do.call analagously to using ....

Value

list

* HS_proj: Projected Hill coefficient after adding a drug * E_inf_proj: Projected efficacy after adding a drug * EC50_proj: Projected potency after adding a drug * E_ninf_proj: Projected baseline viability by the added drug * Rsqr: if show_Rsqr is TRUE, it will include the R squared value indicating the quality of the fit in the result.

References

Motulsky, H., & Christopoulos, A. (2004). Fitting dose-response curves. In Fitting models to biological data using linear and nonlinear regression: A practical guide to curve fitting. Oxford University Press.