loewesadditivity (version 0.1.0)

base_GIA: Estimate GIA according to the base model

Description

Estimate GIA according to the base model

Usage

base_GIA(model_params, dose_A, dose_B, fn_list = NULL)

Arguments

model_params

named vector of parameters to be used in function. Specifically, the named parameters must be "beta_A", "beta_B", "gamma_A", "gamma_B", "tau_1", and "tau_2". See details for more info.

dose_A

numeric vector of doses (e.g. mg/mL) of dose_A

dose_B

numeric vector of doses (e.g. mg/mL) of dose_B

fn_list

NULL

Value

estimated GIA for each combination of dose A and dose B

Details

The equation is given in full as follows. The GIA (%) is given a as a function of the model parameters and the doses \(A_i\) and \(B_i\), respectively. The doses scaled by the respective ED50s \(\beta_A\) and \(\beta_B\) are denoted by \(A_i^*\) and \(B_i^*\), respectively. The parameters \(\gamma_A\) and \(\gamma_B\) are shape parameters. The parameters \(\tau_1\) and \(\tau_2\) are interaction parameters. Finally, \(\lambda_i\) is a weighted combination of dose A and dose B. $$GIA_i = 100\%(1 - e^{-\psi_i})$$ $$\psi_i = \log(2)u_i^{v_i}$$ $$u_i = A^*_i + B_i^* + \tau_1 A^*_i B^*_i$$ $$v_i = \lambda_i \gamma_A + (1-\lambda_i) \gamma_B + \tau_1 \tau_2\lambda_i (1 - \lambda_i) \gamma_A \gamma_B$$ $$\lambda_i = \frac{A_i^*}{A_i^* + B_i^*}$$ $$A_i^* = A_i / \beta_A$$ $$B_i^* = B_i / \beta_B$$

Examples

# NOT RUN {
model_params <- c("beta_A" = 1, "beta_B" = 2, "gamma_A" = .5,
"gamma_B" = .6,  "tau_1" = 1, "tau_2" = 0)
dose_A <- c(0, 1, 0)
dose_B <- c(0, 0, 1)
base_GIA(model_params, dose_A, dose_B)
# }