base_GIA: Estimate GIA according to the base model
In loewesadditivity: Loewe's Additivity

Description Usage Arguments Value Details Examples

Estimate GIA according to the base model

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

`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

estimated GIA for each combination of dose A and dose B

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 β_A and β_B are denoted by A_i^* and B_i^*, respectively. The parameters γ_A and γ_B are shape parameters. The parameters τ_1 and τ_2 are interaction parameters. Finally, λ_i is a weighted combination of dose A and dose B.

GIA_i = 100\%(1 - e^{-ψ_i})

ψ_i = \log(2)u_i^{v_i}

u_i = A^*_i + B_i^* + τ_1 A^*_i B^*_i

v_i = λ_i γ_A + (1-λ_i) γ_B + τ_1 τ_2λ_i (1 - λ_i) γ_A γ_B

λ_i = \frac{A_i^*}{A_i^* + B_i^*}

A_i^* = A_i / β_A

B_i^* = B_i / β_B

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)