evalBraidModel | R Documentation |
Evaluate the BRAID response surface model
evalBraidModel(DA, DB, bpar, calcderivs = FALSE)
DA |
A vector of concentrations of drug A in a combination (values 0
and |
DB |
A vector of concentrations of drug B in a combination (values 0
and |
bpar |
A BRAID response surface parameter vector (see Details) |
calcderivs |
Primarily used by fitting functions for non-linear
optimization. If |
The BRAID response model is, in total, described by nine response surface parameters. A BRAID parameter vector should uniquely determine all these values. They are
IDMA: The dose of median effect of drug A, also called the EC50
IDMB: The dose of median effect of drug B
na: The Hill slope, or sigmoidicity, of drug A
nb: The Hill slope of drug B
kappa: The BRAID interaction parameter, indicating additivity (kappa = 0), antagonism (2 < kappa < 0), or synergy (kappa > 0)
E0: The minimal effect, the effect observed when neither drug is present
EfA: The maximal effect of drug A, the effect theoretically observed when drug B is absent and drug A is present at infinite concentration
EfB: The maximal effect of drug B,
Ef: The maximal effect of the combination, theoretically observed when both drugs are present at infinite concentration. It may be (but often is not) further from E0 than either EfA or EfB.
In many cases, however, it is easier to specify only some of the final three parameters. braidrm functions therefore support BRAID parameter vectors of length 7 (in which the sixth and seventh values are assumed to be E0 and Ef, and EfA and EfB are assumed to be equal to Ef), length 8 (in which the seventh and eighth values are EfA and EfB, and Ef is assumed to be equal to whichever of these two values is further from E0), or the full length 9 parameter vector.
If calcderivs
is FALSE
, a numeric vector the same length as DA
and/or DB
with the predicted BRAID response surface values. If
calcderivs
is TRUE
, a list with two elements: value
, containing the
response surface values, and derivatives
, a matrix with as many rows as
value
has elements, and nine columns containing the partial derivatives of
the response surface with respect to the nine BRAID response surface
parameters
concentrations <- c(0, 2^(-3:3))
surface <- data.frame(
concA = rep(concentrations,each=length(concentrations)),
concB = rep(concentrations,times=length(concentrations))
)
surface$additive <- evalBraidModel(
surface$concA,
surface$concB,
c(1, 1, 3, 3, 0, 0, 100, 100, 100)
)
surface$synergy <- evalBraidModel(
surface$concA,
surface$concB,
c(1, 1, 3, 3, 2, 0, 80, 90)
)
surface$antagonism <- evalBraidModel(
surface$concA,
surface$concB,
c(1, 1, 3, 3, -1, 0, 100)
)
head(surface)
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