Description Usage Arguments Details Value Author(s) Examples
This is the main function to fit an interaction index model to drug combination data based on the Loewe additivity model. The interaction index can be specified in a flexible way as a function of doses and other variables.
1 2 3 4 5 6 7 8 9 10 11 12 13 |
data |
A (long) data frame to fit the model to. Required columns are
"d1", "d2" and "effect". Other variables are allowed and can be used in
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mono |
An optional "MarginalFit" object, obtained from
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model |
A pre-defined model to use for the interaction index tau. One of "additive", "uniform", "linear1", "separate1", "linear2", "separate2", "separate12" or "zhao". See details. |
tauFormula |
A formula to define the interaction index tau, using either
'literal' (as in |
tauLog |
Whether to fit the model using log-transformed tau parameters.
This is mostly useful for "separate"-type tau models for better convergence.
Note that if TRUE, tau cannot be negative, which may be not approriate
for some models, such as "linear1" and "linear2".
Note that this affects the coefficient names in the result
("logtau1", "logtau2", ... instead of "tau1", "tau2", ...), so if
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tauStart |
Vector of starting values for tau parameters, either of length 1 or of the same length as the total number of tau parameters. |
stage |
Whether to run a 1-stage or 2-stage estimation. |
fixed |
Constraints on monotherapy and/or tau parameters as a vector
of the form 'name = value', if NULL (default), taken from |
inactiveIn |
which compound is inactive (1 or 2), or 0 (default) when both compounds are active. |
verbose |
Whether to show extra information useful for debugging. |
... |
Further arguments passed to the |
There are different ways to specify a model for the interaction index tau:
Using one of the pre-defined models as specified in the model
argument:
"additive", for additivity model,
"uniform", one overall value for tau,
"linear1", linear dependency on log10 dose of the first compound,
"linear2", linear dependency on log10 dose of the second compound,
"separate1", different tau for each dose of the first compound,
"separate2", different tau for each dose of the second compound,
"separate12", different tau for each combination of doses of the two compounds,
"zhao", quadratic response surface model following Zhao et al 2012.
Using a literal or symbolic formula. Note that for the monotherapies, tau is assumed to be equal to 1. Therefore, continuous models may entail discontinuities in the interaction index when d1 and d2 approach 0.
Fitted object of class "HarbronFit" which is an nls
-like
object with extra elements.
Maxim Nazarov
1 2 3 4 5 6 7 | data("checkerboardData", package = "drugCombo")
data1 <- checkerboardData[checkerboardData$exp == 1, ]
mono1 <- fitMarginals(data1, fixed = c(b = 1))
# all three ways below are equivalent
fitLin1 <- fitModel(data = data1, mono = mono1, model = "linear1")
fitLin1b <- fitModel(data1, mono1, tauFormula = ~ log10(d1))
fitLin1c <- fitModel(data1, mono1, tauFormula = ~ tau1+tau2*log10(d1))
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