Performs a convenient pre-built set of BRAID and dose-response analysis tasks
runBraidAnalysis(
formula,
data,
defaults,
weights = NULL,
start = NULL,
direction = 0,
lower = NULL,
upper = NULL,
useBIC = TRUE,
...
)# S3 method for formula
runBraidAnalysis(
formula,
data,
defaults,
weights = NULL,
start = NULL,
direction = 0,
lower = NULL,
upper = NULL,
useBIC = TRUE,
...
)
# S3 method for default
runBraidAnalysis(
formula,
data,
defaults,
weights = NULL,
start = NULL,
direction = 0,
lower = NULL,
upper = NULL,
useBIC = TRUE,
...
)
An object of class braidAnalysis, containing the following values:
concs: a width-two array containing the two tested doses for each
measurement
act: a numeric vector with as many values as concs has rows,
containing the measured values for each measurement
weights: a numeric vector of weights, the same length as act,
specifying the weight given to each measurement in fitting. All weights are
1 by default
braidFit: a fit object of class braidrm containing the best-fit BRAID
surface according to the given constraints
hillFit1: If the given data contains measurements of the first drug in
isolation, those measurements are fit using basicdrm::findBestHillModel;
the results of this analysis are stored as an object of class hillrm as
hillFit1. If no such measurements are found, this will be NULL
hillFit2: the corresponding fit for measurements of the second drug
alone, if they are included; NULL otherwise
Either an object of class formula such as would be provided
to a modeling function like stats::lm(), or a width-2 numeric array vector
of concentration pairs (including 0 or Inf). A formula should specify a
single output as a function of two inputs, eg. activity ~ conc1 + conc2.
If forumula is a symbolic formula, a data frame containing the
specified values. If formula is a numeric array of concentrations, a
numeric vector of response values, the same length as the number of rows of
formula.
Default minimal and maximal effect values used to fix effect parameters during model selection.
A vector of weights (between 0 and 1) the same length as
the data which determines the weight with which each measurement
will impact the the sum of squared errors. Weights will be multiplied by
errors before squaring. If NULL (the default) all weights will be set
to 1. Can be a numeric vector, or the name of a column in data if formula
is a symbolic formula
A BRAID parameter vector specifying the first guess where the
non-linear optimization should begin. May be a length 7, 8, or 9 vector,
though a full length vector is always preferable. If NULL (the default),
it will be estimated from the data.
Determines the possible directionality of the BRAID model. If 0 (the default) no additional constraints are placed on the parameters. If greater than 0, the fitting will require that the maximal effects are all greater than or equal to the minimal effect. If less than 0, the fitting will require that all maximal effect is less than or equal to the minimal effect.
A numeric vector of lower bounds on the fitted parameter values.
May be the same length as the number of fitted parameters, or a full,
length-9 vector. Missing or unspecified lower bounds may be included as NA
or Inf; if unspecified, lower bounds on the first five parameters (IDMA,
IDMB, na, nb, and kappa) will be automatically estimated from the data.
Bounds on the minimal and maximal effect parameters however (E0, EfA, EfB,
and Ef) will be assumed to be infinite unless specified. A value of NULL,
the default, will be treated as all lower parameter bounds being
unspecified.
A numeric vector of upper bounds on the fitted parameter values.
Used in the same way as lower.
If TRUE (the default), the best (read: most parsimonious)
model will be selected from all tested models using the Bayesian information
criterion (Schwarz 1978). If FALSE the function will follow the convention
of earlier versions of the braidrm package and use the Akaike information
criterion (Akaike 1974).
Additional parameters to be passed to braidrm::findBestBraid()
surface <- synergisticExample
analysis <- runBraidAnalysis(measure~concA+concB, surface, defaults=c(0,1))
names(analysis)
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