Description Usage Arguments Details Value References Examples
This function allows the user to implement the MCPMod function on negative
binomial, Poisson, and binary data, without having to write any additional
code. If analyzing survival data, see the
MCPModSurv
function.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  MCPModGen(
family = c("negative binomial", "binomial"),
link = c("log", "logit", "probit", "cauchit", "cloglog", "identity", "log risk ratio",
"risk ratio"),
returnS = FALSE,
w = NULL,
dose,
resp,
data = NULL,
models,
addCovars = ~1,
placAdj = FALSE,
selModel = c("AIC", "maxT", "aveAIC"),
alpha = 0.025,
df = NULL,
critV = NULL,
doseType = c("TD", "ED"),
Delta,
p,
pVal = TRUE,
alternative = c("one.sided", "two.sided"),
na.action = na.fail,
mvtcontrol = mvtnorm.control(),
bnds,
control = NULL,
offset = NULL,
...
)

family 
A character string containing the error distribution to be used in the model. 
link 
A character string for the model link function. 
returnS 
Logical determining whether muHat and SHat should be returned, in additional to the MCPMod output. 
w 
Either a numeric vector of the same length as dose and resp, or a character vector denoting the column name in the data. 
dose, resp 
Either vectors of equal length specifying dose and response
values, or character vectors specifying the names of variables in the data
frame specified in 
data 
Data frame with names specified in 'dose', 'resp', and optionally 'w'. If data is not specified, it is assumed that 'dose' and 'resp' are numerical vectors 
models 
An object of class "Mods", see 
addCovars 
Formula specifying additive linear covariates (e.g. '~ factor(gender)'). 
placAdj 
Logical specifying whether the provided by 'resp' are to be treated as placeboadjusted estimates. 
selModel 
Optional character vector specifying the model selection criterion for dose estimation. Possible values are
For type = "general" the "gAIC" is used. 
alpha 
Significance level for the multiple contrast test 
df 
An optional numeric value specifying the degrees of freedom. Infinite degrees of freedom ('df=Inf', the default), correspond to the multivariate normal distribution. 
critV 
Supply a precalculated critical value. If this argument is NULL, no critical value will be calculated and the test decision is based on the pvalues. If critV = TRUE the critical value will be calculated. 
doseType 
doseType determines the dose to estimate, ED or TD (see also

Delta 
doseType determines the dose to estimate, ED or TD (see also

p 
doseType determines the dose to estimate, ED or TD (see also

pVal 
Logical determining, whether pvalues should be calculated. 
alternative 
Character determining the alternative for the multiple contrast trend test. 
na.action 
A function which indicates what should happen when the data contain NAs. 
mvtcontrol 
A list specifying additional control parameters for the qmvt
and pmvt calls in the code, see also 
bnds 
Bounds for nonlinear parameters. This needs to be a list with list
entries corresponding to the selected bounds. The names of the list
entries need to correspond to the model names. The

control 
Control list for the optimization. The entry nlminbcontrol needs to be a list and is passed directly to control argument in the nlminb function, that is used internally for models with 2 nonlinear parameters (e.g. sigmoid Emax or beta model). The entry optimizetol is passed directly to the tol argument of the optimize function, which is used for models with 1 nonlinear parameters (e.g. Emax or exponential model). The entry gridSize needs to be a list with entries dim1 and dim2 giving the size of the grid for the gridsearch in 1d or 2d models. 
offset 
Either a numeric vector of the same length as dose and resp, or a character vector denoting the column name in the data. 
... 
Additional arguments to be passed to 
This function works by first fitting a glm with the chosen family and link.
The μ vector and S matrix are extracted from the glm, and these
values are supplied to the MCPMod function, along with all userdefined
arguments.
Currently, the function can take the negative binomial and
Poisson family with a log, sqrt, identity, risk ratio, and log risk ratio
links, or a bernoulli family with a log, logit, probit, cauchit,
clogloglink, identity, risk ratio, and log risk ratio links.
An object of class MCPMod if 'returnS = FALSE'. Otherwise, a list containing an object of class MCPMod, the numeric vector μ, and the numeric matrix S.
Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997). Model selection an integral part of inference, Biometrics, 53, 603–618
1 2 3 4 5 6 7 8 9 10  # Analyze the binary migraine data from the DoseFinding package.
data(migraine)
models = Mods(linear = NULL, emax = 1, quadratic = c(0.004), doses = migraine$dose)
# Now analyze using binomial weights
PFrate < migraine$painfree/migraine$ntrt
migraine$pfrat = migraine$painfree / migraine$ntrt
MCPModGen("binomial","logit",returnS = TRUE, w = "ntrt", dose = "dose",
resp = "pfrat", data = migraine, models = models, selModel = "aveAIC",
Delta = 0.2)

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