Description Usage Arguments Value Examples

This function serves as an alternative for using the MCPModGen function
directly for general data. The function returns the estimates for *μ* and
*S*, which are needed for MCPMod.

1 2 3 4 5 6 7 8 9 10 11 12 13 |

`family` |
A character string containing the error distribution to be used in the model. |

`link` |
A character string for the model link function. |

`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` |
Either vectors of equal length specifying dose and response
values, or character vectors specifying the names of variables in the data
frame specified in |

`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 |

`addCovars` |
Formula specifying additive linear covariates (e.g. '~ factor(gender)'). |

`placAdj` |
Logical specifying whether the provided by 'resp' are to be treated as placebo-adjusted estimates. |

`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 |

A list containing the *μ* vector and *S* matrix.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
# 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
muS = prepareGen("binomial", "logit", w = "ntrt", dose = "dose",
resp = "pfrat", data = migraine)
## Look at the elements of muS
muS
MCPMod(muS$data$dose, muS$data$resp, models = models, S = muS$S,
type = "general", selModel = "aveAIC",Delta = 0.2)
``` |

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