Description Usage Arguments Value Author(s) References Examples

View source: R/tukeytrendformula.R

Wrapper function to fit a given model after different rescalings of a single dose variable. The fitted models are combined into a list that is suitable as input to the multiple marginal model function of package multcomp, mmm.

1 2 3 |

`formula` |
formula object suitable for the model function specified in |

`data` |
data.frame containing the variables of interest |

`model` |
character string, naming the function for model fitting, currently |

`dose` |
A single character string, naming a numeric variable in the models formula. This variable is rescaled acc. to the options in |

`scaling` |
A vector of character strings, naming the options for rescaling the variable specified in |

`ctype` |
optional character string naming a contrast type for multiple comparisons between dose levels, when |

`ddf` |
single character string, defining the option for the degree of freedom in inference after model fitting. By default, |

`d0shift` |
an optional factor, that is multiplied with the interpolated dose score for |

`...` |
arguments passed to the model fitting function named in |

A list with elements

`mmm` |
a list of fitted models, after rescaling the |

`mlf` |
a list of matrices defining a linear functions of model parameters for each model in |

`df` |
a vector of degrees of freedom, one for each model in |

and information of the model type and call of the initial model

Frank Schaarschmidt and Christian Ritz (providing internal functions to interface objects of class `"lmerMod"`

and `"lme"`

)

Tukey JW, Ciminera JL, Heyse JF (1985). Testing the statistical certainty of a response to increasing doses of a drug. Biometrics 41(1), 295-301.

Pipper CB, Ritz C, Bisgaard H (2012). A versatile methode for confirmatory evaluation of the effects of a covariate in multiple models. JRSSC - Applied Statistics 61, 315-326.

1 2 3 4 5 6 7 8 9 10 11 | ```
data(litter, package="multcomp")
# compare
dl <- litter
dl$dosen <- as.numeric(as.character(dl$dose))
ttlitter <- tukeytrendformula(weight ~ dosen + number, data=dl, model="lm", dose="dosen",
scaling=c("ari", "ord", "log", "treat"), ctype="Dunnett")
summary(asglht(ttlitter))
``` |

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