Description Usage Arguments Details Value Examples

This function tries to choose sensible values of the explanatory variables
from the data used to build a model or any other specified data.
(or from data specified with the `data =`

argument.)

1 |

`data` |
optional data frame from which to extract levels for explanatory variables |

`nlevels` |
how many levels to construct for input variables.
For quantitative variables, this is a suggestion. Set to |

`at` |
named list giving specific values at which to hold the variables. Use this to override the automatic generation of levels for any or all explanatory variables. |

`model` |
the model to display graphically |

`...` |
a more concise mechanism to passing desired values for variables |

For categorical variables, the most populated levels are used. For quantitative
variables, a sequence of `pretty()`

values is generated.

For categorical variables, will return the nlevels most popular levels, unless the levels are specified explicitly in an argument.

A dataframe containing all combinations of the selected values for
the explanatory variables. If there are p explanatory variables,
there will be about `nlevels^p`

cases.

1 2 3 4 5 6 7 | ```
## Not run:
df_typical(mosaicData::Galton, nlevels = 2, father = 70, mother = 68, nkids = 3)
df_typical(mosaicData::Galton, nlevels = 2)
mod1 <- lm(wage ~ age * sex + sector, data = mosaicData::CPS85)
df_typical(model = mod1, nlevels = 3)
## End(Not run)
``` |

mosaicModel documentation built on Sept. 22, 2017, 5:05 p.m.

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