df_typical {mosaicModel} | R Documentation |
Find typical levels of explanatory variables in a model/dataset.
Description
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.)
Usage
df_typical(data = NULL, nlevels = 3, at = list(), model = NULL, ...)
Arguments
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 |
Details
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.
Value
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.
Examples
## 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)