typical_levels: Find typical levels of explanatory variables in a...

In statisticalModeling: Functions for Teaching Statistical Modeling

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

typical_levels(model = NULL, data = NULL, nlevels = 3, at = list(), ...)

Arguments

model	the model to display graphically
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. pretty() will determine
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.
...	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.

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: mod1 <- lm(wage ~ age * sex + sector, data = mosaicData::CPS85)
typical_levels(mod1)
mod3 <- glm(married == "Married" ~ age + sex * sector,
            data = mosaicData::CPS85, family = "binomial")
typical_levels(mod3, nlevels = 2)

## End(Not run)

statisticalModeling documentation built on May 29, 2017, 11:56 p.m.