mod_effect {mosaicModel}

Calculate effect sizes in a model

Description

Like a derivative or finite-difference

Usage

mod_effect(model, formula, step = NULL, bootstrap = 0, to = step,
  nlevels = 1, data = NULL, at = NULL, class_level = NULL, ...)

Arguments

model

the model from which the effect size is to be calculated

formula

a formula whose right-hand side is the variable with respect to which the effect size is to be calculated.

step

the numerical stepsize for the change var, or a comparison category for a categorical change var. This will be either a character string or a number, depending on the type of variable specified in the formula.

bootstrap

The number of bootstrap replications to construct. If greater than 1, calculate a standard error using that number of replications.

to

a synonym for step. (In English, "to" is more appropriate for a categorical input, "step" for a quantitative. But you can use either.)

nlevels

integer specifying the number of levels to use for "typical" inputs. (Default: up to 3)

data

Specifies exactly the cases at which you want to calculate the effect size.

at

similar to ... but expects a list or dataframe of the values you want to set. Like ..., all combinations of the values specified will be used as inputs.

class_level

Name of the categorical level for which the probability is to be used. Applies only to classifiers. (Default: Use the first level.) Unlike ... or at, no new combinations will be created.

...

additional arguments for evaluation levels of explanatory variables.

Details

When you want to force or restrict the effect size calculation to specific values for explanatory variables, list those variables and levels as a vector in ... For example, educ = c(10, 12, 16) will cause the effect size to be calculated at each of those three levels of education. Any variables whose levels are not specified in ... will have values selected automatically.

Value

a data frame giving the effect size and the values of the explanatory variables at which the effect size was calculated. There will also be a column to_ showing the value jumped to for the variable with respect to which the effect size is calculated. When bootstrap is greater than 1, there will be a standard error reported on the effect size; see the variable ending in _se.

Examples

mod1 <- lm(wage ~ age * sex * educ + sector, data = mosaicData::CPS85)
mod_effect(mod1, ~ sex)
mod_effect(mod1, ~ sector)
mod_effect(mod1, ~ age, sex = "M", educ = c(10, 12, 16), age = c(30, 40))
mod_effect(mod1, ~ age, sex = "F", age = 34, step = 1)
mod_effect(mod1, ~ sex, age = 35, sex = "M", to = "F" )
# For classifiers, the change in *probability* of a level is reported.
mod2 <- rpart::rpart(sector ~ age + sex + educ + wage, data = mosaicData::CPS85)
mod_effect(mod2, ~ educ)
mod_effect(mod2, ~ educ, class_level = "manag")