statisticalModeling (version 0.3.0)

effect_size: Calculate effect sizes in a model

Description

Like a derivative or finite-difference

Usage

effect_size(model, formula, step = NULL, bootstrap = FALSE, to = step, data = NULL, at = 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

If TRUE, calculate a standard error using bootstrapping. Alternatively, you can specify the number of bootstrap replications (default:100).

to

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

data

Specifies exactly the cases at which you want to calculate the effect size. Unlike ... or at, no new combinations will be created.

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.

...

additional arguments for evaluation levels of explanatory variables or to be passed to predict(). For instance, for a glm, perhaps you want type = "response".

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.

Examples

mod1 <- lm(wage ~ age * sex * educ + sector, data = mosaicData::CPS85)
effect_size(mod1, ~ sex)
effect_size(mod1, ~ sector)
effect_size(mod1, ~ age, sex = "M", educ = c(10, 12, 16), age = c(30, 40))
effect_size(mod1, ~ age, sex = "F", age = 34, step = 1)
effect_size(mod1, ~ sex, age = 35, sex = "M", to = "F" )