model_eval: Evaluate a model on inputs

In LSTbook: Data and Software for "Lessons in Statistical Thinking"

Evaluate a model on inputs

Description

Evaluate a model on inputs

Usage

model_eval(
  mod,
  data = NULL,
  ...,
  skeleton = FALSE,
  ncont = 3,
  interval = c("prediction", "confidence", "none"),
  level = 0.95,
  type = c("response", "link")
)

Arguments

mod	A model as from model_train(), lm() or glm()
data	A data frame of inputs. If missing, the inputs will be assembled from ... or from the training data, or an skeleton will be constructed.
skeleton	Logical flag. If TRUE, a skeleton on inputs will be created. See model_skeleton().
ncont	Only relevant to skeleton. The number of levels at which to evaluate continuous variables. See model_skeleton().
interval	One of "prediction" (default), "confidence", or "none".
level	The level at which to construct the interval (default: 0.95)
type	Either "response" (default) or "link". Relevant only to glm models. The format of the .output
...	Optional vectors specifying the inputs. See examples.

Value

A data frame. There is one row for each row of the input values (see data parameter). The columns include

• the explanatory variables

• .output — the output of the model that corresponds to the explanatory value

• the .lwr and .upr bounds of the prediction or confidence interval

• if training data is used as the input, then it's possible to calculate the residual. This will be called .resid.

Examples

mod <- mtcars |> model_train(mpg ~ hp + wt)
model_eval(mod, hp=100, wt=c(2,3))
model_eval(mod) # training data
model_eval(mod, skeleton=TRUE)

LSTbook documentation built on April 3, 2025, 6:02 p.m.