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Fit a simple, non-informative model

Source: R/nullmodel.R
nullmodel.Rd

Fit a single mean, set of quantiles, or largest class model. nullmodel() is the underlying computational function for the null_model() specification.

Usage

nullmodel(x, ...)

# Default S3 method
nullmodel(x = NULL, y, quantile_levels = NULL, ...)

# S3 method for class 'nullmodel'
print(x, ...)

# S3 method for class 'nullmodel'
predict(object, new_data = NULL, type = NULL, ...)

Arguments

x

An optional matrix or data frame of predictors. These values are not used in the model fit.

...

Optional arguments (not yet used).

y

A numeric vector (for regression) or factor (for classification) of outcomes.

quantile_levels

A numeric vector of probabilities used to compute outcome quantiles. When NULL, the mean is computed for numeric outcomes.

object

An object of class nullmodel.

new_data

A matrix or data frame of predictors, only used to determine the number of predictions to return.

type

Either "raw" (for regression), "quantile" (for quantile regression), "class", or "prob" (for classification).

Value

The output of nullmodel() is a list of class nullmodel with elements:

  • call: The function call.

  • value: The mean or quantiles of y, or the most prevalent class.

  • levels: When y is a factor, a vector of levels; NULL otherwise.

  • quantile_levels: The probabilities used to compute quantiles, or NULL.

  • pct: When y is a factor, a data frame with a column for each class (NULL otherwise). The column for the most prevalent class has the proportion of the training samples with that class; the other columns are zero.

  • n: The number of elements in y.

predict.nullmodel() returns a factor, numeric vector, or matrix depending on the class of y and whether quantiles were requested. All predictions are always the same.

Details

nullmodel() emulates other model building functions, but returns the simplest model possible given a training set: a single mean or set of quantiles for numeric outcomes and the most prevalent class for factor outcomes. When class probabilities are requested, the percentage of the training set samples with the most prevalent class is returned.

Examples


outcome <- factor(sample(letters[1:2],
                         size = 100,
                         prob = c(.1, .9),
                         replace = TRUE))
useless <- nullmodel(y = outcome)
useless
#> Null Classification Model
#> Predicted Value: b 
predict(useless, matrix(NA, nrow = 5))
#> [1] b b b b b
#> Levels: a b