# nullModel

##### Fit a simple, non-informative model

Fit a single mean or largest class model

- Keywords
- models

##### Usage

`nullModel(x, ...)`# S3 method for default
nullModel(x = NULL, y, ...)

# S3 method for nullModel
predict(object, newdata = 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

- object
An object of class

`nullModel`

- newdata
A matrix or data frame of predictors (only used to determine the number of predictions to return)

- type
Either "raw" (for regression), "class" or "prob" (for classification)

##### Details

`nullModel`

emulates other model building functions, but returns the
simplest model possible given a training set: a single mean 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.

##### Value

The output of `nullModel`

is a list of class `nullModel`

with elements

the function call

the mean of
`y`

or the most prevalent class

when `y`

is a
factor, a vector of levels. `NULL`

otherwise

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).

the number of elements in `y`

predict.nullModel returns a either a factor or numeric vector depending on the class of y. All predictions are always the same.

##### Examples

```
# NOT RUN {
outcome <- factor(sample(letters[1:2],
size = 100,
prob = c(.1, .9),
replace = TRUE))
useless <- nullModel(y = outcome)
useless
predict(useless, matrix(NA, nrow = 10))
# }
```

*Documentation reproduced from package caret, version 6.0-86, License: GPL (>= 2)*