R: Extract data from a model object

find_data {prediction} | R Documentation

## Extract data from a model object

### Description

Attempt to reconstruct the data used to create a model object

### Usage

    find_data(model, ...)

    ## Default S3 method:
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'data.frame'
    find_data(model, ...)

    ## S3 method for class 'crch'
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'glimML'
    find_data(model, ...)

    ## S3 method for class 'glm'
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'hxlr'
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'lm'
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'mca'
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'merMod'
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'svyglm'
    find_data(model, ...)

    ## S3 method for class 'train'
    find_data(model, ...)

    ## S3 method for class 'vgam'
    find_data(model, env = parent.frame(), ...)

    ## S3 method for class 'vglm'
    find_data(model, env = parent.frame(), ...)

### Arguments

`model` | The model object.
`...` | Additional arguments passed to methods.
`env` | An environment in which to look for the `data` argument to the modelling call.

### Details

This is a convenience function and, as such, carries no guarantees. To behave well, it typically requires that a model object be specified using a formula interface and an explicit `data` argument. Models that can be specified using variables from the `.GlobalEnv` or with a non-formula interface (e.g., a matrix of data) will tend to generate errors. `find_data` is an S3 generic so it is possible to expand it with new methods.

### Value

A data frame containing the original data used in a modelling call, modified according to the original model's 'subset' and 'na.action' arguments, if appropriate.

### See Also

`prediction`, `build_datalist`, `mean_or_mode`, `seq_range`

### Examples

    require("datasets")
    x <- lm(mpg ~ cyl * hp + wt, data = head(mtcars))
    find_data(x)

[Package prediction version 0.3.2 Index]