Tidy, Type-Safe 'prediction()' Methods

The prediction and margins packages are a combined effort to port the functionality of Stata's (closed source) margins command to (open source) R. prediction is focused on one function - prediction() - that provides type-safe methods for generating predictions from fitted regression models. prediction() is an S3 generic, which always return a "data.frame" class object rather than the mix of vectors, lists, etc. that are returned by the predict() methods for various model types. It provides a key piece of underlying infrastructure for the margins package. Users interested in generating marginal (partial) effects, like those generated by Stata's margins, dydx(*) command, should consider using margins() from the sibling project, margins.

In addition to prediction(), this package provides a number of utility functions for generating useful predictions:

Simple code examples

A major downside of the predict() methods for common modelling classes is that the result is not type-safe. Consider the following simple example:

library("stats")
library("datasets")
x <- lm(mpg ~ cyl * hp + wt, data = mtcars)
class(predict(x))
## [1] "numeric"
class(predict(x, se.fit = TRUE))
## [1] "list"

prediction solves this issue by providing a wrapper around predict(), called prediction(), that always returns a tidy data frame with a very simple print() method:

library("prediction")
(p <- prediction(x))
## Average prediction for 32 observations: 20.0906
class(p)
## [1] "prediction" "data.frame"
head(p)
##    mpg cyl disp  hp drat    wt  qsec vs am gear carb   fitted se.fitted
## 1 21.0   6  160 110 3.90 2.620 16.46  0  1    4    4 21.90488 0.6927034
## 2 21.0   6  160 110 3.90 2.875 17.02  0  1    4    4 21.10933 0.6266557
## 3 22.8   4  108  93 3.85 2.320 18.61  1  1    4    1 25.64753 0.6652076
## 4 21.4   6  258 110 3.08 3.215 19.44  1  0    3    1 20.04859 0.6041400
## 5 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2 17.25445 0.7436172
## 6 18.1   6  225 105 2.76 3.460 20.22  1  0    3    1 19.53360 0.6436862

The output always contains the original data (i.e., either data found using the find_data() function or passed to the data argument to prediction()). This makes it much simpler to pass predictions to, e.g., further summary or plotting functions.

Additionally the vast majority of methods allow the passing of an at argument, which can be used to obtain predicted values using modified version of data held to specific values:

prediction(x, at = list(hp = seq_range(mtcars$hp, 5)))
## Average predictions for 32 observations:
##  at(hp)  value
##    52.0 22.605
##   122.8 19.328
##   193.5 16.051
##   264.2 12.774
##   335.0  9.497

This more or less serves as a direct R port of (the subset of functionality of) Stata's margins command that calculates predictive marginal means, etc. For calculation of marginal or partial effects, see the margins package.

Supported model classes

The currently supported model classes are:

Requirements and Installation

CRAN Downloads Build Status Build status codecov.io Project Status: Active - The project has reached a stable, usable state and is being actively developed.

The development version of this package can be installed directly from GitHub using ghit:

if (!require("ghit")) {
    install.packages("ghit")
    library("ghit")
}
install_github("leeper/prediction")