---
title: "Tidy, Type-Safe 'prediction()' Methods"
output: github_document
---

<img src="man/figures/logo.png" align="right" />

The **prediction** and **margins** packages are a combined effort to port the functionality of Stata's (closed source) [`margins`](http://www.stata.com/help.cgi?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**](https://cran.r-project.org/package=margins).

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

 - `find_data()`, an S3 generic with methods that find the data frame used to estimate a regression model. This is a wrapper around `get_all_vars()` that attempts to locate data as well as modify it according to `subset` and `na.action` arguments used in the original modelling call.
 - `mean_or_mode()` and `median_or_mode()`, which provide a convenient way to compute the data needed for predicted values *at means* (or *at medians*), respecting the differences between factor and numeric variables.
 - `seq_range()`, which generates a vector of *n* values based upon the range of values in a variable
 - `build_datalist()`, which generates a list of data frames from an input data frame and a specified set of replacement `at` values (mimicking the `atlist` option of Stata's `margins` command)

## 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:


```r
library("stats")
library("datasets")
x <- lm(mpg ~ cyl * hp + wt, data = mtcars)
class(predict(x))
```

```
## [1] "numeric"
```

```r
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:


```r
library("prediction")
(p <- prediction(x))
```

```
## Data frame with 32 predictions from
##  lm(formula = mpg ~ cyl * hp + wt, data = mtcars)
## with average prediction: 20.0906
```

```r
class(p)
```

```
## [1] "prediction" "data.frame"
```

```r
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:


```r
prediction(x, at = list(hp = seq_range(mtcars$hp, 5)))
```

```
## Data frame with 160 predictions from
##  lm(formula = mpg ~ cyl * hp + wt, data = mtcars)
## with average predictions:
```

```
##     hp      x
##   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**](https://cran.r-project.org/package=margins) package.

## Supported model classes

The currently supported model classes are:

 - "lm" from `stats::lm()`
 - "glm" from `stats::glm()`, `MASS::glm.nb()`, `glmx::glmx()`, `glmx::hetglm()`, `brglm::brglm()`
 - "ar" from `stats::ar()`
 - "Arima" from `stats::arima()`
 - "arima0" from `stats::arima0()`
 - "biglm" from `biglm::biglm()` (including `"ffdf"` backed models)
 - "betareg" from `betareg::betareg()`
 - "bruto" from `mda::bruto()`
 - "clm" from `ordinal::clm()`
 - "coxph" from `survival::coxph()`
 - "crch" from `crch::crch()`
 - "earth" from `earth::earth()`
 - "fda" from `mda::fda()`
 - "Gam" from `gam::gam()`
 - "gausspr" from `kernlab::gausspr()`
 - "gee" from `gee::gee()`
 - "glimML" from `aod::betabin()`, `aod::negbin()`
 - "glimQL" from `aod::quasibin()`, `aod::quasipois()`
 - "glmnet" from `glmnet::glmnet()`
 - "gls" from `nlme::gls()`
 - "hurdle" from `pscl::hurdle()`
 - "hxlr" from `crch::hxlr()`
 - "ivreg" from `AER::ivreg()`
 - "knnreg" from `caret::knnreg()`
 - "kqr" from `kernlab::kqr()`
 - "ksvm" from `kernlab::ksvm()`
 - "lda" from `MASS:lda()`
 - "lme" from `nlme::lme()`
 - "loess" from `stats::loess()`
 - "lqs" from `MASS::lqs()`
 - "mars" from `mda::mars()`
 - "mca" from `MASS::mca()`
 - "mclogit" from `mclogit::mclogit()`
 - "mda" from `mda::mda()`
 - "merMod" from `lme4::lmer()` and `lme4::glmer()`
 - "mnlogit" from `mnlogit::mnlogit()`
 - "mnp" from `MNP::mnp()`
 - "naiveBayes" from `e1071::naiveBayes()`
 - "nlme" from `nlme::nlme()`
 - "nls" from `stats::nls()`
 - "nnet" from `nnet::nnet()`, `nnet::multinom()`
 - "plm" from `plm::plm()`
 - "polr" from `MASS::polr()`
 - "ppr" from `stats::ppr()`
 - "princomp" from `stats::princomp()`
 - "qda" from `MASS:qda()`
 - "rlm" from `MASS::rlm()`
 - "rpart" from `rpart::rpart()`
 - "rq" from `quantreg::rq()`
 - "selection" from `sampleSelection::selection()`
 - "speedglm" from `speedglm::speedglm()`
 - "speedlm" from `speedglm::speedlm()`
 - "survreg" from `survival::survreg()`
 - "svm" from `e1071::svm()`
 - "svyglm" from `survey::svyglm()`
 - "tobit" from `AER::tobit()`
 - "train" from `caret::train()`
 - "truncreg" from `truncreg::truncreg()`
 - "zeroinfl" from `pscl::zeroinfl()`

## Requirements and Installation

[![CRAN](https://www.r-pkg.org/badges/version/prediction)](https://cran.r-project.org/package=prediction)
![Downloads](https://cranlogs.r-pkg.org/badges/prediction)
[![Build Status](https://travis-ci.org/leeper/prediction.svg?branch=master)](https://travis-ci.org/leeper/prediction)
[![Build status](https://ci.appveyor.com/api/projects/status/a4tebeoa98cq07gy/branch/master?svg=true)](https://ci.appveyor.com/project/leeper/prediction/branch/master)
[![codecov.io](https://codecov.io/github/leeper/prediction/coverage.svg?branch=master)](https://codecov.io/github/leeper/prediction?branch=master)
[![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)

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

``` r
if (!require("remotes")) {
    install.packages("remotes")
}
remotes::install_github("leeper/prediction")
```
