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:

`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)

A major downside of the `predict()`

methods for common modelling classes is that the result is not type-safe. Consider the following simple example:

`## [1] "numeric"`

`## [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:

`## Average prediction for 32 observations: 20.0906`

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

```
## 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:

`## 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.

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) - “bigLm” from
`bigLm::bigLm()`

- “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()`

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

: