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 variablebuild_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:
## Data frame with 32 predictions from
## lm(formula = mpg ~ cyl * hp + wt, data = mtcars)
## with average prediction: 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:
## 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 package.
The currently supported model classes are:
stats::lm()
stats::glm()
, MASS::glm.nb()
, glmx::glmx()
, glmx::hetglm()
, brglm::brglm()
stats::ar()
stats::arima()
stats::arima0()
biglm::biglm()
(including "ffdf"
backed models)betareg::betareg()
mda::bruto()
ordinal::clm()
survival::coxph()
crch::crch()
earth::earth()
mda::fda()
gam::gam()
kernlab::gausspr()
gee::gee()
aod::betabin()
, aod::negbin()
aod::quasibin()
, aod::quasipois()
glmnet::glmnet()
nlme::gls()
pscl::hurdle()
crch::hxlr()
AER::ivreg()
caret::knnreg()
kernlab::kqr()
kernlab::ksvm()
MASS:lda()
nlme::lme()
stats::loess()
MASS::lqs()
mda::mars()
MASS::mca()
mclogit::mclogit()
mda::mda()
lme4::lmer()
and lme4::glmer()
mnlogit::mnlogit()
MNP::mnp()
e1071::naiveBayes()
nlme::nlme()
stats::nls()
nnet::nnet()
, nnet::multinom()
plm::plm()
MASS::polr()
stats::ppr()
stats::princomp()
MASS:qda()
MASS::rlm()
rpart::rpart()
quantreg::rq()
sampleSelection::selection()
speedglm::speedglm()
speedglm::speedlm()
survival::survreg()
e1071::svm()
survey::svyglm()
AER::tobit()
caret::train()
truncreg::truncreg()
pscl::zeroinfl()
The development version of this package can be installed directly from GitHub using remotes
: