modelr

Overview

The modelr package provides functions that help you create elegant pipelines when modelling. It is designed primarily to support teaching the basics of modelling within the tidyverse, particularly in R for Data Science.

Please see https://www.tidymodels.org/ for a more comprehensive framework for modelling within the tidyverse.

Installation

# The easiest way to get modelr is to install the whole tidyverse:
install.packages("tidyverse")

# Alternatively, install just modelr:
install.packages("modelr")

# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("tidyverse/modelr")

Status

modelr is stable: it has achieved its goal of making it easier to teach modelling within the tidyverse. For more general modelling tasks, check out the family of “tidymodel” packages like recipes, rsample, parsnip, and tidyposterior.

Getting started

library(modelr)

Partitioning and sampling

The resample class stores a “reference” to the original dataset and a vector of row indices. A resample can be turned into a dataframe by calling as.data.frame(). The indices can be extracted using as.integer():

# a subsample of the first ten rows in the data frame
rs <- resample(mtcars, 1:10)
as.data.frame(rs)
#>                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Duster 360        14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 240D         24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230          22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Merc 280          19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
as.integer(rs)
#>  [1]  1  2  3  4  5  6  7  8  9 10

The class can be utilized in generating an exclusive partitioning of a data frame:

# generate a 30% testing partition and a 70% training partition
ex <- resample_partition(mtcars, c(test = 0.3, train = 0.7))
lapply(ex, dim)
#> $test
#> [1]  9 11
#> 
#> $train
#> [1] 23 11

modelr offers several resampling methods that result in a list of resample objects (organized in a data frame):

# bootstrap
boot <- bootstrap(mtcars, 100)
# k-fold cross-validation
cv1 <- crossv_kfold(mtcars, 5)
# Monte Carlo cross-validation
cv2 <- crossv_mc(mtcars, 100)

dim(boot$strap[[1]])
#> [1] 32 11
dim(cv1$train[[1]])
#> [1] 25 11
dim(cv1$test[[1]])
#> [1]  7 11
dim(cv2$train[[1]])
#> [1] 25 11
dim(cv2$test[[1]])
#> [1]  7 11

Model quality metrics

modelr includes several often-used model quality metrics:

mod <- lm(mpg ~ wt, data = mtcars)
rmse(mod, mtcars)
#> [1] 2.949163
rsquare(mod, mtcars)
#> [1] 0.7528328
mae(mod, mtcars)
#> [1] 2.340642
qae(mod, mtcars)
#>        5%       25%       50%       75%       95% 
#> 0.1784985 1.0005640 2.0946199 3.2696108 6.1794815

Interacting with models

A set of functions let you seamlessly add predictions and residuals as additional columns to an existing data frame:

set.seed(1014)
df <- tibble::tibble(
  x = sort(runif(100)),
  y = 5 * x + 0.5 * x ^ 2 + 3 + rnorm(length(x))
)

mod <- lm(y ~ x, data = df)
df %>% add_predictions(mod)
#> # A tibble: 100 × 3
#>          x     y  pred
#>      <dbl> <dbl> <dbl>
#>  1 0.00740 3.90   3.08
#>  2 0.0201  2.86   3.15
#>  3 0.0280  2.93   3.19
#>  4 0.0281  3.16   3.19
#>  5 0.0312  3.19   3.21
#>  6 0.0342  3.72   3.23
#>  7 0.0514  0.984  3.32
#>  8 0.0586  5.98   3.36
#>  9 0.0637  2.96   3.39
#> 10 0.0652  3.54   3.40
#> # … with 90 more rows
df %>% add_residuals(mod)
#> # A tibble: 100 × 3
#>          x     y   resid
#>      <dbl> <dbl>   <dbl>
#>  1 0.00740 3.90   0.822 
#>  2 0.0201  2.86  -0.290 
#>  3 0.0280  2.93  -0.256 
#>  4 0.0281  3.16  -0.0312
#>  5 0.0312  3.19  -0.0223
#>  6 0.0342  3.72   0.496 
#>  7 0.0514  0.984 -2.34  
#>  8 0.0586  5.98   2.62  
#>  9 0.0637  2.96  -0.428 
#> 10 0.0652  3.54   0.146 
#> # … with 90 more rows

For visualization purposes it is often useful to use an evenly spaced grid of points from the data:

data_grid(mtcars, wt = seq_range(wt, 10), cyl, vs)
#> # A tibble: 60 × 3
#>       wt   cyl    vs
#>    <dbl> <dbl> <dbl>
#>  1  1.51     4     0
#>  2  1.51     4     1
#>  3  1.51     6     0
#>  4  1.51     6     1
#>  5  1.51     8     0
#>  6  1.51     8     1
#>  7  1.95     4     0
#>  8  1.95     4     1
#>  9  1.95     6     0
#> 10  1.95     6     1
#> # … with 50 more rows

# For continuous variables, seq_range is useful
mtcars_mod <- lm(mpg ~ wt + cyl + vs, data = mtcars)
data_grid(mtcars, wt = seq_range(wt, 10), cyl, vs) %>% add_predictions(mtcars_mod)
#> # A tibble: 60 × 4
#>       wt   cyl    vs  pred
#>    <dbl> <dbl> <dbl> <dbl>
#>  1  1.51     4     0  28.4
#>  2  1.51     4     1  28.9
#>  3  1.51     6     0  25.6
#>  4  1.51     6     1  26.2
#>  5  1.51     8     0  22.9
#>  6  1.51     8     1  23.4
#>  7  1.95     4     0  27.0
#>  8  1.95     4     1  27.5
#>  9  1.95     6     0  24.2
#> 10  1.95     6     1  24.8
#> # … with 50 more rows

Code of conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.