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modelr <img src="man/figures/logo.png" align="right" />
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Overview
The modelr package provides functions that help you create elegant pipelines when modelling. It was designed primarily to support teaching the basics of modelling for the 1st edition of R for Data Science.
We no longer recommend it and instead suggest 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")
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
#> # ℹ 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
#> # ℹ 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
#> # ℹ 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
#> # ℹ 50 more rows