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.
# The easiest way to get modelr is to install the whole tidyverse:
install.packages("tidyverse")
# Alternatively, install just modelr:
install.packages("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
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
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
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4