Basic Regressions with mverse

This vignette describes the workflow of linear regression modeling in the multiverse with the following functions:

library(tidyverse)
library(mverse)

We will use the Boston housing dataset {Harrison Jr and Rubinfeld (1978)} as an example. This dataset has 506 observations on 14 variables. This dataset is extensively used in regression analyses and algorithm benchmarks. The objective is to predict the median value of a home (medv) with the feature variables.

glimpse(MASS::Boston) # using kable for displaying data in html
## Rows: 506
## Columns: 14
## $ crim    <dbl> 0.00632, 0.02731, 0.02729, 0.03237, 0.06905, 0.02985, 0.08829,…
## $ zn      <dbl> 18.0, 0.0, 0.0, 0.0, 0.0, 0.0, 12.5, 12.5, 12.5, 12.5, 12.5, 1…
## $ indus   <dbl> 2.31, 7.07, 7.07, 2.18, 2.18, 2.18, 7.87, 7.87, 7.87, 7.87, 7.…
## $ chas    <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ nox     <dbl> 0.538, 0.469, 0.469, 0.458, 0.458, 0.458, 0.524, 0.524, 0.524,…
## $ rm      <dbl> 6.575, 6.421, 7.185, 6.998, 7.147, 6.430, 6.012, 6.172, 5.631,…
## $ age     <dbl> 65.2, 78.9, 61.1, 45.8, 54.2, 58.7, 66.6, 96.1, 100.0, 85.9, 9…
## $ dis     <dbl> 4.0900, 4.9671, 4.9671, 6.0622, 6.0622, 6.0622, 5.5605, 5.9505…
## $ rad     <int> 1, 2, 2, 3, 3, 3, 5, 5, 5, 5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4,…
## $ tax     <dbl> 296, 242, 242, 222, 222, 222, 311, 311, 311, 311, 311, 311, 31…
## $ ptratio <dbl> 15.3, 17.8, 17.8, 18.7, 18.7, 18.7, 15.2, 15.2, 15.2, 15.2, 15…
## $ black   <dbl> 396.90, 396.90, 392.83, 394.63, 396.90, 394.12, 395.60, 396.90…
## $ lstat   <dbl> 4.98, 9.14, 4.03, 2.94, 5.33, 5.21, 12.43, 19.15, 29.93, 17.10…
## $ medv    <dbl> 24.0, 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15…

Simple Linear Regression with mverse

In order to perform a linear regression in the multiverse, we create a formula branch with all the models we wish to explore, add it the mverse object, and execute lm on each universe by calling lm_mverse.

Create a multiverse with mverse.

mv <- create_multiverse(MASS::Boston)

We can explore models of the median value of home prices medv on different combinations of the following explanatory variables: proportion of adults without some high school education and proportion of male workers classified as laborers (lstat), average number of rooms per dwelling (rm), per capita crime rate (crim), and property tax (tax).

Create the models with formula_branch()

formulas <- formula_branch(medv ~ log(lstat) * rm,
                           medv ~ log(lstat) * tax,
                           medv ~ log(lstat) * tax * rm)

Add the models to the multiverse mv.

mv <- mv %>% add_formula_branch(formulas)

Fit lm() across mv using lm_mverse().

lm_mverse(mv)

By default, summary will give the estimates of parameters for each model. You can also output other information by changing the output parameter.

summary(mv)
## # A tibble: 16 × 9
##    universe formulas_branch term  estimate std.error statistic  p.value conf.low
##    <fct>    <fct>           <chr>    <dbl>     <dbl>     <dbl>    <dbl>    <dbl>
##  1 1        medv ~ log(lst… (Int… -2.49e+1   6.66        -3.74 2.07e- 4 -3.80e+1
##  2 1        medv ~ log(lst… log(…  1.16e+1   2.61         4.45 1.05e- 5  6.50e+0
##  3 1        medv ~ log(lst… rm     1.10e+1   0.973       11.3  2.08e-26  9.05e+0
##  4 1        medv ~ log(lst… log(… -3.35e+0   0.405       -8.29 1.04e-15 -4.15e+0
##  5 2        medv ~ log(lst… (Int…  4.62e+1   2.83        16.3  1.89e-48  4.07e+1
##  6 2        medv ~ log(lst… log(… -9.60e+0   1.15        -8.31 9.04e-16 -1.19e+1
##  7 2        medv ~ log(lst… tax    1.35e-2   0.00750      1.80 7.23e- 2 -1.23e-3
##  8 2        medv ~ log(lst… log(… -6.35e-3   0.00278     -2.28 2.29e- 2 -1.18e-2
##  9 3        medv ~ log(lst… (Int… -1.88e+2  15.4        -12.2  3.36e-30 -2.18e+2
## 10 3        medv ~ log(lst… log(…  5.23e+1   6.70         7.80 3.73e-14  3.91e+1
## 11 3        medv ~ log(lst… tax    3.82e-1   0.0344      11.1  7.46e-26  3.15e-1
## 12 3        medv ~ log(lst… rm     3.10e+1   2.30        13.5  1.98e-35  2.65e+1
## 13 3        medv ~ log(lst… log(… -1.00e-1   0.0135      -7.40 5.89e-13 -1.27e-1
## 14 3        medv ~ log(lst… log(… -7.30e+0   1.06        -6.86 2.04e-11 -9.40e+0
## 15 3        medv ~ log(lst… tax:… -4.84e-2   0.00529     -9.16 1.32e-18 -5.88e-2
## 16 3        medv ~ log(lst… log(…  1.07e-2   0.00216      4.96 9.62e- 7  6.49e-3
## # … with 1 more variable: conf.high <dbl>

Changing output to df yields the degrees of freedom table.

summary(mv, output = "df")
## # A tibble: 3 × 5
##   universe formulas_branch                  p n.minus.p p.star
##   <fct>    <fct>                        <int>     <int>  <int>
## 1 1        medv ~ log(lstat) * rm           4       502      4
## 2 2        medv ~ log(lstat) * tax          4       502      4
## 3 3        medv ~ log(lstat) * tax * rm     8       498      8

Other options include F (output = "f") statistics

summary(mv, output = "f")
## # A tibble: 3 × 5
##   universe formulas_branch              fstatistic numdf.f dendf.f
##   <fct>    <fct>                             <dbl>   <dbl>   <dbl>
## 1 1        medv ~ log(lstat) * rm             482.       3     502
## 2 2        medv ~ log(lstat) * tax            341.       3     502
## 3 3        medv ~ log(lstat) * tax * rm       368.       7     498

and \(R^2\) (output = "r").

# output R-squared by `r.squared` or "r"
summary(mv, output = "r")
## # A tibble: 3 × 4
##   universe formulas_branch              r.squared adj.r.squared
##   <fct>    <fct>                            <dbl>         <dbl>
## 1 1        medv ~ log(lstat) * rm           0.742         0.741
## 2 2        medv ~ log(lstat) * tax          0.671         0.669
## 3 3        medv ~ log(lstat) * tax * rm     0.838         0.836

Finally, we can display how the effect of number of rooms in a dwelling log(lstat) using spec_curve.

spec_curve(mv, var = "log(lstat)")

Harrison Jr, David, and Daniel L Rubinfeld. 1978. “Hedonic Prices and the Demand for Clean Air.” Journal of Environmental Economics and Management 5.