mverse

mverse is an extension to multiverse package (Sarma et al. 2021) which allows users create explorable multiverse analysis (Steegen et al. 2016) in R. This extension provides user friendly abstraction and a set of examples for researchers, educators, and students in statistics.

Installation

You can install the released version of mverse from CRAN with:

install.packages("mverse")

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("mverseanalysis/mverse", build_vignettes = TRUE)

Usage

The following demonstration performs a multiverse analysis using hurricane dataset (Jung et al. 2014) included in the library. We first create 6 universes as described in Figure 1. A filter branch with 2 options and a mutate branch with 3 options results in 6 universes in total. We then fit a Poisson regression model across the multiverse and inspect a coefficient estimate. See vignette("hurricane") for a detailed analysis as well as the terminologies used.

#> Warning: Using alpha for a discrete variable is not advised.

Figure 1. Having one branch with 2 options and another with 3 results in 2 x 3 = 6 universes in total.

Initiate

First, we start by loading the library and defining a mverse object with the dataset of interest.

library(mverse)
mv <- mverse(hurricane)

Define Branches

We use the *_branch() methods to define branches. filter_branch() defines filtering operations using dplyr::filter() with different options for the filter.

outliers <- filter_branch(
  ! Name %in% c("Katrina"),
  ! Name %in% c("Katrina", "Audrey")
)

mutate_branch() multiplexes dplyr::mutate() to add a new column in the dataset.

strength <- mutate_branch(
  NDAM, HighestWindSpeed, Minpressure_Updated_2014)

In order to fit a Poisson regression, we need to specify the model using R’s formula syntax and the underlying distribution using family. In mverse, we provide the specifications using formula_branch() and family_branch(). In this demonstration, we only define a single option for both formula and family but it is possible to provide multiple options for them as well.

model <- formula_branch(alldeaths ~ strength * MasFem)
distribution <- family_branch(poisson)

Add Branches

After defining the branches, we can add the branch objects to the mverse object using add_*_branch() methods.

mv <- mv %>%
  add_filter_branch(outliers) %>%
  add_mutate_branch(strength) %>%
  add_formula_branch(model) %>%
  add_family_branch(distribution)

Fit Model

glm_mverse() multiplexes stats::glm() function and fits a GLM in each universe according to the specifications provided by add_fomula_branch() and add_family_branch().

mv <- mv %>% glm_mverse()

Extract Results

After completing the analysis, we can extract the results using summary(). The method returns a table with branching options, estimates, 95% confidence intervals for all regression terms across the multiverse.

res <- summary(mv)
res
#> # A tibble: 24 × 16
#>    universe outliers_branch strength_branch model_branch distribution_branch
#>    <fct>    <fct>           <fct>           <fct>        <fct>              
#>  1 1        outliers_1      strength_1      model_1      distribution_1     
#>  2 1        outliers_1      strength_1      model_1      distribution_1     
#>  3 1        outliers_1      strength_1      model_1      distribution_1     
#>  4 1        outliers_1      strength_1      model_1      distribution_1     
#>  5 2        outliers_1      strength_2      model_1      distribution_1     
#>  6 2        outliers_1      strength_2      model_1      distribution_1     
#>  7 2        outliers_1      strength_2      model_1      distribution_1     
#>  8 2        outliers_1      strength_2      model_1      distribution_1     
#>  9 3        outliers_1      strength_3      model_1      distribution_1     
#> 10 3        outliers_1      strength_3      model_1      distribution_1     
#> # ℹ 14 more rows
#> # ℹ 11 more variables: term <chr>, estimate <dbl>, std.error <dbl>,
#> #   statistic <dbl>, p.value <dbl>, conf.low <dbl>, conf.high <dbl>,
#> #   outliers_branch_code <fct>, strength_branch_code <fct>,
#> #   model_branch_code <fct>, distribution_branch_code <fct>

The resulting data is a tibble object and we can use regular tidyverse grammar to manipulate the data. In the code below, we specifically focus on the estimated coefficient for MasFem and its confidence intervals.

library(dplyr)
res %>%
  filter(term == "MasFem") %>%
  select(outliers_branch, strength_branch, term, estimate, conf.low, conf.high)
#> # A tibble: 6 × 6
#>   outliers_branch strength_branch term   estimate conf.low conf.high
#>   <fct>           <fct>           <chr>     <dbl>    <dbl>     <dbl>
#> 1 outliers_1      strength_1      MasFem   0.0623  0.0427     0.0822
#> 2 outliers_1      strength_2      MasFem   0.0531 -0.00942    0.116 
#> 3 outliers_1      strength_3      MasFem  -0.845  -1.59      -0.103 
#> 4 outliers_2      strength_1      MasFem   0.0623  0.0427     0.0822
#> 5 outliers_2      strength_2      MasFem   0.0956  0.0301     0.161 
#> 6 outliers_2      strength_3      MasFem  -1.02   -1.81      -0.247

Plot a Specification Curve

We can also inspect the result graphically using spec_curve(). The method builds a specification curve (Simonsohn, Simmons, and Nelson 2020) for a term in the regression model specified by var. The method also allows multiple ways of sorting the estimates. See ?spec_curve for details.

spec_summary(mv, var = "MasFem") %>% 
  spec_curve(spec_matrix_spacing = 4) +
  labs(colour = "Significant at 0.05")

References