Tools to Support Relative Importance Analysis

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Overview

The {domir} package provides tools that allow relative importance analysis across a wide variety of data analytic situations an analyst might encounter. With {domir}, if you have a statistical/machine learning model and an extractor function to obtain a fit statistic, you can conduct a relative importance analysis to evaluate the importance of independent variables/features/predictors in the model.

More specifically, {domir} provides a flexible wrapper function for conducting relative importance analysis. The current implementation of the package focuses solely on dominance analysis with the domin function that can accommodate modeling functions that use R formulas (or that can be adapted to do so by the user; which effectively encompasses any model).

An extensive conceptual introduction to dominance analysis is provided as a vignette in the package. Much of the content to follow will eventually be included in vignettes in the future.

Installation

To install the most recent stable version of domir from CRAN use:

install.packages("domir")

To install the working development version of {domir} using the devtools package use:

devtools::install_github("https://github.com/jluchman/domir")

What {domir} Does

Before discussing details of the {domir} package, I provide some examples of what {domir} can do.

The focus of this section is on outlining how domir::domin extends existing packages and on the structure of the function.

Comparison with Existing Relative Importance Packages

Fundamentally, domir::domin is an extension of the “lmg” type for the calc.relimpo function in the {relaimpo} package as well as the dominanceAnalysis function in the {dominanceanalysis} package.

domir::domin can replicate the results produced by the above packages but, as will be seen, requires a “deconstructed” the model to be submitted to it. This difference in structure does make domin more complex but also allows the function a great deal more flexibility in terms of the kinds of models and fit statistics that can be dominance analyzed.

Before discussing some of the elements that make domin flexible, consider the following example that shows how domin is similar to existing packages. All three of the dominance analysis results to come are based on the following linear model:

lm(mpg ~ am + vs + cyl, data = mtcars)

The variance explained R2 will be the focal fit statistic.

{domir}’s domin

domin(mpg ~ am + vs + cyl, 
      lm, 
      list(summary, "r.squared"), 
      data = mtcars)
## Overall Fit Statistic:      0.7619773 
## 
## General Dominance Statistics:
##     General Dominance Standardized Ranks
## am          0.1774892    0.2329324     3
## vs          0.2027032    0.2660226     2
## cyl         0.3817849    0.5010450     1
## 
## Conditional Dominance Statistics:
##        IVs: 1    IVs: 2      IVs: 3
## am  0.3597989 0.1389842 0.033684441
## vs  0.4409477 0.1641982 0.002963748
## cyl 0.7261800 0.3432799 0.075894823
## 
## Complete Dominance Designations:
##             Dmnated?am Dmnated?vs Dmnated?cyl
## Dmnates?am          NA         NA       FALSE
## Dmnates?vs          NA         NA       FALSE
## Dmnates?cyl       TRUE       TRUE          NA

In domin, the lm model is submitted in pieces. Specifically, the key inputs were the formula (mpg ~ am + vs + cyl) and the model to be called using the formula (lm). In this way, domin is a Map- or apply-like function as it receives an object on which to operate (i.e., the formula) and a function to which to apply to it.

In being like apply, domin is agnostic to the fit statistic to use for the model called and it must be supplied with a list outlining extractor function information (list(summary, "r.squared"); described further in the Details section).

Like apply, other arguments (data = mtcars) can also be passed to each call of lm.

The focus of domin’s print-ed results focuses on the numerical results from “General Dominance Statistics” and “Conditional Dominance Statistics” and, a logical matrix of “Complete Dominance Designations”.

{relaimpo}’s calc.relimp with “lmg”

relaimpo::calc.relimp(mpg ~ am + vs + cyl, 
                      data = mtcars, 
                      type = "lmg")
## Response variable: mpg 
## Total response variance: 36.3241 
## Analysis based on 32 observations 
## 
## 3 Regressors: 
## am vs cyl 
## Proportion of variance explained by model: 76.2%
## Metrics are not normalized (rela=FALSE). 
## 
## Relative importance metrics: 
## 
##           lmg
## am  0.1774892
## vs  0.2027032
## cyl 0.3817849
## 
## Average coefficients for different model sizes: 
## 
##            1X       2Xs       3Xs
## am   7.244939  4.316851  3.026480
## vs   7.940476  2.995142  1.294614
## cyl -2.875790 -2.795816 -2.137632

{relaimpo}’s calc.relimp accepts only lm models and the variance explained R2 as a fit statistic. As a result, the function does not ask for model or fit statistic type.

The function’s printed results provide the “lmg” relative importance statistics (i.e., General Dominance Statistics) and, in addition, report the average lm coefficients across all models.

{dominanceanalysis}’s dominanceAnalysis

dominanceanalysis::dominanceAnalysis(lm(mpg ~ am + vs + cyl, 
                                        data = mtcars))
## 
## Dominance analysis
## Predictors: am, vs, cyl 
## Fit-indices: r2 
## 
## * Fit index:  r2 
##     complete conditional general
## am                              
## vs                            am
## cyl    am,vs       am,vs   am,vs
## 
## Average contribution:
##   cyl    vs    am 
## 0.382 0.203 0.177

{dominanceanalysis}’s dominanceAnalysis implements dominance analysis for specific models, of which lm is a supported model. dominanceAnalysis accepts a fitted lm object as input and uses the explained variance R2 as the fit statistic.

dominanceAnalysis’s printed output is focused on qualitative dominance designations but also reports the, magnitude sorted, average contribution (i.e., General Dominance Statistic) values.

How {domir} Extends on Previous Packages

The intention of {domir} is to extend relative importance to new data analytic situations the user might encounter where a dominance analysis could be valuable.

The sections below outline some pertinent examples of specific models that the domin function can accommodate.

Linear Model Revisited

domin is fit statistic agnostic and, as such, one component of its flexibility is in allowing the user to apply any applicable fit statistic for a model for the purposes of relative importance analysis.

In this example, the explained variance R2 is swapped with an alternative, but nonetheless applicable, fit statistic: the McFadden pseudo-R2 as implemented by the {pscl} package.

Note the use of the pipes to capture.output and invisible. These are not not strictly necessary but if not used will print far more output than is needed as pscl::pR2 is a rather verbose function and will print a message for each model fitted.

(mcf_da_lm <- 
   domin(mpg ~ am + vs + cyl, 
         lm, 
         list(pscl::pR2, "McFadden"), 
         data = mtcars)) |> 
  capture.output() |> 
  invisible()

mcf_da_lm
## Overall Fit Statistic:      0.2243283 
## 
## General Dominance Statistics:
##     General Dominance Standardized Ranks
## am         0.04848726    0.2161442     3
## vs         0.04970277    0.2215627     2
## cyl        0.12613826    0.5622931     1
## 
## Conditional Dominance Statistics:
##         IVs: 1     IVs: 2      IVs: 3
## am  0.06969842 0.05507782 0.020685547
## vs  0.09088103 0.05629333 0.001933959
## cyl 0.20243215 0.13272881 0.043253806
## 
## Complete Dominance Designations:
##             Dmnated?am Dmnated?vs Dmnated?cyl
## Dmnates?am          NA         NA       FALSE
## Dmnates?vs          NA         NA       FALSE
## Dmnates?cyl       TRUE       TRUE          NA

Note that this fit statistic produces effectively the same answers, in terms of qualitative importance inferences about the terms, as that from the explained variance R2.

Ordered Logistic Regression

domin acts like an apply function for models and does not have built in methods. This is another component of its flexibility as it can accommodate functions that, to this point, have not been supported in relative importance analysis. One pertinent example is the polr function from the {MASS} package also using pscl::pR2 as fit statistic.

mtcars2 <- data.frame(mtcars, carb2 = as.factor(mtcars$carb))

(da_polr <- 
    domin(carb2 ~ am + vs + mpg, 
          MASS::polr, 
          list(pscl::pR2, "McFadden"), 
          data = mtcars2)) |> 
  capture.output() |> 
  invisible()

da_polr
## Overall Fit Statistic:      0.2647682 
## 
## General Dominance Statistics:
##     General Dominance Standardized Ranks
## am         0.04221668    0.1594477     3
## vs         0.09264306    0.3499026     2
## mpg        0.12990844    0.4906497     1
## 
## Conditional Dominance Statistics:
##          IVs: 1     IVs: 2     IVs: 3
## am  0.001505741 0.05272927 0.07241503
## vs  0.161029601 0.10315565 0.01374394
## mpg 0.151278401 0.14042103 0.09802589
## 
## Complete Dominance Designations:
##             Dmnated?am Dmnated?vs Dmnated?mpg
## Dmnates?am          NA         NA       FALSE
## Dmnates?vs          NA         NA          NA
## Dmnates?mpg       TRUE         NA          NA

Decision Trees

domin can also accept models that do not produce model coefficients like rpart::rpart.

domin(mpg ~ am + vs + cyl, 
      rpart::rpart, 
      list(\(model) 
            list(R2 = 1-model$cptable[nrow(model$cptable), 3]), 
            "R2"),
      data = mtcars)
## Overall Fit Statistic:      0.7324601 
## 
## General Dominance Statistics:
##     General Dominance Standardized Ranks
## am          0.1199330    0.1637400     3
## vs          0.1605074    0.2191346     2
## cyl         0.4520197    0.6171254     1
## 
## Conditional Dominance Statistics:
##        IVs: 1     IVs: 2    IVs: 3
## am  0.3597989 0.00000000 0.0000000
## vs  0.4409477 0.04057437 0.0000000
## cyl 0.7324601 0.33208674 0.2915124
## 
## Complete Dominance Designations:
##             Dmnated?am Dmnated?vs Dmnated?cyl
## Dmnates?am          NA         NA       FALSE
## Dmnates?vs          NA         NA       FALSE
## Dmnates?cyl       TRUE       TRUE          NA

Note that an anonymous function can be used as a valid submission to the fitstat argument. In this case, the anonymous function transforms and extracts the proportion of error from the rpart object. If the model object returns its own fit statistic, it can be extracted using an anonymous function.

Multinomial Logistic (softmax) Regression with Extra Features

domin, similar to other packages, can combine multiple terms into a single set as well as use one or more terms as covariate(s) in all model subsets.

This example outlines another model, multinom from the {nnet} package,
another function that has not been accommodated in relative importance packages, that uses sets and all/covariate terms.

In addition, complete = FALSE which saves a little computation time and suppresses reporting complete dominance designations.

(da_mnl <- 
  domin(carb2 ~ mpg, 
      nnet::multinom, 
      list(pscl::pR2, "McFadden"), 
      sets = list(c("am", "vs"), c("cyl", "disp")),
      all = c("gear"),
      complete = FALSE,
      data = mtcars2)) |> 
  capture.output() |> 
  invisible()

da_mnl
## Overall Fit Statistic:      0.9282015 
## All Subsets Fit Statistic:  0.1393919 
## 
## General Dominance Statistics:
##      General Dominance Standardized Ranks
## mpg          0.2958544    0.3187394     2
## set1         0.1770852    0.1907832     3
## set2         0.3158700    0.3403033     1
## 
## Conditional Dominance Statistics:
##         IVs: 1    IVs: 2    IVs: 3
## mpg  0.4452671 0.2553281 0.1869679
## set1 0.2886101 0.1365589 0.1060867
## set2 0.5769312 0.2753437 0.0953351
## 
## Components of sets:
## set1 : am vs 
## set2 : cyl disp 
## 
## All subsets variables: gear
da_mnl$Subset_Details$Full_Model
## carb2 ~ mpg + am + vs + cyl + disp + gear
## <environment: 0x55e5feae2600>

The domin automatically combines the entries in the formula_overall, sets, and all arguments. The full model formula can be obtained from the domin object in the .$Subset_Details$Full_Model element.

Zero-Inflated Poisson with Wrapper Function

Although domin can work directly with modeling functions that accept standard formula, more complex formulas such as those used by models such as zeroinfl models from the package {pscl} can also be accommodated using wrapper functions.

The below wrapper functionzinfl_wrap uses the entries in the formula to create a symmetric count and zero-inflation formulas that will be submitted to zeroinfl model.

In an effort to illustrate what each model submitted to zeroinfl looks like, the model formula for all 7 models is printed before each run.

zinfl_wrap <- function(model, ...) {
  zip_terms <- model |> terms() |> attr("term.labels") |> paste(collapse = " + ")
  zip_formula_rhs <- zip_terms |> rep(times = 2) |> paste(collapse = " | ")
  zip_formula_lhs <- (model |> all.vars())[[1]]
  zip_formula <- c(zip_formula_lhs, zip_formula_rhs) |> paste(collapse = " ~ ") |> as.formula()
  print(deparse(zip_formula))
  pscl::zeroinfl(zip_formula, ...)
}

domin(art ~ fem + mar + kid5, 
      zinfl_wrap,
      list(\(model) {capture.output(result <- pscl::pR2(model)); result}, "McFadden"), 
      data=pscl::bioChemists)
## [1] "art ~ fem | fem"
## [1] "art ~ mar | mar"
## [1] "art ~ kid5 | kid5"
## [1] "art ~ fem + mar | fem + mar"
## [1] "art ~ fem + kid5 | fem + kid5"
## [1] "art ~ mar + kid5 | mar + kid5"
## [1] "art ~ fem + mar + kid5 | fem + mar + kid5"

## Overall Fit Statistic:      0.009101817 
## 
## General Dominance Statistics:
##      General Dominance Standardized Ranks
## fem       0.0059812901   0.65715343     1
## mar       0.0008482014   0.09319035     3
## kid5      0.0022723252   0.24965622     2
## 
## Conditional Dominance Statistics:
##            IVs: 1      IVs: 2      IVs: 3
## fem  0.0054489923 0.006059012 0.006435866
## mar  0.0005852711 0.000925923 0.001033410
## kid5 0.0008854100 0.002350047 0.003581519
## 
## Complete Dominance Designations:
##              Dmnated?fem Dmnated?mar Dmnated?kid5
## Dmnates?fem           NA        TRUE         TRUE
## Dmnates?mar        FALSE          NA        FALSE
## Dmnates?kid5       FALSE        TRUE           NA

Further discussion of how to generate wrapper commands is outlined below in the Details section.


Details

Having provided some examples of what domin can do, this section moves on to outline details of how domin works by way of the structure of the function.

domin estimates models for all possible subsets of the terms submitted to it by repeatedly calling different models using different subsets of terms and collecting their results for processing/averaging. domin takes inspiration from the apply family of functions and works in a similar way - invoking repeated function calls from three ‘building block’ arguments:

  1. a formula
  2. a modeling function
  3. list of instructions to call an extractor function that obtains a model fit statistic

You can think of domin as repeatedly invoking the following process:

modeling_function(formula) |> fit_statistic()

Hence, the modeling function is called using the formula input with a subset of terms and the results of the modeling function are are ‘piped’ to the fit statistic function that is used for dominance statistic computation.

In the sections below, each of the three arguments to domin are discussed in greater detail.

1) Formula

The first, and most important, argument for domin is the formula input. Understanding how the formula input is constructed and submitted to each call of the modeling function is important for the effective use of domin.

The formula components are the most important pieces of domin as they directly define the terms used to dominance analyze the model and, thus, the scope of all subsets of models used in the dominance statistics.

The formula input is derived from three arguments in domin.

  1. the formula_overall argument
  2. the sets argument
  3. the all argument

formula_overall

The formula_overall argument must be a formula object and take the form of response ~ terms as that is a standard format for many modeling functions such as lm and glm.

The entries on the right hand side of the formula are parsed using the terms utilities in the stats library. The desired behavior is that variable names separated by + are used as different terms in computing all combinations of variables in the dominance analysis.

It is also important to note that all special formula processing is applied to the formula including the use of I(), :, *, and offset(). The actual list of terms on which domin computes all combinations is obtained from attr(,"term.labels"). If the user wants to test to see what domin will do with the formula submitted use: formula(.) |> terms() |> attr("term.labels") to see which labels it will produce. Note that domin does not have a method to accommodate second or higher order terms like {relaimpo} and will issue a warning when second or higher order terms (i.e., any two variables are */multiplied together) are used.

One important point of note is that, despite the use of some special formula processing, formula_overall is not ‘data frame aware’. That is, shorthand such as ~ . will not work to select variables in a data frame even if a data argument is supplied to the domin function. To use ~ . the user can still process the formula outside of domin as by supplying the data argument to terms with the desired formula:

formula(mpg ~ .) |> terms(data=mtcars) |> formula()
## mpg ~ cyl + disp + hp + drat + wt + qsec + vs + am + gear + carb

As can be seen, this produces a formula that can be used by domin.

A formula that includes only an intercept term is accepted and effectively ignores the right hand side of the formula in the case that there are only sets terms under consideration in the dominance analysis.

The formula must include a response. Predictive modeling functions in reg that do not must be adapted to do so.

sets

Each list element submitted will be treated as another “term” in the dominance analysis and all the individual variables in the list element will be included and excluded together from the dominance analysis.

Consequently, the sets argument must be submitted as a list. sets expects that the elements of the list will be formatted as a vector of string names (i.e., c("a", "b")) and/or formula-like string of text (i.e., "a + b"). In the case that the list element is a vector, domin will combine the vector’s elements to create a formula-like string by collapsing the elements with +’s. Individual names and formula-like strings can be combined in a vector.

Special formula processing is not applied to the entries in sets and the elements in each set of terms will be passed to the modeling function as supplied by the user.

all

This argument is used as a set of covariates. These variables are added to the formula but are not considered a part of the all subsets computations.

The all argument is, similar to sets expected to be a vector of string names and/or formula-like string of text. Also similar to sets, the entries in the all argument will be combined

After processing, the formula object is combined with entries from the sets argument. The entries in the sets argument are minimally processed and are combined to create a formula-like string by collapsing the elements with +’s.

Special formula processing is also not applied to the entries in all and the elements in this argument will be passed to the modeling function as supplied by the user.

Combining

All three pieces of the formula component of domin are combined and submitted as a formula object, to call models. This is important to note for users looking to create wrapper functions for use in domin and is used in the Zero-Inflated Poisson with Wrapper Function section above.

Each of the different types of components of the formula contribute differently to the number of regressions run which total: 2p, where p is the number of terms in the dominance analysis.

The entries in formula_overall result in one term for each variable separated by +. These elements tend to increase p the fastest and result in much larger

Each element in the list submitted to sets also results in another term. Given that multiple variables can be combined within a set, these can be used to reduce the size of

The entries in all are covariates and do not contribute to the terms used in computing all subsets of models.

One important note is that factor()s will also not be expanded into their coefficient form in the formula_overall at this stage and thus, irrespective of the number of levels, a factor variable is considered a single term for the all subsets computations.

There must be at least two terms in the formula_overall or sets arguments for domin to proceed.

2) Modeling Function

The second input is the modeling function that is called repeatedly by domin. The only requirement for this modeling function is that it accepts a standard formula or can be adapted by the user to do so with a customized wrapper function (see this section for an example).

The modeling function passes arguments to do.call and allows any function that do.call can accommodate. For example, glm can be called as a string (i.e., with quotes "glm"), a name (i.e., without quotes and with or without namespace; glm, stats::glm), or as an anonymous function (e.g., function(x, ...) glm(x, ...), \(x, ...) glm(x, ...)).

All function arguments that are not used by domin (e.g., a data argument) are passed (via ...) to each call of this function/all models estimated.

The modeling function that is called repeatedly by domin uses the formula as the first argument always followed by all other arguments. The modeling function must thus accept a formula as it’s first argument or must be adapted using a wrapper function to do so.

Note that the modeling function must return an object that can be passed to the fit statistic extractor function discussed next.

3) Fit Statistic Extractor Function

The third input is a list of arguments that are used to call a fit statistic extractor function using the model object created by the modeling function discussed above.

Like the modeling function entries, the fit statistic extractor function passes arguments to do.call and does so in a specific order/positionally.

The first element of the list of arguments for the fit extractor function can be any function called as a string (i.e., with quotes), a name (i.e., without quotes and with or without namespace), or as an anonymous function (see this section for an example). This element of the fit extractor function list is required.

The second element of the list is a string that indicates the element of the object returned by the fit statistic extractor function to be used for dominance analysis. Thus, the fit statistic extractor function should return (or be adapted to return) a named vector or list from which domin can select the fit statistic. This element of the fit extractor function list is required.

The third element, and every subsequent element, in the list is optional and submitted as additional (an) argument(s) to the fit statistic extractor function. These elements are effectively a second set of ... elements but must be placed in the third and any subsequent element positions in this list.

Finally, as mentioned in the modeling function section, the fit statistic extractor function must accept the model object/result of the modeling function and must do so as it’s first argument. Note that the model object is passed automatically by domin to the fit statistic extractor function call.

When considered together, the user can think about the fit statistic extractor’s list, as constructed and used in domin, as having the following structure:

first_element(model_object, third_element_etc)[second_element]

Currently, domin expects to receive, and can only accommodate, scalar-valued (i.e., vector of length 1) fit statistics.

Overall Considerations

This section outlines a few key considerations for the effective use of domin.

domin does not check to ensure that the sample underlying the modeling is consistent across modeling runs on which the dominance statistics are computed. If the modeling function uses a na.action that omits missing responses, and different variables have different missing observations, the sample included for each modeling run will vary. I recommend filtering the data to include only the sample that does not have any missing data on the variables included.

domin only does a few basic checks on the input to the function to ensure that it meets expectations and is otherwise the responsibility of the user to ensure that the arguments submitted to domin conform to expectation (hence the extensive discussion here of expected inputs). This is also, partly, the reason that results of individual model fits in computing all subsets are not captured–so the user can see what models are actually being fit and diagnose problems–at the expense of being potentially rather verbose.

Author’s Opinions: Development Perspective

I have a few opinions about relative importance that will likely guide development of this package’s functionality in the shorter term.

In my view, domin is a tool for model explanation/evaluation (i.e., understanding a fitted, pre-selected model) and is not as useful for model selection (i.e., choosing a “final” model). The term importance is often used for model selection-like applications, especially for machine learning models, when the term coined by Azen, Budescu, and Reiser (2001) “criticality” is likely a more appropriate name. In the end, if a predictor has a non-trivial chance of not being selected, it should probably not be in an importance analysis like domin. Into the foreseeable future, domir will not offer methods that can flag predictors that have no importance as that is the purpose of model selection methods.

In addition, I see relatively little value in applying inferential methods such as bootstrapping to importance methods like those offered in domin. Given that the model applied to domin should have passed through model selection methods and, potentially, have applied inferential methods to obtain confidence intervals and standard errors at the stage of model selection, the application of similar methods at this subsequent stage of model evaluation seems excessive. That is, obtaining the stability of importance statistics using confidence intervals with bootstrapping, while possible to implement, is computationally demanding and, in my experience, offers little additional information over and above the confidence intervals of the base model. Unstable coefficients have unstable importance statistics. The source of the instability tends to be due to predictor overlap, which can be affected by peculiarities of the sample, but can also be observed in the pattern of conditional dominance statistics produced. I recommend using these statistics first and only obtaining confidence intervals for dominance statistics in situations where the user suspects a great deal of instability–though this also might imply the base model needs adjusting.