```
library("vimp")
library("SuperLearner")
```

In the main vignette, I
discussed variable importance defined using R-squared. I also mentioned
that all of the analyses were carried out using a *condititonal*
variable importance measure. In this document, I will discuss all three
types of variable importance that may be computed using
`vimp`

.

In general, I define variable importance as a function of the true
population distribution (denoted by \(P_0\)) and a predictiveness measure \(V\) – large values of \(V\) are assumed to be better. Currently,
the measures \(V\) implemented in
`vimp`

are \(R^2\),
classification accuracy, area under the receiver operating
characteristic curve (AUC), and deviance. For a fixed function \(f\), the predictiveness is given by \(V(f, P)\), where large values imply that
\(f\) is a good predictor of the
outcome. The best possible prediction function, \(f_0\), is the *oracle* model – i.e.,
the prediction function that I would use if I had access to the
distribution \(P_0\). Often, \(f_0\) is the true conditional mean (e.g.,
for \(R^2\)). Then the *total oracle
predictiveness* can be defined as \(V(f_0,
P_0)\). This is the best possible value of predictiveness.

I define variable importance measures (VIMs) as contrasts in oracle predictivness. The oracle models that I plug in determine what type of variable importance is being considered, as I outline below. For the remainder of this document, suppose that I have \(p\) variables, and an index set \(s\) of interest (containing some subset of the \(p\) variables). Throughout this document, I will use the VRC01 data (Magaret, Benkeser, Williamson, et al. 2019), a subset of the data freely available from the Los Alamos National Laboratory’s Compile, Neutralize, and Tally Neutralizing Antibody Panels database. Information about these data is available here. Throughout, I will also use a simple library of learners for the Super Learner (this is for illustration only; in practice, I suggest using a large library of learners, as outlined in the main vignette). Finally, I will use the area under the receiver operating characteristic curve (AUC) to measure importance.

```
# read in the data
data("vrc01")
# subset to the columns of interest for this analysis
library("dplyr")
library("tidyselect")
# retain only the columns of interest for this analysis
<- vrc01$ic50.censored
y <- vrc01 %>%
X select(starts_with("geog"), starts_with("subtype"), starts_with("length"))
<- "SL.glm" learners
```

The *reduced oracle predictiveness* is defined as \(V(f_{0,-s}, P_0)\), where \(f_{0,-s}\) is the best possible prediction
function that *does not use the covariates with index in \(s\)*. Then the conditional VIM is
defined as \[V(f_0, P_0) - V(f_{0,-s},
P_0).\] This is the measure of importance that I estimated in the
main vignette. To estimate the
conditional VIM for family history of heart disease, I can use the
following code:

```
# note the use of a small V and a small number of SL folds, for illustration only
set.seed(1234)
<- 2
V <- list(V = 2)
sl_cvcontrol <- vimp_auc(Y = y, X = X, indx = 5, SL.library = learners, na.rm = TRUE, V = V, cvControl = sl_cvcontrol) subtype_01_AE_cond
```

The *marginal oracle predictiveness* is defined as \(V(f_{0,s}, P_0)\), where \(f_{0,s}\) is the best possible prediction
function that *only uses the covariates with index in \(s\)*. The *null oracle
predictiveness* is defined as \(V(f_{0,
\emptyset}, P_0)\), where \(f_{0,\emptyset}\) is the best possible
prediction function that *uses no covariates* (i.e., is fitting
the mean). Then the marginal VIM is defined as \[V(f_{0,s}, P_0) - V(f_{0,\emptyset},
P_0).\] To estimate the marginal VIM for family history of heart
disease, I can use the following code:

```
# note the use of a small V and a small number of SL folds, for illustration only
set.seed(5678)
<- vimp_auc(Y = y, X = X[, 5, drop = FALSE], indx = 1, SL.library = learners, na.rm = TRUE, V = V, cvControl = sl_cvcontrol) subtype_01_AE_marg
```

The Shapley population VIM (SPVIM) generalizes the marginal and
conditional VIMs by averaging over all possible subsets. More
specifically, the SPVIM for feature \(j\) is given by \[\sum_{s \subseteq \{1,\ldots,p\} \setminus \{j\}}
\binom{p-1}{\lvert s \rvert}^{-1}\{V(f_{0, s \cup \{j\}}, P_0)) -
V(f_{0,s}, P_0)\};\] this is the average gain in predictiveness
from adding feature \(j\) to each
possible grouping of the other features. To estimate the SPVIM for
family history of heart disease, I can use the following code (note that
`sp_vim`

returns VIM estimates for all features):

```
set.seed(91011)
<- sp_vim(Y = y, X = X, type = "auc", SL.library = learners, na.rm = TRUE, V = V, cvControl = sl_cvcontrol, env = environment()) all_vim_spvim
```

In some cases, there may be confounding factors that you want to adjust for in all cases. For example, in HIV vaccine studies, we often adjust for baseline demographic variables, including age and behavioral factors. If this is the case, then the null predictiveness above can be modified to be \(V(f_{0,c}, P_0)\), where \(c\) is the index set of all confounders.

The three VIMs defined here may be different for a given feature of interest. Indeed, we can see this for whether or not subtype is 01_AE in the VRC01 data:

```
subtype_01_AE_cond#> Variable importance estimates:
#> Estimate SE 95% CI VIMP > 0 p-value
#> s = 5 0.001778934 0.05614002 [0, 0.1118113] FALSE 0.4873607
subtype_01_AE_marg#> Variable importance estimates:
#> Estimate SE 95% CI VIMP > 0 p-value
#> s = 1 0.01302083 0.1073992 [0, 0.2235194] FALSE 0.4517514
# note: need to look at row for s = 5
all_vim_spvim#> Variable importance estimates:
#> Estimate SE 95% CI VIMP > 0 p-value
#> s = 1 0.000000000 0.021791723 [0, 0.04271099] FALSE 0.5000000
#> s = 2 0.011043008 0.017183290 [0, 0.04472164] FALSE 0.4557810
#> s = 3 0.010119884 0.028051877 [0, 0.06510055] FALSE 0.4600018
#> s = 4 0.012518761 0.034394897 [0, 0.07993152] FALSE 0.4509761
#> s = 5 0.000000000 0.013993958 [0, 0.02742765] FALSE 0.5000000
#> s = 6 0.000000000 0.006363417 [0, 0.01247207] FALSE 0.5000000
#> s = 7 0.000000000 0.009907345 [0, 0.01941804] FALSE 0.5000000
#> s = 8 0.015550728 0.019018377 [0, 0.05282606] FALSE 0.4379738
#> s = 9 0.000000000 0.023765091 [0, 0.04657872] FALSE 0.5000000
#> s = 10 0.000000000 0.018762896 [0, 0.03677460] FALSE 0.5000000
#> s = 11 0.014845056 0.018106896 [0, 0.05033392] FALSE 0.4407254
#> s = 12 0.034118865 0.033878081 [0, 0.10051868] FALSE 0.3683432
#> s = 13 0.000000000 0.013849446 [0, 0.02714442] FALSE 0.5000000
#> s = 14 0.007543575 0.023941056 [0, 0.05446718] FALSE 0.4699677
#> s = 15 0.000000000 0.015921007 [0, 0.03120460] FALSE 0.5000000
#> s = 16 0.043251122 0.065473115 [0, 0.17157607] FALSE 0.3454781
#> s = 17 0.035069436 0.064557522 [0, 0.16159985] FALSE 0.3735006
#> s = 18 0.044388824 0.051888790 [0, 0.14608898] FALSE 0.3366255
#> s = 19 0.001066722 0.020758848 [0, 0.04175332] FALSE 0.4957381
#> s = 20 0.028659642 0.054194553 [0, 0.13487901] FALSE 0.3933259
#> s = 21 0.017929705 0.040749285 [0, 0.09779684] FALSE 0.4308049
```

This is simply a function of the fact that the VIMs are different population parameters. All three likely provide useful information in practice:

- the marginal VIM provides information about the predictiveness of the covariate in isolation;
- the conditional VIM provides information about the predictiveness of the covariate adjusting for all other covariates; and
- the SPVIM provides information about the predictiveness of the covariate averaged over all sets of adjustment variables.

To choose a VIM, identify which of these three (there may be more than one) that best addresses your scientific question.

Magaret, CA, DC Benkeser, BD Williamson, et al. 2019. “Prediction
of Vrc01 Neutralization Sensitivity by HIV-1
Gp160 Sequence Features.” *PLOS Computational Biology*. https://doi.org/10.1371/journal.pcbi.1006952.