`R/variable_importance.R`

`calculate_linear_vimp.Rd`

`calculate_linear_vimp`

estimates the linear hypothesis test of removing a particular moderator
from a linear model containing all moderators. Unlike `calculate_vimp`

, this will only be
unbiased and have correct asymptotic coverage rates if the true model is linear. This linear
approach is also substantially faster, so may be useful when prototyping an analysis.

```
calculate_linear_vimp(
full_data,
weight_col,
pseudo_outcome,
...,
.VIMP_cfg,
.Model_cfg
)
```

- full_data
dataframe

- weight_col
Unquoted name of the weight column.

- pseudo_outcome
Unquoted name of the pseudo-outcome.

- ...
Unquoted names of covariates to include in the joint effect model. The variable importance will be calculated for each of these covariates.

- .VIMP_cfg
A

`VIMP_cfg`

object defining how VIMP should be estimated.- .Model_cfg
A

`Model_cfg`

object defining how the joint effect model should be estimated.

Williamson, B. D., Gilbert, P. B., Carone, M., & Simon, N. (2021). Nonparametric variable importance assessment using machine learning techniques. Biometrics, 77(1), 9-22.

Williamson, B. D., Gilbert, P. B., Simon, N. R., & Carone, M. (2021). A general framework for inference on algorithm-agnostic variable importance. Journal of the American Statistical Association, 1-14.