Description Usage Arguments Details Value Author(s) References

View source: R/identify_covariates.R

Given a matrix of covarites, `identify_covariates`

returns the top `keep_n_covars`

or the indexes of those columns.

1 | ```
identify_covariates(covars, keep_n_covars = 200, indexes = FALSE)
``` |

`covars` |
a matrix or something that can be coerced with |

`keep_n_covars` |
number of covariates to keep |

`indexes` |
Should indexes be returned? Or a subset of |

Columns are sorted in descending order of `min(prevalence, 1-prevalence)`

where `prevalence`

is the the proportion of
non-zero values in a given column.

If `indexes==TRUE`

, a vector of the top `keep_n_covars`

column indexes is returned.

If `indexes==FALSE`

, a matrix of covariates is returned whos columns are the top `keep_n_covars`

colums of
`covars`

. Columns are in their original order.
If also `keep_n_covars >= ncol(covar)`

, then the function returns immediately without ranking columns in terms of
prevalence as it is unecessary.

**Differences from Schneeweiss et al. (2009):**

Covariates that have fewer than 100 non-zero values are not automatically dropped. If typical covariates tend to have more than 100 non-zero values will typically be ranked higher than those with fewer than 100 automatically.

Indexes of identified columns or a subset of `covars`

Sam Lendle

Schneeweiss, S., Rassen, J. A., Glynn, R. J., Avorn, J., Mogun,
H., & Brookhart, M. A. (2009). High-dimensional propensity score adjustment
in studies of treatment effects using health care claims data. *Epidemiology
(Cambridge, Mass.)*, 20(4), 512.

lendle/hdps documentation built on Aug. 18, 2017, 12:11 a.m.

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