expand: Expand and fill in missing 'data.frame' rows

In rbmi: Reference Based Multiple Imputation

Expand and fill in missing data.frame rows

Description

These functions are essentially wrappers around base::expand.grid() to ensure that missing combinations of data are inserted into a data.frame with imputation/fill methods for updating covariate values of newly created rows.

Usage

expand(data, ...)

fill_locf(data, vars, group = NULL, order = NULL)

expand_locf(data, ..., vars, group, order)

Arguments

data	dataset to expand or fill in.
...	variables and the levels that should be expanded out (note that duplicate entries of levels will result in multiple rows for that level).
vars	character vector containing the names of variables that need to be filled in.
group	character vector containing the names of variables to group by when performing LOCF imputation of var.
order	character vector containing the names of additional variables to sort the data.frame by before performing LOCF.

Details

The draws() function makes the assumption that all subjects and visits are present in the data.frame and that all covariate values are non missing; expand(), fill_locf() and expand_locf() are utility functions to support users in ensuring that their data.frame's conform to these assumptions.

expand() takes vectors for expected levels in a data.frame and expands out all combinations inserting any missing rows into the data.frame. Note that all "expanded" variables are cast as factors.

fill_locf() applies LOCF imputation to named covariates to fill in any NAs created by the insertion of new rows by expand() (though do note that no distinction is made between existing NAs and newly created NAs). Note that the data.frame is sorted by c(group, order) before performing the LOCF imputation; the data.frame will be returned in the original sort order however.

expand_locf() a simple composition function of fill_locf() and expand() i.e. fill_locf(expand(...)).

Missing First Values

The fill_locf() function performs last observation carried forward imputation. A natural consequence of this is that it is unable to impute missing observations if the observation is the first value for a given subject / grouping. These values are deliberately not imputed as doing so risks silent errors in the case of time varying covariates. One solution is to first use expand_locf() on just the visit variable and time varying covariates and then merge on the baseline covariates afterwards i.e.

library(dplyr)

dat_expanded <- expand(
    data = dat,
    subject = c("pt1", "pt2", "pt3", "pt4"),
    visit = c("vis1", "vis2", "vis3")
)

dat_filled <- dat_expanded %>%
    left_join(baseline_covariates, by = "subject")

Examples

## Not run: 
dat_expanded <- expand(
    data = dat,
    subject = c("pt1", "pt2", "pt3", "pt4"),
    visit = c("vis1", "vis2", "vis3")
)

dat_filled <- fill_loc(
    data = dat_expanded,
    vars = c("Sex", "Age"),
    group = "subject",
    order = "visit"
)

## Or

dat_filled <- expand_locf(
    data = dat,
    subject = c("pt1", "pt2", "pt3", "pt4"),
    visit = c("vis1", "vis2", "vis3"),
    vars = c("Sex", "Age"),
    group = "subject",
    order = "visit"
)

## End(Not run)

rbmi documentation built on Nov. 24, 2023, 5:11 p.m.