Last Observation Carried Forward

In longitudinal studies it's common that individuals drop out before all responses can be obtained. Measurements obtained before the individual dropped out can be used to impute the unknown measurement(s). The last observation carried forward method is one way to impute values for the missing observations. For the last observation carried forward (LOCF) approach the missing values are replaced by the last observed value of that variable for each individual regardless of when it occurred.

Usage

locf(x)

Arguments

x

a vector, a data.frame or a matrix containing NAs.

Value

an object of the same type and dimension as x.

Details

locf() replaces NAs with the most recent non-NA prior to it.

The function will replace all NAs found in a vector with the last earlier value not being NA. In data frames and matrices each column is treated separately, so that values are never carried across column boundaries. Factors are supported and keep their levels and ordering.

It should be noted, that the last observation carried forward approach may result in biased estimates and may underestimate the variability.

Note

Based on code by Daniel Wollschlaeger, adapted to conform to package standards; multi-column, data-frame, and factor support added by the package author.

See also

See also the package Hmisc for less coarse imputation functions.

Other vector.na: coalesceX(), isNA(), naIf(), naReplace()

Examples

d.frm <- data.frame(
  day=rep(c("mon", "tue", "wed", "thu", "fri", "sat", "sun"), 4)
, val=rep(c(runif(5), rep(NA,2)), 4) )

d.frm$locf <- locf( d.frm$val )
d.frm
#>    day        val       locf
#> 1  mon 0.64855298 0.64855298
#> 2  tue 0.06010516 0.06010516
#> 3  wed 0.05253167 0.05253167
#> 4  thu 0.21957116 0.21957116
#> 5  fri 0.13193263 0.13193263
#> 6  sat         NA 0.13193263
#> 7  sun         NA 0.13193263
#> 8  mon 0.64855298 0.64855298
#> 9  tue 0.06010516 0.06010516
#> 10 wed 0.05253167 0.05253167
#> 11 thu 0.21957116 0.21957116
#> 12 fri 0.13193263 0.13193263
#> 13 sat         NA 0.13193263
#> 14 sun         NA 0.13193263
#> 15 mon 0.64855298 0.64855298
#> 16 tue 0.06010516 0.06010516
#> 17 wed 0.05253167 0.05253167
#> 18 thu 0.21957116 0.21957116
#> 19 fri 0.13193263 0.13193263
#> 20 sat         NA 0.13193263
#> 21 sun         NA 0.13193263
#> 22 mon 0.64855298 0.64855298
#> 23 tue 0.06010516 0.06010516
#> 24 wed 0.05253167 0.05253167
#> 25 thu 0.21957116 0.21957116
#> 26 fri 0.13193263 0.13193263
#> 27 sat         NA 0.13193263
#> 28 sun         NA 0.13193263