fChange (version 2.1.0)

impute: Functional Imputation

Description

Several basic imputation methods for missing values in functional data formatted as dfts objects.

Usage

impute(
  X,
  method = c("zero", "mean_obs", "median_obs", "mean_data", "median_data", "linear",
    "functional"),
  obs_share_data = FALSE
)

Value

A dfts object of the data with missing values interpolated.

Arguments

X

A dfts object or data which can be automatically converted to that format. See dfts().

method

String to indicate method of imputation.

• zero: Fill missing values with 0.

• mean_obs: Fill missing values with the mean of each observation.

• median_obs: Fill missing values with the median of each observation.

• mean_data: Fill missing values with the mean of the data at that particular fparam value.

• median_data: Fill missing values with the median of the data at that particular fparam value.

• linear: Fill missing values with linear interpolation.

• functional: Fill missing values with functional interpolation. This is done by fitting the data to basis with the package 'fda'.

obs_share_data

Boolean in linear interpolation that indicates if data should be shared across observations. For example, if the end of observation i related to the start of observation i+1. Default is FALSE, which suggests independence. If true, the distance between the end and start of observations is taken to be the mean average distance of points in fparam.

Examples

temp <- data.frame(
  c(NA, NA, 3:9, NA),
  c(NA, stats::rnorm(2), NA, stats::rnorm(6)),
  stats::rnorm(10),
  c(stats::rnorm(4), rep(NA, 3), stats::rnorm(3)),
  rep(NA, 10),
  c(stats::rnorm(1), rep(NA, 9)),
  c(stats::rnorm(9), NA),
  stats::rnorm(10),
  stats::rnorm(10),
  c(NA, NA, 3:9, NA)
)
impute(temp, method = "mean_obs")
impute(temp, method = "linear", obs_share_data = TRUE)