Functional Imputation — impute • fChange

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
)

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.

Value

A dfts object of the data with missing values interpolated.

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")
#> X 
#> dimension: 10 x 10
#> season: 1
#> fparam (10): 0 0.1111111 0.2222222 0.3333333 0.4444444 0.5555556 ... 
#> labels (10): 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.. ... 
#> 
#> Example data
#>      [,1]       [,2]       [,3]       [,4] [,5]     [,6]
#> [1,]    6 -0.0541986  0.7779787 -0.7922591   NA 2.053649
#> [2,]    6 -0.6796192 -0.2680903 -1.1136238   NA 2.053649
#> [3,]    3 -0.1617645  0.3205850 -1.5919281   NA 2.053649
#> [4,]    4 -0.0541986 -0.1249798 -1.1974301   NA 2.053649
#> [5,]    5 -0.4905572  1.3617403 -0.4252574   NA 2.053649
#> [6,]    6  1.6459217  0.4818533 -0.4252574   NA 2.053649
impute(temp, method = "linear", obs_share_data = TRUE)
#> X 
#> dimension: 10 x 10
#> season: 1
#> fparam (10): 0 0.1111111 0.2222222 0.3333333 0.4444444 0.5555556 ... 
#> labels (10): 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.. ... 
#> 
#> Example data
#>      [,1]       [,2]       [,3]       [,4]     [,5]     [,6]
#> [1,]    1  4.6601904  0.7779787 -0.7922591 0.891822 2.053649
#> [2,]    2 -0.6796192 -0.2680903 -1.1136238 1.008005 1.977912
#> [3,]    3 -0.1617645  0.3205850 -1.5919281 1.124187 1.902175
#> [4,]    4 -0.3261608 -0.1249798 -1.1974301 1.240370 1.826437
#> [5,]    5 -0.4905572  1.3617403 -0.9017269 1.356553 1.750700
#> [6,]    6  1.6459217  0.4818533 -0.6060237 1.472736 1.674963