Imputes the missing values in the input data

This function uses Amelia::amelia to impute missing (NA) values in the input data set. This function averages over multiple Amelia imputations to obtain more consistent results. The Amelia imputation model uses all variables of the supplied data_matrix, the first lag of those variables, time, time squared, and day-part dummies.

impute_datamatrix(data_matrix, measurements_per_day, imputation_iterations)

Arguments

data_matrix	The raw, unimputed data matrix.
measurements_per_day	The number of measurements per day. This variable is used for adding day part dummy variables to aid the imputation.
imputation_iterations	The amount of times the Amelia imputation should be averaged over.

Value

This function returns the modified matrix.

Examples

# create a matrix with some missing values
data_matrix <- matrix(nrow = 40, ncol = 3)
data_matrix[, ] <- runif(ncol(data_matrix) * nrow(data_matrix), 1, nrow(data_matrix))
while (sum(is.na(data_matrix)) == 0)
  data_matrix[as.logical(round(runif(ncol(data_matrix) * nrow(data_matrix), -0.3, 0.7)))] <- NA
colnames(data_matrix) <- c('rumination', 'happiness', 'activity')
data_matrix
#>       rumination happiness  activity
#>  [1,]  15.074074 29.950721        NA
#>  [2,]  26.447892        NA 21.075038
#>  [3,]         NA 25.747227 39.280132
#>  [4,]  27.551146  6.328515 14.813282
#>  [5,]  34.512150 37.279106 18.777766
#>  [6,]  33.706570 26.393627  9.580964
#>  [7,]         NA 21.619662        NA
#>  [8,]  10.270212  9.779500 15.400207
#>  [9,]  23.566603 39.347408  5.939997
#> [10,]  36.018198  5.252903 29.855028
#> [11,]         NA  3.727875 38.278785
#> [12,]  30.673471 31.328175 27.078306
#> [13,]  29.513042 26.674567 19.255485
#> [14,]  34.244863 11.108907        NA
#> [15,]  39.056904        NA        NA
#> [16,]   5.408853  6.799704 25.518602
#> [17,]         NA 25.986539 18.649546
#> [18,]  26.275228  8.848299 35.632592
#> [19,]   3.186849 30.001231 31.583871
#> [20,]         NA        NA  6.384322
#> [21,]  12.490376 34.291799 11.971209
#> [22,]  24.801685 32.333090 28.570913
#> [23,]   5.723246        NA 18.245290
#> [24,]  25.547244 32.019731 27.430242
#> [25,]  28.766257        NA 28.327884
#> [26,]  24.871229 24.401456  3.667850
#> [27,]   2.342740 17.878738 38.381637
#> [28,]  26.969741 36.890756        NA
#> [29,]  26.465724 25.024563        NA
#> [30,]         NA 17.993008 20.607136
#> [31,]         NA        NA 37.485169
#> [32,]  27.387821 30.991527 18.113186
#> [33,]  35.356391  5.996515 14.668975
#> [34,]  29.517937 22.006692 19.583659
#> [35,]  16.546173        NA 38.680221
#> [36,]  17.790305        NA 12.589315
#> [37,]   6.670887 22.207915        NA
#> [38,]  18.593693 10.429268 34.529168
#> [39,]         NA 32.322620  8.539194
#> [40,]   3.746268 37.968635        NA
autovarCore:::impute_datamatrix(data_matrix, 1, 100)
#>       rumination happiness  activity
#>  [1,]  15.074074 29.950721 21.532391
#>  [2,]  26.447892 24.294730 21.075038
#>  [3,]  21.630919 25.747227 39.280132
#>  [4,]  27.551146  6.328515 14.813282
#>  [5,]  34.512150 37.279106 18.777766
#>  [6,]  33.706570 26.393627  9.580964
#>  [7,]  22.460247 21.619662 25.847934
#>  [8,]  10.270212  9.779500 15.400207
#>  [9,]  23.566603 39.347408  5.939997
#> [10,]  36.018198  5.252903 29.855028
#> [11,]  21.941243  3.727875 38.278785
#> [12,]  30.673471 31.328175 27.078306
#> [13,]  29.513042 26.674567 19.255485
#> [14,]  34.244863 11.108907 24.702607
#> [15,]  39.056904 16.928420 25.022153
#> [16,]   5.408853  6.799704 25.518602
#> [17,]  24.650743 25.986539 18.649546
#> [18,]  26.275228  8.848299 35.632592
#> [19,]   3.186849 30.001231 31.583871
#> [20,]  27.779351 30.361221  6.384322
#> [21,]  12.490376 34.291799 11.971209
#> [22,]  24.801685 32.333090 28.570913
#> [23,]   5.723246 21.408631 18.245290
#> [24,]  25.547244 32.019731 27.430242
#> [25,]  28.766257 15.463104 28.327884
#> [26,]  24.871229 24.401456  3.667850
#> [27,]   2.342740 17.878738 38.381637
#> [28,]  26.969741 36.890756 14.036992
#> [29,]  26.465724 25.024563 18.989008
#> [30,]  21.370947 17.993008 20.607136
#> [31,]  16.383332 19.631505 37.485169
#> [32,]  27.387821 30.991527 18.113186
#> [33,]  35.356391  5.996515 14.668975
#> [34,]  29.517937 22.006692 19.583659
#> [35,]  16.546173 14.973198 38.680221
#> [36,]  17.790305 27.466426 12.589315
#> [37,]   6.670887 22.207915 24.961803
#> [38,]  18.593693 10.429268 34.529168
#> [39,]  18.652749 32.322620  8.539194
#> [40,]   3.746268 37.968635 20.856719