Impute via k-nearest neighbors

hd_impute_knn() imputes missing values in a dataset using the k-nearest neighbors method. It can also display the percentage of missing values in each column before imputation. The user can also specify the number of neighbors to consider for imputation.

Usage

hd_impute_knn(dat, k = 5, verbose = TRUE)

Arguments

dat

An HDAnalyzeR object or a dataset in wide format and sample ID as its first column.

k

The number of neighbors to consider for imputation. Default is 5.

verbose

If TRUE, the percentage of missing values in each column is displayed.

Value

The imputed dataset.

Examples

# Create the HDAnalyzeR object providing the data and metadata
hd_object <- hd_initialize(example_data, example_metadata)
hd_object$data
#> # A tibble: 586 × 101
#>    DAid    AARSD1   ABL1  ACAA1    ACAN    ACE2  ACOX1   ACP5    ACP6  ACTA2
#>    <chr>    <dbl>  <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl>  <dbl>
#>  1 DA00001   3.39  2.76   1.71   0.0333  1.76   -0.919 1.54    2.15    2.81 
#>  2 DA00002   1.42  1.25  -0.816 -0.459   0.826  -0.902 0.647   1.30    0.798
#>  3 DA00003  NA    NA     NA      0.989  NA       0.330 1.37   NA      NA    
#>  4 DA00004   3.41  3.38   1.69  NA       1.52   NA     0.841   0.582   1.70 
#>  5 DA00005   5.01  5.05   0.128  0.401  -0.933  -0.584 0.0265  1.16    2.73 
#>  6 DA00006   6.83  1.18  -1.74  -0.156   1.53   -0.721 0.620   0.527   0.772
#>  7 DA00007  NA    NA      3.96   0.682   3.14    2.62  1.47    2.25    2.01 
#>  8 DA00008   2.78  0.812 -0.552  0.982  -0.101  -0.304 0.376  -0.826   1.52 
#>  9 DA00009   4.39  3.34  -0.452 -0.868   0.395   1.71  1.49   -0.0285  0.200
#> 10 DA00010   1.83  1.21  -0.912 -1.04   -0.0918 -0.304 1.69    0.0920  2.04 
#> # ℹ 576 more rows
#> # ℹ 91 more variables: ACTN4 <dbl>, ACY1 <dbl>, ADA <dbl>, ADA2 <dbl>,
#> #   ADAM15 <dbl>, ADAM23 <dbl>, ADAM8 <dbl>, ADAMTS13 <dbl>, ADAMTS15 <dbl>,
#> #   ADAMTS16 <dbl>, ADAMTS8 <dbl>, ADCYAP1R1 <dbl>, ADGRE2 <dbl>, ADGRE5 <dbl>,
#> #   ADGRG1 <dbl>, ADGRG2 <dbl>, ADH4 <dbl>, ADM <dbl>, AGER <dbl>, AGR2 <dbl>,
#> #   AGR3 <dbl>, AGRN <dbl>, AGRP <dbl>, AGXT <dbl>, AHCY <dbl>, AHSP <dbl>,
#> #   AIF1 <dbl>, AIFM1 <dbl>, AK1 <dbl>, AKR1B1 <dbl>, AKR1C4 <dbl>, …

# Data after imputation
res <- hd_impute_knn(hd_object, k = 3)
#> # A tibble: 91 × 2
#>    Variable NA_percentage
#>    <chr>            <dbl>
#>  1 AARSD1            5.80
#>  2 ABL1              5.80
#>  3 ACAA1             5.29
#>  4 ACAN              3.92
#>  5 ACE2              6.14
#>  6 ACOX1             3.92
#>  7 ACP6              2.22
#>  8 ACTA2             6.14
#>  9 ACTN4             6.14
#> 10 ACY1              3.92
#> # ℹ 81 more rows
res$data
#> # A tibble: 586 × 101
#>    DAid  AARSD1  ABL1  ACAA1    ACAN    ACE2  ACOX1   ACP5    ACP6 ACTA2   ACTN4
#>    <chr>  <dbl> <dbl>  <dbl>   <dbl>   <dbl>  <dbl>  <dbl>   <dbl> <dbl>   <dbl>
#>  1 DA00…   3.39 2.76   1.71   0.0333  1.76   -0.919 1.54    2.15   2.81   0.742 
#>  2 DA00…   1.42 1.25  -0.816 -0.459   0.826  -0.902 0.647   1.30   0.798 -0.0659
#>  3 DA00…   3.80 2.82   2.35   0.989  -0.0218  0.330 1.37    0.561  1.34   0.737 
#>  4 DA00…   3.41 3.38   1.69   0.411   1.52    1.38  0.841   0.582  1.70   0.108 
#>  5 DA00…   5.01 5.05   0.128  0.401  -0.933  -0.584 0.0265  1.16   2.73   0.350 
#>  6 DA00…   6.83 1.18  -1.74  -0.156   1.53   -0.721 0.620   0.527  0.772  0.229 
#>  7 DA00…   3.44 4.75   3.96   0.682   3.14    2.62  1.47    2.25   2.01   0.170 
#>  8 DA00…   2.78 0.812 -0.552  0.982  -0.101  -0.304 0.376  -0.826  1.52  -0.597 
#>  9 DA00…   4.39 3.34  -0.452 -0.868   0.395   1.71  1.49   -0.0285 0.200 -0.532 
#> 10 DA00…   1.83 1.21  -0.912 -1.04   -0.0918 -0.304 1.69    0.0920 2.04   0.501 
#> # ℹ 576 more rows
#> # ℹ 90 more variables: ACY1 <dbl>, ADA <dbl>, ADA2 <dbl>, ADAM15 <dbl>,
#> #   ADAM23 <dbl>, ADAM8 <dbl>, ADAMTS13 <dbl>, ADAMTS15 <dbl>, ADAMTS16 <dbl>,
#> #   ADAMTS8 <dbl>, ADCYAP1R1 <dbl>, ADGRE2 <dbl>, ADGRE5 <dbl>, ADGRG1 <dbl>,
#> #   ADGRG2 <dbl>, ADH4 <dbl>, ADM <dbl>, AGER <dbl>, AGR2 <dbl>, AGR3 <dbl>,
#> #   AGRN <dbl>, AGRP <dbl>, AGXT <dbl>, AHCY <dbl>, AHSP <dbl>, AIF1 <dbl>,
#> #   AIFM1 <dbl>, AK1 <dbl>, AKR1B1 <dbl>, AKR1C4 <dbl>, AKT1S1 <dbl>, …