impute_mice() imputes missing values in a dataset using the MICE algorithm.
It allows the user to exclude certain columns from imputation and can also display the
percentage of missing values in each column before imputation. The user can also specify
the number of imputed datasets to create, the maximum number of iterations, and the imputation
method to use.
Usage
impute_mice(
olink_data,
wide = TRUE,
m = 5,
maxit = 5,
method = "pmm",
exclude_cols = c("DAid", "Disease"),
show_na_percentage = TRUE
)Arguments
- olink_data
The input dataset.
- wide
If TRUE, the data is in wide format.
- m
The number of imputed datasets to create. Default is 5.
- maxit
The maximum number of iterations. Default is 5.
- method
The imputation method to use. Type
methods(mice::mice)afterlibrary(mice)for all options. Default is "pmm".- exclude_cols
The columns to exclude from imputation.
- show_na_percentage
If TRUE, the percentage of missing values in each column is displayed.
Examples
# Data before imputation
test_data <- example_data |>
dplyr::select(DAid, Assay, NPX) |>
tidyr::pivot_wider(names_from = "Assay", values_from = "NPX") |>
dplyr::slice_head(n = 100)
test_data
#> # A tibble: 100 × 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 90 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
impute_mice(test_data)
#> # A tibble: 88 × 2
#> column na_percentage
#> <chr> <dbl>
#> 1 ADA2 7
#> 2 ANG 7
#> 3 ANGPTL3 7
#> 4 ANPEP 7
#> 5 AOC3 7
#> 6 APOM 7
#> 7 ART3 7
#> 8 AXL 7
#> 9 ADAMTS8 6
#> 10 AHSP 6
#> # ℹ 78 more rows
#> Warning: Number of logged events: 2645
#> # A tibble: 100 × 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… 3.48 7.25 -2.09 0.989 4.48 0.330 1.37 1.34 4.48 1.52
#> 4 DA00… 3.41 3.38 1.69 0.401 1.52 2.82 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 1.52
#> 7 DA00… 4.92 6.57 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 90 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>, …