hd_split_data() splits the data into training and test sets based on the ratio
provided. It also stratifies the data based on the variable of interest if any.
At this stage the user can select metadata variable predictors to be included in
the training and test sets.
Usage
hd_split_data(
dat,
metadata = NULL,
variable = "Disease",
metadata_cols = NULL,
ratio = 0.75,
seed = 123
)Arguments
- dat
An HDAnalyzeR object or a dataset in wide format and sample ID as its first column.
- metadata
A dataset containing the metadata information with the sample ID as the first column. If a HDAnalyzeR object is provided, this parameter is not needed.
- variable
The name of the metadata variable containing the case and control groups. Default is "Disease".
- metadata_cols
The metadata variables to be selected from the metadata as predictors. Default is NULL.
- ratio
The ratio of training data to test data. Default is 0.75.
- seed
Seed for reproducibility. Default is 123.
Details
It is always recommended to split the data into training and test sets to avoid
overfitting. This function also initializes the model object to be used in the
downstream machine learning pipeline. The user can create their own model object
with the train and test data splits, but it must be a list with the train set as
the first and the test set as the second element. The function utilizes the
initial_split() function from the rsample package to split the data. For more
information on the rsample package, please check their documentation.
Examples
# Initialize an HDAnalyzeR object
hd_object <- hd_initialize(example_data, example_metadata)
# Split the data into training and test sets
hd_split_data(hd_object, variable = "Disease")
#> Warning: Too little data to stratify.
#> • Resampling will be unstratified.
#> $train_data
#> # A tibble: 439 × 102
#> DAid Disease AARSD1 ABL1 ACAA1 ACAN ACE2 ACOX1 ACP5 ACP6
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 DA00003 AML NA NA NA 0.989 NA 0.330 1.37 NA
#> 2 DA00004 AML 3.41 3.38 1.69 NA 1.52 NA 0.841 0.582
#> 3 DA00005 AML 5.01 5.05 0.128 0.401 -0.933 -0.584 0.0265 1.16
#> 4 DA00006 AML 6.83 1.18 -1.74 -0.156 1.53 -0.721 0.620 0.527
#> 5 DA00007 AML NA NA 3.96 0.682 3.14 2.62 1.47 2.25
#> 6 DA00008 AML 2.78 0.812 -0.552 0.982 -0.101 -0.304 0.376 -0.826
#> 7 DA00010 AML 1.83 1.21 -0.912 -1.04 -0.0918 -0.304 1.69 0.0920
#> 8 DA00011 AML 3.48 4.96 3.50 -0.338 4.48 1.26 2.18 1.62
#> 9 DA00012 AML 4.31 0.710 -1.44 -0.218 -0.469 -0.361 -0.0714 -1.30
#> 10 DA00013 AML 1.31 2.52 1.11 0.997 4.56 -1.35 0.833 2.33
#> # ℹ 429 more rows
#> # ℹ 92 more variables: ACTA2 <dbl>, 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>, …
#>
#> $test_data
#> # A tibble: 147 × 102
#> DAid Disease AARSD1 ABL1 ACAA1 ACAN ACE2 ACOX1 ACP5 ACP6 ACTA2
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 DA000… AML 3.39 2.76 1.71 0.0333 1.76 -0.919 1.54 2.15 2.81
#> 2 DA000… AML 1.42 1.25 -0.816 -0.459 0.826 -0.902 0.647 1.30 0.798
#> 3 DA000… AML 4.39 3.34 -0.452 -0.868 0.395 1.71 1.49 -0.0285 0.200
#> 4 DA000… AML 3.31 1.90 NA -0.926 0.408 0.687 1.03 0.612 2.19
#> 5 DA000… AML 1.46 0.832 -2.73 -0.371 2.27 0.0234 0.144 0.826 1.98
#> 6 DA000… AML 2.62 2.48 0.537 -0.215 1.82 0.290 1.27 1.11 0.206
#> 7 DA000… AML 2.47 2.16 -0.486 NA 0.386 NA 1.38 0.536 1.86
#> 8 DA000… AML 3.62 3.06 -1.34 0.965 1.05 1.53 0.152 -0.124 2.81
#> 9 DA000… AML 4.39 3.31 0.454 0.290 2.68 0.116 -1.32 0.945 2.14
#> 10 DA000… AML 0.964 2.94 1.55 1.67 2.50 0.164 1.83 1.46 3.03
#> # ℹ 137 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>, …
#>
#> attr(,"class")
#> [1] "hd_model"