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Augment data with information from a gt_pca object

Source: R/gt_pca_tidiers.R
augment_gt_pca.Rd

Augment for gt_pca accepts a model object and a dataset and adds scores to each observation in the dataset. Scores for each component are stored in a separate column, which is given name with the pattern ".fittedPC1", ".fittedPC2", etc. For consistency with broom::augment.prcomp, a column ".rownames" is also returned; it is a copy of 'id', but it ensures that any scripts written for data augmented with broom::augment.prcomp will work out of the box (this is especially helpful when adapting plotting scripts).

Usage

# S3 method for class 'gt_pca'
augment(x, data = NULL, k = NULL, ...)

Arguments

x

A gt_pca object returned by one of the gt_pca_* functions.

data

the gen_tibble used to run the PCA.

k

the number of components to add

...

Not used. Needed to match generic signature only.

Value

A gen_tibble containing the original data along with additional columns containing each observation's projection into PCA space.

See also

gt_pca_autoSVD() gt_pca_tidiers

Examples

# Create a gen_tibble of lobster genotypes
bed_file <-
  system.file("extdata", "lobster", "lobster.bed", package = "tidypopgen")
lobsters <- gen_tibble(bed_file,
  backingfile = tempfile("lobsters"),
  quiet = TRUE
)

# Remove monomorphic loci and impute
lobsters <- lobsters %>% select_loci_if(loci_maf(genotypes) > 0)
lobsters <- gt_impute_simple(lobsters, method = "mode")

# Create PCA object
pca <- gt_pca_partialSVD(lobsters)

# Augment the gen_tibble with PCA scores
augment(pca, data = lobsters)
#> # A gen_tibble: 79 loci
#> # A tibble:     176 × 14
#>    id    population  genotypes .rownames .fittedPC1 .fittedPC2 .fittedPC3
#>    <chr> <chr>      <vctr_SNP> <chr>          <dbl>      <dbl>      <dbl>
#>  1 Ale04 Ale         [0,.,...] Ale04          3.43      -2.93      1.96  
#>  2 Ale05 Ale         [1,0,...] Ale05          4.50      -0.306    -2.47  
#>  3 Ale06 Ale         [.,0,...] Ale06          3.26      -2.58      3.51  
#>  4 Ale08 Ale         [.,2,...] Ale08          2.47      -2.58      1.23  
#>  5 Ale13 Ale         [0,.,...] Ale13          0.183     -2.49      2.29  
#>  6 Ale15 Ale         [1,0,...] Ale15          2.93      -2.06      2.69  
#>  7 Ale16 Ale         [0,.,...] Ale16          4.59      -3.10      2.60  
#>  8 Ale17 Ale         [1,.,...] Ale17          4.18      -3.34      0.0791
#>  9 Ale18 Ale         [0,0,...] Ale18          2.68      -4.09      0.987 
#> 10 Ale19 Ale         [2,.,...] Ale19          4.83      -0.820     2.91  
#> # ℹ 166 more rows
#> # ℹ 7 more variables: .fittedPC4 <dbl>, .fittedPC5 <dbl>, .fittedPC6 <dbl>,
#> #   .fittedPC7 <dbl>, .fittedPC8 <dbl>, .fittedPC9 <dbl>, .fittedPC10 <dbl>

# Adjust the number of components to add
augment(pca, data = lobsters, k = 2)
#> # A gen_tibble: 79 loci
#> # A tibble:     176 × 6
#>    id    population  genotypes .rownames .fittedPC1 .fittedPC2
#>    <chr> <chr>      <vctr_SNP> <chr>          <dbl>      <dbl>
#>  1 Ale04 Ale         [0,.,...] Ale04          3.43      -2.93 
#>  2 Ale05 Ale         [1,0,...] Ale05          4.50      -0.306
#>  3 Ale06 Ale         [.,0,...] Ale06          3.26      -2.58 
#>  4 Ale08 Ale         [.,2,...] Ale08          2.47      -2.58 
#>  5 Ale13 Ale         [0,.,...] Ale13          0.183     -2.49 
#>  6 Ale15 Ale         [1,0,...] Ale15          2.93      -2.06 
#>  7 Ale16 Ale         [0,.,...] Ale16          4.59      -3.10 
#>  8 Ale17 Ale         [1,.,...] Ale17          4.18      -3.34 
#>  9 Ale18 Ale         [0,0,...] Ale18          2.68      -4.09 
#> 10 Ale19 Ale         [2,.,...] Ale19          4.83      -0.820
#> # ℹ 166 more rows