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Augment data with information from a gt_dapc object

Source: R/gt_dapc_tidiers.R
augment.gt_dapc.Rd

Augment for gt_dapc 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 ".fittedLD1", ".fittedLD2", 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_dapc'
augment(x, data = NULL, k = NULL, ...)

Arguments

x

A gt_dapc object returned by gt_dapc().

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_dapc() gt_dapc_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 and run DAPC
pca <- gt_pca_partialSVD(lobsters)
populations <- as.factor(lobsters$population)
dapc_res <- gt_dapc(pca, n_pca = 6, n_da = 2, pop = populations)

# Augment the gen_tibble with the DAPC scores
augment(dapc_res, data = lobsters, k = 2)
#> # A gen_tibble: 79 loci
#> # A tibble:     176 × 6
#>    id    population  genotypes .rownames .fittedLD1 .fittedLD2
#>    <chr> <chr>      <vctr_SNP> <chr>          <dbl>      <dbl>
#>  1 Ale04 Ale                 1 Ale04           3.87      0.132
#>  2 Ale05 Ale                 2 Ale05           3.96     -0.402
#>  3 Ale06 Ale                 3 Ale06           3.25     -0.801
#>  4 Ale08 Ale                 4 Ale08           3.06      0.398
#>  5 Ale13 Ale                 5 Ale13           1.60      1.05 
#>  6 Ale15 Ale                 6 Ale15           2.80     -0.499
#>  7 Ale16 Ale                 7 Ale16           4.85     -0.194
#>  8 Ale17 Ale                 8 Ale17           4.66      0.420
#>  9 Ale18 Ale                 9 Ale18           4.71      1.00 
#> 10 Ale19 Ale                10 Ale19           3.62     -1.37 
#> # ℹ 166 more rows