View source: R/gt_dapc_tidiers.R
augment.gt_dapc | R Documentation |
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).
## S3 method for class 'gt_dapc'
augment(x, data = NULL, k = NULL, ...)
x |
A |
data |
the |
k |
the number of components to add |
... |
Not used. Needed to match generic signature only. |
A gen_tibble containing the original data along with additional columns containing each observation's projection into PCA space.
gt_dapc()
gt_dapc_tidiers
# 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)
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