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Format multiple trials with or without overlapping genotypes into training and test sets according to user-provided cross validation scheme

Source: R/format_cv.R
format_cv.Rd

Standalone function that is also used within train_spectra to divide trials or studies into training and test sets based on overlap in trial environments and genotype entries

Usage

format_cv(
  trial1,
  trial2,
  trial3 = NULL,
  cv.scheme,
  stratified.sampling = TRUE,
  proportion.train = 0.7,
  seed = NULL,
  remove.genotype = FALSE
)

Arguments

trial1

data.frame object that is for use only when cv.scheme is provided. Contains the trial to be tested in subsequent model training functions. The first column contains unique identifiers, second contains genotypes, third contains reference values, followed by spectral columns. Include no other columns to right of spectra! Column names of spectra must start with "X", reference column must be named "reference", and genotype column must be named "genotype".

trial2

data.frame object that is for use only when cv.scheme is provided. This data.frame contains a trial that has overlapping genotypes with trial1 but that were grown in a different site/year (different environment). Formatting must be consistent with trial1.

trial3

data.frame object that is for use only when cv.scheme is provided. This data.frame contains a trial that may or may not contain genotypes that overlap with trial1. Formatting must be consistent with trial1.

cv.scheme

A cross validation (CV) scheme from Jarquín et al., 2017. Options for cv.scheme include:

  • "CV1": untested lines in tested environments

  • "CV2": tested lines in tested environments

  • "CV0": tested lines in untested environments

  • "CV00": untested lines in untested environments

stratified.sampling

If TRUE, training and test sets will be selected using stratified random sampling. Default is TRUE.

proportion.train

Fraction of samples to include in the training set. Default is 0.7.

seed

Number used in the function set.seed() for reproducible randomization. If NULL, no seed is set. Default is NULL.

remove.genotype

boolean that, if TRUE, removes the "genotype" column is removed from the output data.frame. Default is FALSE.

Value

List of data.frames ($train.set, $test.set) compiled according to user-provided cross validation scheme.

Details

Use of a cross-validation scheme requires a column in the input data.frame named "genotype" to ensure proper sorting of training and test sets. Variables trial1 and trial2 are required, while trial 3 is optional.

References

Jarquín, D., C. Lemes da Silva, R. C. Gaynor, J. Poland, A. Fritz, R. Howard, S. Battenfield, and J. Crossa. 2017. Increasing genomic-enabled prediction accuracy by modeling genotype × environment interactions in Kansas wheat. Plant Genome 10(2):1-15. <doi:10.3835/plantgenome2016.12.0130>

Author

Jenna Hershberger jmh579@cornell.edu

Examples

# Must have a column called "genotype", so we'll create a fake one for now
# We will use CV00, which does not require any overlap in genotypes
# In real scenarios, CV schemes that rely on genotypes should not be applied
# when genotypes are unknown, as in this case.
library(magrittr)
trials <- ikeogu.2017 %>%
  dplyr::mutate(genotype = 1:nrow(ikeogu.2017)) %>% # fake for this example
  dplyr::rename(reference = DMC.oven) %>%
  dplyr::select(
    study.name, sample.id, genotype, reference,
    tidyselect::starts_with("X")
  )
trial1 <- trials %>%
  dplyr::filter(study.name == "C16Mcal") %>%
  dplyr::select(-study.name)
trial2 <- trials %>%
  dplyr::filter(study.name == "C16Mval") %>%
  dplyr::select(-study.name)
cv.list <- format_cv(
  trial1 = trial1, trial2 = trial2, cv.scheme = "CV00",
  stratified.sampling = FALSE, remove.genotype = TRUE
)
cv.list$train.set[1:5, 1:5]
#> # A tibble: 5 × 5
#>   sample.id reference  X350  X351  X352
#>   <chr>         <dbl> <dbl> <dbl> <dbl>
#> 1 C16Mval_1      44.1 0.497 0.474 0.489
#> 2 C16Mval_2      43.7 0.546 0.556 0.557
#> 3 C16Mval_3      43.4 0.513 0.521 0.529
#> 4 C16Mval_4      43.1 0.565 0.570 0.583
#> 5 C16Mval_5      41.8 0.497 0.502 0.510
cv.list$test.set[1:5, 1:5]
#> # A tibble: 5 × 5
#>   sample.id  reference  X350  X351  X352
#>   <chr>          <dbl> <dbl> <dbl> <dbl>
#> 1 C16Mcal_12      42.0 0.551 0.548 0.531
#> 2 C16Mcal_13      35.4 0.491 0.496 0.497
#> 3 C16Mcal_14      42.2 0.519 0.514 0.520
#> 4 C16Mcal_15      31.6 0.652 0.649 0.650
#> 5 C16Mcal_16      36.4 0.517 0.514 0.530