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Train a model based predict reference values with spectral data

Source: R/train_spectra.R
train_spectra.Rd

Trains spectral prediction models using one of several algorithms and sampling procedures.

Usage

train_spectra(
  df,
  num.iterations,
  test.data = NULL,
  k.folds = 5,
  proportion.train = 0.7,
  tune.length = 50,
  model.method = "pls",
  best.model.metric = "RMSE",
  stratified.sampling = TRUE,
  cv.scheme = NULL,
  trial1 = NULL,
  trial2 = NULL,
  trial3 = NULL,
  split.test = FALSE,
  seed = 1,
  verbose = TRUE,
  save.model = lifecycle::deprecated(),
  rf.variable.importance = lifecycle::deprecated(),
  output.summary = lifecycle::deprecated(),
  return.model = lifecycle::deprecated()
)

Arguments

df

data.frame object. First column contains unique identifiers, second contains reference values, followed by spectral columns. Include no other columns to right of spectra! Column names of spectra must start with "X" and reference column must be named "reference"

num.iterations

Number of training iterations to perform

test.data

data.frame with same specifications as df. Use if specific test set is desired for hyperparameter tuning. If NULL, function will automatically train with a stratified sample of 70%. Default is NULL.

k.folds

Number indicating the number of folds for k-fold cross-validation during model training. Default is 5.

proportion.train

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

tune.length

Number delineating search space for tuning of the PLSR hyperparameter ncomp. Must be set to 5 when using the random forest algorithm (model.method == rf). Default is 50.

model.method

Model type to use for training. Valid options include:

  • "pls": Partial least squares regression (Default)

  • "rf": Random forest

  • "svmLinear": Support vector machine with linear kernel

  • "svmRadial": Support vector machine with radial kernel

best.model.metric

Metric used to decide which model is best. Must be either "RMSE" or "Rsquared"

stratified.sampling

If TRUE, training and test sets will be selected using stratified random sampling. This term is only used if test.data == NULL. Default is TRUE.

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

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.

split.test

boolean that allows for a fixed training set and a split test set. Example// train model on data from two breeding programs and a stratified subset (70%) of a third and test on the remaining samples (30%) of the third. If FALSE, the entire provided test set test.data will remain as a testing set or if none is provided, 30% of the provided train.data will be used for testing. Default is FALSE.

seed

Integer to be used internally as input for set.seed(). Only used if stratified.sampling = TRUE. In all other cases, seed is set to the current iteration number. Default is 1.

verbose

If TRUE, the number of rows removed through filtering will be printed to the console. Default is TRUE.

save.model

DEPRECATED save.model = FALSE is no longer supported; this function will always return a saved model.

rf.variable.importance

DEPRECATED rf.variable.importance = FALSE is no longer supported; variable importance results are always returned if the model.method is set to `pls` or `rf`.

output.summary

DEPRECATED output.summary = FALSE is no longer supported; a summary of output is always returned alongside the full performance statistics.

return.model

DEPRECATED return.model = FALSE is no longer supported; a trained model object is always returned alongside the full performance statistics and summary.

Value

list of the following:

  1. model is a model object trained with all rows of df.

  2. summary.model.performance is a data.frame with model performance statistics in summary format (2 rows, one with mean and one with standard deviation of all training iterations).

  3. full.model.performance is a data.frame with model performance statistics in long format (number of rows = num.iterations)

  4. predictions is a data.frame containing predicted values for each test set entry at each iteration of model training.

  5. importance is a data.frame that contains variable importance for each wavelength. Only available for model.method options "rf" and "pls".

Included summary statistics:

  • Tuned parameters depending on the model algorithm:

    • Best.n.comp, the best number of components

    • Best.ntree, the best number of trees in an RF model

    • Best.mtry, the best number of variables to include at every decision point in an RF model

  • RMSECV, the root mean squared error of cross-validation

  • R2cv, the coefficient of multiple determination of cross-validation (k-fold CV for pls/svm; OOB for rf)

  • RMSEP, the root mean squared error of prediction

  • R2p, the squared Pearson’s correlation between predicted and observed test set values

  • RPD, the ratio of standard deviation of observed test set values to RMSEP

  • RPIQ, the ratio of performance to interquartile difference

  • CCC, the concordance correlation coefficient

  • Bias, the average difference between the predicted and observed values

  • SEP, the standard error of prediction

  • R2sp, the squared Spearman’s rank correlation between predicted and observed test set values

Author

Jenna Hershberger jmh579@cornell.edu

Examples

# \donttest{
library(magrittr)
ikeogu.2017 %>%
  dplyr::filter(study.name == "C16Mcal") %>%
  dplyr::rename(reference = DMC.oven,
                unique.id = sample.id) %>%
  dplyr::select(unique.id, reference, dplyr::starts_with("X")) %>%
  na.omit() %>%
  train_spectra(
    df = .,
    tune.length = 3,
    num.iterations = 3,
    best.model.metric = "RMSE",
    stratified.sampling = TRUE
  ) %>%
  summary()
#> Returning model...
#>                           Length Class      Mode
#> model                     22     mvr        list
#> summary.model.performance 15     data.frame list
#> model.performance         15     data.frame list
#> predictions                5     data.frame list
#> importance                 4     data.frame list
# }