test_spectra: Test the performance of spectral models
In GoreLab/waves: Vis-NIR Spectral Analysis Wrapper

test_spectra

R Documentation

Test the performance of spectral models

Description

Wrapper that trains models based spectral data to predict reference values and reports model performance statistics

Usage

test_spectra(
  train.data,
  num.iterations,
  test.data = NULL,
  pretreatment = 1,
  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,
  wavelengths = deprecated(),
  preprocessing = deprecated(),
  output.summary = deprecated(),
  rf.variable.importance = deprecated()
)

Arguments

`train.data`	`data.frame` object of spectral data for input into a spectral prediction model. 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`.
`pretreatment`	Number or list of numbers 1:13 corresponding to desired pretreatment method(s): Raw data (default) Standard normal variate (SNV) SNV and first derivative SNV and second derivative First derivative Second derivative Savitzky–Golay filter (SG) SNV and SG Gap-segment derivative (window size = 11) SG and first derivative (window size = 5) SG and first derivative (window size = 11) SG and second derivative (window size = 5) SG and second derivative (window size = 11)
`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`.
`wavelengths`	DEPRECATED `wavelengths` is no longer supported; this information is now inferred from `df` column names
`preprocessing`	DEPRECATED please use `pretreatment` to specify the specific pretreatment(s) to test. For behavior identical to that of `preprocessing = TRUE`, set `pretreatment = 1:13`'.
`output.summary`	DEPRECATED `output.summary = FALSE` is no longer supported; a summary of output is always returned alongside the full performance statistics.
`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'.

Details

Calls pretreat_spectra, format_cv, and train_spectra functions.

Value

list of 5 objects:

'model.list' is a list of trained model objects, one for each pretreatment method specified by the pretreatment argument. Each model is trained with all rows of df.
'summary.model.performance' is a data.frame containing summary statistics across all model training iterations and pretreatments. See below for a description of the summary statistics provided.
'model.performance' is a data.frame containing performance statistics for each iteration of model training separately (see below).
'predictions' is a data.frame containing both reference and predicted values for each test set entry in each iteration of model training.
'importance' is a data.frame containing variable importance results for each wavelength at each iteration of model training. If model.method is not "pls" or "rf", this list item is NULL.

'summary.model.performance' and 'model.performance' data.frames summary statistics include:

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 for PLSR models
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(s)

Jenna Hershberger jmh579@cornell.edu

Examples


library(magrittr)
ikeogu.2017 %>%
  dplyr::rename(reference = DMC.oven,
                unique.id = sample.id) %>%
  dplyr::select(unique.id, reference, dplyr::starts_with("X")) %>%
  na.omit() %>%
  test_spectra(
    train.data = .,
    tune.length = 3,
    num.iterations = 3,
    pretreatment = 1
  )

GoreLab/waves documentation built on April 15, 2024, 3:28 p.m.