R/train_spectra.R
train_spectra.RdTrains spectral prediction models using one of several algorithms and sampling procedures.
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 = deprecated(),
rf.variable.importance = deprecated(),
output.summary = deprecated(),
return.model = deprecated()
)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"
Number of training iterations to perform
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.
Number indicating the number of folds for k-fold cross-validation during model training. Default is 5.
Fraction of samples to include in the training set. Default is 0.7.
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 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
Metric used to decide which model is best. Must be either "RMSE" or "Rsquared"
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.
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
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".
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.
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.
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.
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.
If TRUE, the number of rows removed through filtering
will be printed to the console. Default is TRUE.
DEPRECATED save.model = FALSE is no
longer supported; this function will always return a saved model.
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`.
DEPRECATED output.summary = FALSE
is no longer supported; a summary of output is always returned alongside
the full performance statistics.
DEPRECATED return.model = FALSE
is no longer supported; a trained model object is always returned
alongside the full performance statistics and summary.
list of the following:
model is a model object trained with all rows of df.
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).
full.model.performance is a data.frame with model
performance statistics in long format
(number of rows = num.iterations)
predictions is a data.frame containing predicted values
for each test set entry at each iteration of model training.
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 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
# \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 20 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
# }