rename | R Documentation |
‘r lifecycle::badge(’deprecated')'
waves 0.2.0 renamed a number of functions to ensure that every function name adheres to the tidyverse style guide.
* 'AggregateSpectra()' -> 'aggregate_spectra()' * 'DoPreprocessing()' -> 'pretreat_spectra()' * 'FilterSpectra()' -> 'filter_spectra()' * 'FormatCV()' -> 'format_cv()' * 'PlotSpectra()' -> 'plot_spectra()' * 'PredictFromSavedModel()' -> 'predict_spectra()' * 'SaveModel()' -> 'save_model()' * 'TestModelPerformance()' -> 'test_spectra()' * 'TrainSpectralModel()' -> 'train_spectra()'
AggregateSpectra(
df,
grouping.colnames = c("unique.id"),
reference.value.colname = "reference",
agg.function = "mean"
)
DoPreprocessing(df, test.data = NULL, pretreatment = 1)
FilterSpectra(
df,
filter = TRUE,
return.distances = FALSE,
num.col.before.spectra = 4,
window.size = 10,
verbose = TRUE
)
FormatCV(
trial1,
trial2,
trial3 = NULL,
cv.scheme,
stratified.sampling = TRUE,
proportion.train = 0.7,
seed = NULL,
remove.genotype = FALSE
)
PlotSpectra(
df,
num.col.before.spectra = 1,
window.size = 10,
detect.outliers = TRUE,
color = NULL,
alternate.title = NULL,
verbose = TRUE
)
PredictFromSavedModel(
input.data,
model.stats.location,
model.location,
model.method = "pls"
)
SaveModel(
df,
save.model = TRUE,
pretreatment = 1,
model.save.folder = NULL,
model.name = "PredictionModel",
best.model.metric = "RMSE",
k.folds = 5,
proportion.train = 0.7,
tune.length = 50,
model.method = "pls",
num.iterations = 10,
stratified.sampling = TRUE,
cv.scheme = NULL,
trial1 = NULL,
trial2 = NULL,
trial3 = NULL,
verbose = TRUE
)
TestModelPerformance(
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,
verbose = TRUE
)
TrainSpectralModel(
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,
verbose = TRUE
)
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