R/save_model.R
save_model.RdGiven a set of pretreatment methods, saves the best spectral
prediction model and model statistics to model.save.folder as
model.name.Rds and model.name_stats.csv respectively. If only
one pretreatment method is supplied, results from that method are stored.
save_model(
df,
write.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,
seed = 1,
verbose = TRUE,
save.model = deprecated(),
wavelengths = deprecated(),
autoselect.preprocessing = deprecated(),
preprocessing.method = 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"
If TRUE, the trained model will be saved in .Rds
format to the location specified by model.save.folder. If
FALSE, the best model will be output by the function but will not
save to a file. Default is TRUE.
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)
Path to folder where model will be saved. If not provided, will save to working directory.
Name that model will be saved as in
model.save.folder. Default is "PredictionModel".
Metric used to decide which model is best. Must be either "RMSE" or "Rsquared"
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
Number of training iterations to perform
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.
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 wavelengths is no
longer supported; this information is now inferred from df
column names
DEPRECATED
autoselect.preprocessing = FALSE is no longer supported. If
multiple pretreatment methods are supplied, the best will be automatically
selected as the model to be saved.
DEPRECATED preprocessing.method
has been renamed "pretreatment"
List of model stats (in data.frame) and trained model object.
If the parameter write.model is TRUE, both objects are saved to
model.save.folder. To use the optimally trained model for
predictions, use tuned parameters from $bestTune.
Wrapper that uses pretreat_spectra,
format_cv, and train_spectra functions.
# \donttest{
library(magrittr)
test.model <- 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() %>%
save_model(
df = .,
write.model = FALSE,
pretreatment = 1:13,
model.name = "my_prediction_model",
tune.length = 3,
num.iterations = 3
)
#> Pretreatment initiated.
#> Training models...
#> Working on Raw_data
#> Loading required package: lattice
#> Warning: package ‘pls’ was built under R version 4.2.3
#>
#> Attaching package: ‘pls’
#> The following object is masked from ‘package:caret’:
#>
#> R2
#> The following object is masked from ‘package:stats’:
#>
#> loadings
#> Returning model...
#> Working on SNV
#> Returning model...
#> Working on SNV1D
#> Returning model...
#> Working on SNV2D
#> Returning model...
#> Working on D1
#> Returning model...
#> Working on D2
#> Returning model...
#> Working on SG
#> Returning model...
#> Working on SNVSG
#> Returning model...
#> Working on SGD1
#> Returning model...
#> Working on SG.D1W5
#> Returning model...
#> Working on SG.D1W11
#> Returning model...
#> Working on SG.D2W5
#> Returning model...
#> Working on SG.D2W11
#> Returning model...
#>
#> Training Summary:
#> # A tibble: 13 × 40
#> Pretreatment RMSEp_mean R2p_mean RPD_mean RPIQ_mean CCC_mean Bias_mean
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Raw_data 2.30 0.717 1.86 2.41 0.817 0.299
#> 2 SNV 1.83 0.820 2.34 3.03 0.888 0.190
#> 3 SNV1D 2.93 0.548 1.44 1.87 0.702 0.403
#> 4 SNV2D 4.28 0.0717 0.988 1.28 0.205 0.635
#> 5 D1 2.76 0.591 1.53 1.99 0.738 0.463
#> 6 D2 4.28 0.0728 0.988 1.28 0.206 0.658
#> 7 SG 2.30 0.716 1.86 2.41 0.817 0.299
#> 8 SNVSG 1.80 0.828 2.39 3.09 0.893 0.167
#> 9 SGD1 2.37 0.706 1.81 2.34 0.807 0.370
#> 10 SG.D1W5 2.33 0.721 1.83 2.37 0.814 0.460
#> 11 SG.D1W11 2.35 0.713 1.82 2.35 0.811 0.395
#> 12 SG.D2W5 4.07 0.0941 1.04 1.35 0.225 0.370
#> 13 SG.D2W11 3.15 0.500 1.34 1.74 0.636 0.644
#> # ℹ 33 more variables: SEP_mean <dbl>, RMSEcv_mean <dbl>, R2cv_mean <dbl>,
#> # R2sp_mean <dbl>, best.ncomp_mean <dbl>, best.ntree_mean <dbl>,
#> # best.mtry_mean <dbl>, RMSEp_sd <dbl>, R2p_sd <dbl>, RPD_sd <dbl>,
#> # RPIQ_sd <dbl>, CCC_sd <dbl>, Bias_sd <dbl>, SEP_sd <dbl>, RMSEcv_sd <dbl>,
#> # R2cv_sd <dbl>, R2sp_sd <dbl>, best.ncomp_sd <dbl>, best.ntree_sd <dbl>,
#> # best.mtry_sd <dbl>, RMSEp_mode <dbl>, R2p_mode <dbl>, RPD_mode <dbl>,
#> # RPIQ_mode <dbl>, CCC_mode <dbl>, Bias_mode <dbl>, SEP_mode <dbl>, …
#>
#> Best pretreatment technique: SNVSG
summary(test.model$best.model)
#> Data: X dimension: 120 2141
#> Y dimension: 120 1
#> Fit method: kernelpls
#> Number of components considered: 3
#> TRAINING: % variance explained
#> 1 comps 2 comps 3 comps
#> X 64.48 87.94 91.43
#> reference 33.93 64.97 87.18
test.model$best.model.stats
#> # A tibble: 1 × 40
#> Pretreatment RMSEp_mean R2p_mean RPD_mean RPIQ_mean CCC_mean Bias_mean
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 SNVSG 1.80 0.828 2.39 3.09 0.893 0.167
#> # ℹ 33 more variables: SEP_mean <dbl>, RMSEcv_mean <dbl>, R2cv_mean <dbl>,
#> # R2sp_mean <dbl>, best.ncomp_mean <dbl>, best.ntree_mean <dbl>,
#> # best.mtry_mean <dbl>, RMSEp_sd <dbl>, R2p_sd <dbl>, RPD_sd <dbl>,
#> # RPIQ_sd <dbl>, CCC_sd <dbl>, Bias_sd <dbl>, SEP_sd <dbl>, RMSEcv_sd <dbl>,
#> # R2cv_sd <dbl>, R2sp_sd <dbl>, best.ncomp_sd <dbl>, best.ntree_sd <dbl>,
#> # best.mtry_sd <dbl>, RMSEp_mode <dbl>, R2p_mode <dbl>, RPD_mode <dbl>,
#> # RPIQ_mode <dbl>, CCC_mode <dbl>, Bias_mode <dbl>, SEP_mode <dbl>, …
# }