stacking_reg_1
(stacking of SVM models) regression model.R/stacking_reg_1.R
stacking_reg_1.Rd
The function processes a split object (training + test sets), according to
the configuration set by the user. For instance, genomic information is
incorporated according to the option set by the user. A list of specific
environmental covariables to use can be provided.
Multiple recipes are created using the package recipes
according to the
data source (genomic, environmental)
These recipes specify additional preprocessing steps, such as standardization
based on the training set, with same transformations used on the test set.
Variables with null variance are removed. If year effect is included, it is
converted to dummy variables.
Each recipe (with G and E features) will be
subsequently fitted with a support vector regression model and predictions
from each model will be combined (see function
fit_cv_split.stacking_reg_1()
).
new_stacking_reg_1( split = NULL, trait = NULL, geno = NULL, env_predictors = NULL, info_environments = NULL, use_selected_markers = F, SNPs = NULL, include_env_predictors = T, list_env_predictors = NULL, lat_lon_included = F, year_included = F, ... ) stacking_reg_1( split, trait, geno, env_predictors, info_environments, use_selected_markers, SNPs, list_env_predictors, include_env_predictors, lat_lon_included, year_included, ... ) validate_stacking_reg_1(x, ...)
split | an object of class |
---|---|
trait |
|
geno |
|
env_predictors |
|
info_environments |
|
use_selected_markers | A |
SNPs | A |
include_env_predictors | A |
list_env_predictors | A |
lat_lon_included |
|
year_included |
|
A list
object of class stacking_reg_1
with the following items:
data.frame
Training set after partial processing
data.frame
Test set after partial processing
A recipe
object, specifying the remaining processing
steps which are implemented when a model is fitted on the training set
with a recipe. Data used are predictors corresponding to genomic data.
A recipe
object, specifying the remaining processing
steps which are implemented when a model is fitted on the training set
with a recipe. Data used are predictors corresponding to enviornmental
predictors.
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, Fran攼㸷ois R, Grolemund G, Hayes A, Henry L, Hester J, others (2019). “Welcome to the Tidyverse.” Journal of open source software, 4(43), 1686. Kuhn M, Wickham H (2020). Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles.. https://www.tidymodels.org.