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_pkgdown.yml
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_pkgdown.yml
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reference:
- title: "Step 1: create METData object"
desc: "Specify input data and processing parameters (automatic retrieval of external weather data; QC on raw weather data, if provided...)"
contents:
- subtitle: "Main function"
contents:
- create_METData
- subtitle: "Get daily weather data for an environment based on geographical coordinates"
contents:
- get_daily_tables_per_env
- daylength
- sat_vap_pressure
- get.ea
- get.ea.with.rhmean
- get.ea.no.RH
- get.es
- get.esmn
- get.esmx
- get_soil_per_env
- penman_monteith_reference_et0
- subtitle: "Check daily weather data (non-exhaustive quality control) provided by user"
contents:
- qc_raw_weather_data
- subtitle: "Compute environmental covariates based on raw daily weather data"
contents:
- get_ECs
- compute_EC_fixed_length_window
- compute_EC_fixed_number_windows
- compute_EC_user_defined_intervals
- compute_EC_gdd
- gdd_information
- get_solar_radiation
- get_wind_data
- get_elevation
- subtitle: "Clustering of environments based on weather data from the complete training dataset"
contents:
- clustering_env_data
- subtitle: "Overview of the METData object created"
contents:
- summary.METData
- print.summary.METData
- title: "Step 2: cross-validated model evaluation of the METData"
desc: "Evaluate predictive ability of a machine learning-based model with a specific CV scheme"
contents:
- subtitle: "Main function"
contents:
- predict_trait_MET_cv
- subtitle: "Create train/test splits to address typical prediction problems for MET datasets"
contents:
- predict_cv0
- predict_cv00
- predict_cv1
- predict_cv2
- subtitle: 'Processing of genotypic data for ML-based predictions'
contents:
- apply_pca
- apply_pcs_G_Add
- select_markers
- marker_effect_per_env_EN
- marker_effect_per_env_FarmCPU
- subtitle: 'ML-methods implemented: processing functions according to the method'
contents:
- get_splits_processed_with_method
- stacking_reg_1
- stacking_reg_2
- stacking_reg_3
- xgb_reg_1
- xgb_reg_2
- xgb_reg_3
- DL_reg_1
- DL_reg_2
- DL_reg_3
- rf_reg_1
- rf_reg_2
- rf_reg_3
- subtitle: 'ML-methods implemented: fitting functions according to the method'
contents:
- fit_cv_split
- fit_cv_split.stacking_reg_1
- fit_cv_split.stacking_reg_2
- fit_cv_split.stacking_reg_3
- fit_cv_split.xgb_reg_1
- fit_cv_split.xgb_reg_2
- fit_cv_split.xgb_reg_3
- fit_cv_split.DL_reg_1
- fit_cv_split.DL_reg_2
- fit_cv_split.DL_reg_3
- fit_cv_split.rf_reg_1
- fit_cv_split.rf_reg_2
- fit_cv_split.rf_reg_3
- subtitle: 'Compute variable importance (model-specific and model-free, e.g. permutation-based methods)'
contents:
- variable_importance_split
- subtitle: 'Plot cross-validated results for predictive ability'
contents:
- plot_results_cv
- subtitle: 'Plot variable importance results'
contents:
- plot_results_vip_cv
- title: "Step 3: Create a table of new phenotypes to predict (i.e. for a set of given genotypes in a given environment)"
contents:
- subtitle: "Main function"
contents:
- create_METData
- subtitle: "Get daily weather data for an environment based on geographical coordinates"
contents:
- get_daily_tables_per_env
- daylength
- get.ea
- get.ea.with.rhmean
- get.ea.no.RH
- get.ea.with.rhmax
- get.es
- get.esmn
- get.esmx
- subtitle: "Check daily weather data (non-exhaustive quality control) provided by user"
contents:
- qc_raw_weather_data
- subtitle: "Compute environmental covariates based on raw daily weather data"
contents:
- get_ECs
- compute_EC_fixed_length_window
- compute_EC_fixed_number_windows
- compute_EC_gdd
- gdd_information
- get_solar_radiation
- get_wind_data
- subtitle: "Clustering of environments based on weather data from the complete training dataset"
contents:
- clustering_env_data
- title: "Step 4: Prediction of performance for untested genotypes and/or environment"
desc: "Implement predictions for unobserved configurations of genotypic and environmental predictors"
contents:
- subtitle: "Main function"
contents:
- predict_trait_MET
- subtitle: 'Processing of genotypic data for ML-based predictions'
contents:
- apply_pca
- apply_pcs_G_Add
- select_markers
- marker_effect_per_env_EN
- marker_effect_per_env_FarmCPU
- subtitle: 'ML-methods implemented: processing functions according to the method'
contents:
- get_splits_processed_with_method
- stacking_reg_1
- stacking_reg_2
- stacking_reg_3
- xgb_reg_1
- xgb_reg_2
- xgb_reg_3
- DL_reg_1
- DL_reg_2
- DL_reg_3
- rf_reg_1
- rf_reg_2
- rf_reg_3
- subtitle: 'ML-methods implemented: fitting functions according to the method'
contents:
- fit_split
- fit_split.stacking_reg_1
- fit_split.stacking_reg_2
- fit_split.stacking_reg_3
- fit_split.xgb_reg_1
- fit_split.xgb_reg_2
- fit_split.xgb_reg_3
- fit_split.DL_reg_1
- fit_split.DL_reg_2
- fit_split.DL_reg_3
- fit_split.rf_reg_1
- fit_split.rf_reg_2
- fit_split.rf_reg_3
- subtitle: 'Compute variable importance (model-specific; model-agnostic methods, e.g. permutation-based methods)'
contents:
- variable_importance_split
- plot_results_vip
- subtitle: 'Accumulated local effects plots: understand the influence of the value of a variable on the changes in model´s prediction'
contents:
- ALE_plot_split
- subtitle: 'Plot variable importance results'
contents:
- plot_results_vip
- title: "Step 5: Analysis of prediction results for new observations, by location and by environmental cluster"
contents:
- analysis_predictions
- title: "Datasets"
desc: "Toy data to illustrate the use of the package functions"
contents:
- pheno_indica
- geno_indica
- map_indica
- climate_variables_indica
- info_environments_indica
- pheno_japonica
- geno_japonica
- map_japonica
- climate_variables_japonica
- info_environments_japonica
- pheno_G2F
- geno_G2F
- map_G2F
- soil_G2F
- info_environments_G2F
- intervals_growth_manual_G2F
resource_files:
- vignettes/metalearner_cv0.png