Provides graphics and other functions that evaluate and display models across many different kinds of model architecture. For instance, you can evaluate the effect size of a model input in the same way, regardless of architecture, interaction terms, etc.
|Date of publication||2016-11-12 15:47:15|
|Maintainer||Daniel Kaplan <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
AARP: Prices for life insurance
Alder: Nitrogen fixing by alder plants
Birth_weight: Birth weights and maternal data
College_grades: Grades at a small college
collinearity: Calculate measures of collinearity
Crime: Data from the US FBI Uniform Crime Report, 1960
cv_pred_error: Compare models with k-fold cross-validation
effect_size: Calculate effect sizes in a model
ensemble: Create bootstrapped ensembles of a model
evaluate_model: Evaluate a model for specified inputs
extract_from_model: Extract component parts of models
fmodel: Plot out model values
gf_functions: gf_ plotting functions
gmodel: Graph the function implicit in a model
HDD_Minneapolis: Heating degree days in Minneapolis, Minnesota, USA
Houses_for_sale: Houses for sale
Mussels: Metabolism of zebra mussels
NCI60_snippet: Gene expression in cancer cells.
Oil_history: Historical production of crude oil, worldwide 1880-2014
reference_values: Compute sensible values from a data set for use as a baseline
Runners: Performance of runners in a ten-mile race as they age
School_data: Simulated data bearing on school vouchers
Tadpoles: Swimming speed of tadpoles.
train: Training statistical models
Trucking_jobs: Earnings of workers at a trucking company.
typical_levels: Find typical levels of explanatory variables in a...
Used_Fords: Prices of used Ford automobiles in 2009