All functions
|
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 |
Crime
|
Data from the US FBI Uniform Crime Report, 1960 |
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 |
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. |
Trucking_jobs
|
Earnings of workers at a trucking company. |
Used_Fords
|
Prices of used Ford automobiles in 2009 |
collinearity()
|
Calculate measures of collinearity |
construct_fitting_call()
|
Construct a call for refitting a model from the model itself |
data_from_mod()
|
Extract training data from model |
df_counts()
|
Formula interface to counts |
df_props()
|
Joint and conditional proportions |
df_typical()
|
Find typical levels of explanatory variables in a model/dataset. |
explanatory_vars()
|
Get the names of the explanatory or response variables in a model |
formula_from_mod()
|
Extract the model formula used in specifying the model |
mod_cv()
|
Compare models with k-fold cross validation |
mod_effect()
|
Calculate effect sizes in a model |
mod_ensemble()
|
Create bootstrapped ensembles of a model |
mod_error()
|
Mean square prediction error |
mod_eval() mod_output()
|
Evaluate a model for specified inputs |
mod_eval_fun()
|
Internal functions for evaluating models |
mod_eval_grid()
|
Create discrete levels of explanatory variables in a model/dataset. |
mod_fun()
|
Transforms a model into a function of inputs -> output |
mod_plot()
|
Plot out model values |
mosaicModel
|
mosaicModel package
|
reference_values()
|
Compute sensible values from a data set for use as a baseline |