R: Modelling Functions that Work with the Pipe

# Modelling Functions that Work with the Pipe

## Documentation for package ‘modelr’ version 0.1.1

- DESCRIPTION file.

## Help Pages

add_predictions | Add predictions to a data frame
add_predictors | Add predictors to a formula
add_residuals | Add residuals to a data frame
bootstrap | Generate 'n' bootstrap replicates.
crossv_kfold | Generate cross-validated test-training pairs
crossv_mc | Generate cross-validated test-training pairs
data_grid | Generate a data grid.
fit_with | Fit a list of formulas
formulae | Create a list of formulas
formulas | Create a list of formulas
gather_predictions | Add predictions to a data frame
gather_residuals | Add residuals to a data frame
geom_ref_line | Add a reference line (ggplot2).
heights | Height and income data.
mae | Compute model quality for a given dataset
model-quality | Compute model quality for a given dataset
model_matrix | Construct a design matrix
na.warn | Handle missing values with a warning
permute | Generate 'n' permutation replicates.
permute_ | Generate 'n' permutation replicates.
qae | Compute model quality for a given dataset
resample | A "lazy" resample.
resample_bootstrap | Generate a boostrap replicate
resample_partition | Generate an exclusive partitioning of a data frame
resample_permutation | Create a resampled permutation of a data frame
rmse | Compute model quality for a given dataset
rsquare | Compute model quality for a given dataset
seq_range | Generate a sequence over the range of a vector
sim | Simple simulated datasets
sim1 | Simple simulated datasets
sim2 | Simple simulated datasets
sim3 | Simple simulated datasets
sim4 | Simple simulated datasets
spread_predictions | Add predictions to a data frame
spread_residuals | Add residuals to a data frame
typical | Find the typical value