Function reference • regressinator

Defining populations

Define a population, including predictors and response variables.

population(): Define the population generalized regression relationship

predictor(): Specify the distribution of a predictor variable

response(): Specify a response variable in terms of predictors

ols_with_error(): Family representing a linear relationship with non-Gaussian errors

custom_family(): Family representing a GLM with custom distribution and link function

Simulating and diagnosing fits

Draw samples from a population and diagnose model fits.

sample_x() sample_y(): Draw a data frame from the specified population.

parametric_boot_distribution(): Simulate the distribution of estimates by parametric bootstrap

model_lineup(): Produce a lineup for a fitted model

decrypt(): Decrypt message giving the location of the true plot in a lineup

sampling_distribution(): Simulate the sampling distribution of estimates from a population

Extracting information from regression fits

Get different types of diagnostics from fits.

augment_longer(): Augment a model fit with residuals, in "long" format

partial_residuals(): Augment a model fit with partial residuals for all terms

augment_quantile() augment_quantile_longer(): Augment data with randomized quantile residuals

binned_residuals(): Obtain binned residuals for a model

bin_by_interval() bin_by_quantile(): Group a data frame into bins

empirical_link(): Empirically estimate response values on the link scale

Working with factors

Use factors to model categorical variables.

rfactor(): Draw random values from a factor variable

by_level(): Convert factor levels to numeric values