Linear Regression
In tidypredict: Run Predictions Inside the Database

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(dplyr)
library(tidypredict)
library(parsnip)

Highlights & Limitations

Supports prediction intervals, it uses the qr.solve() function to parse the interval coefficient of each term.
Supports categorical variables and interactions
Only treatment contrast (contr.treatment) are supported.
offset is supported
Categorical variables are supported
In-line functions in the formulas are not supported:
- OK - wt ~ mpg + am
- OK - mutate(mtcars, newam = paste0(am)) and then wt ~ mpg + newam
- Not OK - wt ~ mpg + as.factor(am)
- Not OK - wt ~ mpg + as.character(am)

How it works

library(dplyr)
library(tidypredict)

df <- mtcars %>%
  mutate(char_cyl = paste0("cyl", cyl)) %>%
  select(mpg, wt, char_cyl, am)

model <- lm(mpg ~ wt + char_cyl, offset = am, data = df)

It returns a SQL query that contains the coefficients (model$coefficients) operated against the correct variable or categorical variable value. In most cases the resulting SQL is one short CASE WHEN statement per coefficient. It appends the offset field or value, if one is provided.

library(tidypredict)
tidypredict_sql(model, dbplyr::simulate_mssql())

Alternatively, use tidypredict_to_column() if the results are the be used or previewed in dplyr.

df %>%
  tidypredict_to_column(model) %>%
  head(10)

Prediction intervals

Use tidypredict_sql_interval() to get the SQL query that operates the prediction interval. The interval defaults to 0.95

tidypredict_sql_interval(model, dbplyr::simulate_mssql())

Prediction intervals also works in the tidypredict_to_column(), just set the add_interval argument to TRUE.

df %>%
  tidypredict_to_column(model, add_interval = TRUE) %>%
  head(10)

Under the hood

The parser reads several parts of the lm object to tabulate all of the needed variables. One entry per coefficient is added to the final table, those entries will have the results of qr.solve() already operated and placed in the correct column, they will have a qr_ prefix. There will be one qr_ column per coefficient.

Other variables are added at the end. Some variables are not required for every parsed model. For example, offset is listed because it's part of the formula (call) of the model, if there were no offset in a given model, that line would not exist.

pm <- parse_model(model)
str(pm, 2)

The output from parse_model() is transformed into a dplyr, a.k.a Tidy Eval, formula. All categorical variables are operated using if_else().

tidypredict_fit(model)

A function to put together the Tidy Eval interval formula is also supported

tidypredict_interval(model)

From there, the Tidy Eval formula can be used anywhere where it can be operated. tidypredict provides three paths:

Use directly inside dplyr, mutate(df, !! tidypredict_fit(model))
Use tidypredict_to_column(model) to a piped command set
Use tidypredict_to_sql(model) to retrieve the SQL statement

The same applies to the prediction interval functions.

How it performs

Testing the tidypredict results is easy. The tidypredict_test() function automatically uses the lm model object's data frame, to compare tidypredict_fit(), and tidypredict_interval() to the results given by predict()

tidypredict_test(model)

To run with prediction intervals set the include_intervals argument to TRUE

tidypredict_test(model, include_intervals = TRUE)

parsnip

tidypredict also supports lm() model objects fitted via the parsnip package.

library(parsnip)

parsnip_model <- linear_reg() %>%
  set_engine("lm") %>%
  fit(mpg ~ wt + cyl, offset = am, data = mtcars)

tidypredict_fit(parsnip_model)