It parses a fitted 'R' model object, and returns a formula in 'Tidy Eval' code that calculates the predictions. It works with several databases back-ends because it leverages 'dplyr' and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(), cubist(), and ctree() models.
Run predictions inside the database. tidypredict
parses a fitted R
model object, and returns a formula in ‘Tidy Eval’ code that calculates
the predictions.
It works with several databases back-ends because it leverages
dplyr
and dbplyr
for the final SQL translation of the algorithm. It
currently supports lm()
, glm()
, randomForest()
, ranger()
ane
earth()
models.
Install tidypredict
from CRAN using:
install.packages("tidypredict")
Or install the development version using devtools
as follows:
devtools::install_github("edgararuiz/tidypredict")
tidypredict
is able to parse an R model object, such as:
model <- lm(mpg ~ wt + cyl, data = mtcars)
And then creates the SQL statement needed to calculate the fitted prediction:
tidypredict_sql(model, dbplyr::simulate_mssql())
## <SQL> 39.6862614802529 + (`wt` * -3.19097213898374) + (`cyl` * -1.5077949682598)
The following models are supported:
lm()
glm()
randomForest::randomForest()
ranger
- ranger::ranger()
earth::earth()
earth
packageNew parsed models are now list objects as opposed to data frames.
tidypredict_to_column() no longer supports ranger
and randomForest
because of the multiple queries generated by multiple trees.
All functions that read the parsed models and create the tidy eval formula now use the list object.
Most of the code that depends on dplyr programming has been removed.
Removes dependencies on: tidyr, tibble
randomForest
& ranger
) (#29)ranger()
models.x ~.
in a randomForest() formula fails (#18 @washcycle).