knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(dplyr) library(tidypredict) library(parsnip)
qr.solve()function to parse the interval coefficient of each term.
contr.treatment) are supported.
wt ~ mpg + am
mutate(mtcars, newam = paste0(am))and then
wt ~ mpg + newam
wt ~ mpg + as.factor(am)
wt ~ mpg + as.character(am)
library(dplyr) library(tidypredict) df <- mtcars %>% mutate(char_cyl = paste0("cyl", cyl)) %>% select(mpg, wt, char_cyl, am) model <- lm(mpg ~ wt + char_cyl, offset = am, data = df)
It returns a SQL query that contains the coefficients (
model$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())
tidypredict_to_column() if the results are the be used or previewed in
df %>% tidypredict_to_column(model) %>% head(10)
tidypredict_sql_interval() to get the SQL query that operates the prediction interval. The
interval defaults to 0.95
Prediction intervals also works in the
tidypredict_to_column(), just set the
add_interval argument to
df %>% tidypredict_to_column(model, add_interval = TRUE) %>% head(10)
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
A function to put together the Tidy Eval interval formula is also supported
From there, the Tidy Eval formula can be used anywhere where it can be operated.
tidypredict provides three paths:
mutate(df, !! tidypredict_fit(model))
tidypredict_to_column(model)to a piped command set
tidypredict_to_sql(model)to retrieve the SQL statement
The same applies to the prediction interval functions.
tidypredict results is easy. The
tidypredict_test() function automatically uses the
lm model object's data frame, to compare
tidypredict_interval() to the results given by
To run with prediction intervals set the
include_intervals argument to
tidypredict_test(model, include_intervals = TRUE)
tidypredict also supports
lm() model objects fitted via the
library(parsnip) parsnip_model <- linear_reg() %>% set_engine("lm") %>% fit(mpg ~ wt + cyl, offset = am, data = mtcars) tidypredict_fit(parsnip_model)
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