8.1 Fit a linear model

8.1.1 Base R

library(AmesHousing)

ames <- AmesHousing::make_ames()

# Gr Liv Area = Above grade (ground) living area square feet
lm_ames <- lm(Sale_Price ~ Gr_Liv_Area, data = ames)

lm_ames
## 
## Call:
## lm(formula = Sale_Price ~ Gr_Liv_Area, data = ames)
## 
## Coefficients:
## (Intercept)  Gr_Liv_Area  
##     13289.6        111.7

broom::tidy(lm_ames)
## # A tibble: 2 x 5
##   term        estimate std.error statistic   p.value
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)   13290.   3270.        4.06 0.0000494
## 2 Gr_Liv_Area     112.      2.07     54.1  0

8.1.2 Tidymodels

Steps in a parsnip model:

Pick a model
Set the engine (what library is running the model)
Set the mode (if needed, e.g., regression or classification)

List of models: https://tidymodels.github.io/parsnip/articles/articles/Models.html

Allows you to quickly swap out and try different models

library(parsnip)

lm_spec <- parsnip::linear_reg() %>%
  parsnip::set_engine(engine = "lm")

Train/fit the model:

lm_fit <- parsnip::fit(lm_spec, Sale_Price ~ Gr_Liv_Area, data = ames)
lm_fit
## parsnip model object
## 
## Fit time:  3ms 
## 
## Call:
## stats::lm(formula = formula, data = data)
## 
## Coefficients:
## (Intercept)  Gr_Liv_Area  
##     13289.6        111.7

broom::tidy(lm_fit)
## # A tibble: 2 x 5
##   term        estimate std.error statistic   p.value
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
## 1 (Intercept)   13290.   3270.        4.06 0.0000494
## 2 Gr_Liv_Area     112.      2.07     54.1  0