I have a question about predictions using ggeffects, which is giving me completely different results if I use a traditional lm fit or an extracted parsnip model fit (despite having the same coefficients). Here is an example...
library(tidyverse)
library(tidymodels)
library(ggeffects)
test_df <- structure(list(weight = c(-1.7, 0, 0.6, 0.6, -0.7, -0.3, -0.6,
-1, -1, 2, 0.1, -0.6, -1.5, 2, -0.7, -0.2, -0.9, -0.6, 1.1, -2,
1.4, -1, -1.1, 0.5, 1.3, 0, -0.5, -3, 1.1, -0.6), steps = c(19217,
15758, 14124, 14407, 5565, 20860, 17536, 17156, 17219, 652, 1361,
8524, 1169, 3117, 3135, 1917, 4267, 7067, 8927, 2436, 3014, 5281,
8104, 6836, 8939, 4923, 6885, 10581, 10370, 11024), calories = c(1943,
1581, 1963, 1551, 1699, 1789, 1550, 2036, 1707, 1522, 1672, 1994,
1588, 1506, 1678, 1673, 1662, 1906, 1814, 1609, 1799, 1825, 1654,
2291, 1788, 2019, 1911, 1589, 2177, 2137)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -30L)) %>%
as_tibble(.)
#lm fit
lmmod_simp <- lm(weight ~ steps * calories, data = test_df)
#tidymodels
linear_reg_lm_spec <-
linear_reg() %>%
set_engine('lm')
basic_rec <- recipe(weight ~ steps + calories, test_df) %>%
step_interact(terms = ~ steps:calories)
lm_wflw <- workflow() %>%
add_recipe(basic_rec) %>%
add_model(linear_reg_lm_spec)
lm_fit <- fit(lm_wflw, data = test_df)
lm_fit_extracted <- lm_fit %>% extract_fit_parsnip()
When I look at the output, both have the same coefficients
lmmod_simp
lm_fit_extracted
But when I go to predict, the predictions are completely different
ggemmeans(lmmod_simp, terms = c("steps", "calories[1500,2000,2500]")) %>%
as.data.frame() %>%
ggplot(aes(x,predicted, color=group, linetype = group))+
geom_line()
ggemmeans(lm_fit_extracted, terms = c("steps", "calories[1500,2000,2500]")) %>%
as.data.frame() %>%
ggplot(aes(x,predicted, color=group, linetype = group))+
geom_line()
Perhaps I can't/shouldn't use the parsnip fit object in this way, but it seems odd since they are showing the same coefficients.
I appreciate any help!
You are getting different results because lmmod_simp and lm_fit_extracted are different models. While lm_fit has an interaction effect on steps, lm_fit_extracted has no idea about this interaction as it gets the data after the interaction calculation has been performed.
It is generally not recommended to pull out models from a workflow object if you plan on using it for other things than diagnostics.
