---
title: "Accessing Fitted Model Objects"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Accessing Fitted Model Objects}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
echo = TRUE,
message = FALSE,
warning = FALSE
)
```
```{r}
library(dplyr); library(tidyr); library(purrr) # Data wrangling
library(tidyfit) # Auto-ML modeling
```
The fitted model object is contained in `tidyfit.models` frame in the `model_object` column as an `R6` class. The `tidyFit` `R6` class contains both the underlying model (`...$object`) as well as additional information generated during fitting and needed to obtain predictions or coefficients.
Suppose, for instance, we want to visualize the regression tree of the hierarchical features regression for different degrees of shrinkage (see `?hfr::plot.hfr`). We begin by loading Boston house price data and fitting a regression for 4 different shrinkage parameters. Note that we do not need to specify a `.cv` argument, since we are not looking to select the optimal degree of shrinkage:
```{r}
data <- MASS::Boston
mod_frame <- data %>%
regress(medv ~ ., m("hfr", kappa = c(0.25, 0.5, 0.75, 1))) %>%
unnest(settings)
mod_frame
```
`kappa` defines the extent of shrinkage, with `kappa = 1` equal to an unregularized least squares (OLS) regression, and `kappa = 0.25` representing a regression graph that is shrunken to 25% of its original size, with 25% of the effective degrees of freedom. The regression graph is visualized using the `plot` function.
Let's examine the first model in the `tidyfit.models` frame:
```{r}
mod_frame$model_object[[1]]
```
## Accessing the fitted model
We have two options to plot the regression trees. Many generics function directly on the `tidyFit` class. Therefore, we could simply plot (in this case the unregularized regression graph):
```{r, fig.width=7, fig.height=6, fig.align='center'}
mod_frame %>%
filter(kappa == 1) %>%
pull(model_object) %>%
.[[1]] %>%
plot(kappa = 1)
```
The regression graph shows which variables have a similar explanatory effect on the target (variables that are adjacent have a similar effect). The sizes of the leaf-nodes represent the absolute size of the coefficients.
Alternatively, we could access the underlying `cv.hfr` object using `...$object`:
```{r}
mod_frame <- mod_frame %>%
mutate(mod = map(model_object, ~.$object))
mod_frame
```
Now there is a column with `cv.hfr` objects. This is useful, when we want to perform any analysis not directly implemented in the `tidyFit` generics.
## Comparing different regression graphs
Finally, we can use `pwalk` to compare the different settings in a plot:
```{r, fig.width=7, fig.height=6, fig.align='center'}
# Store current par before editing
old_par <- par()
par(mfrow = c(2, 2))
par(family = "sans", cex = 0.7)
mod_frame %>%
arrange(desc(kappa)) %>%
select(model_object, kappa) %>%
pwalk(~plot(.x, kappa = .y,
max_leaf_size = 2,
show_details = FALSE))
```
```{r}
# Restore old par
par(old_par)
```
Notice how with each smaller value of `kappa` the height of the tree shrinks and the model parameters become more similar in size. This is precisely how HFR regularization works: it shrinks the parameters towards group means over groups of similar features as determined by the regression graph.