Statistical learning methods – both supervised and unsupervised – provide techniques for gaining insights from data. These methods have various goals, including prediction, pattern recognition, and classification; they also vary in complexity and interpretability. This lecture is intended to provide a very broad overview of two methods: lasso and k-means clustering.

`## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──`

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```

As always, I’ll work on today’s example in a GitHub repo + local directory / R Project. This zip file has a couple of datasets we’ll look at.

```
library(tidyverse)
library(glmnet)
```

`## Loading required package: Matrix`

```
##
## Attaching package: 'Matrix'
```

```
## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack
```

`## Loaded glmnet 4.1-3`

`set.seed(11)`

To illustrat the lasso, we’ll data from a study of factors that affect birthweight. The code chunk below loads and cleans these data, converts to factors where appropriate, and takes a sample of size 200 from the result.

```
bwt_df =
read_csv("./data/birthweight.csv") %>%
janitor::clean_names() %>%
mutate(
babysex = as.factor(babysex),
babysex = fct_recode(babysex, "male" = "1", "female" = "2"),
frace = as.factor(frace),
frace = fct_recode(frace, "white" = "1", "black" = "2", "asian" = "3",
"puerto rican" = "4", "other" = "8"),
malform = as.logical(malform),
mrace = as.factor(mrace),
mrace = fct_recode(mrace, "white" = "1", "black" = "2", "asian" = "3",
"puerto rican" = "4")) %>%
sample_n(200)
```

`## Rows: 4342 Columns: 20`

```
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (20): babysex, bhead, blength, bwt, delwt, fincome, frace, gaweeks, malf...
```

```
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

To fit a lasso model, we’ll use `glmnet`

. This package is widely used and broadly useful, but predates the `tidyverse`

by a long time. The interface asks for an outcome vector `y`

and a matrix of predictors `X`

, which are created next. To create a predictor matrix that includes relevant dummy variables based on factors, we’re using `model.matrix`

and excluding the intercept

```
x = model.matrix(bwt ~ ., bwt_df)[,-1]
y = bwt_df$bwt
```

We fit the lasso model for each tuning parameter in a pre-defined grid `lambda`

, and then compare the fits using cross validation. I chose this grid using the trusty “try things until it looks right” method; `glmnet`

will pick something reasonable by default if you prefer that.

```
lambda = 10^(seq(3, -2, -0.1))
lasso_fit =
glmnet(x, y, lambda = lambda)
lasso_cv =
cv.glmnet(x, y, lambda = lambda)
lambda_opt = lasso_cv$lambda.min
```

The plot below shows coefficient estimates corresponding to a subset of the predictors in the dataset – these are predictors that have non-zero coefficients for at least one tuning parameter value in the pre-defined grid. As lambda increases, the coefficient values are shrunk to zero and the model becomes more sparse. The optimal tuning parameter, determined using cross validation, is shown by a vertical blue line.

```
broom::tidy(lasso_fit) %>%
select(term, lambda, estimate) %>%
complete(term, lambda, fill = list(estimate = 0) ) %>%
filter(term != "(Intercept)") %>%
ggplot(aes(x = log(lambda, 10), y = estimate, group = term, color = term)) +
geom_path() +
geom_vline(xintercept = log(lambda_opt, 10), color = "blue", size = 1.2) +
theme(legend.position = "none")
```

The next plot shows the CV curve itself. This is relatively shallow – having nothing at all in your model isn’t great, but you can get reasonable predictions from models that have “too many” predictors.

```
broom::tidy(lasso_cv) %>%
ggplot(aes(x = log(lambda, 10), y = estimate)) +
geom_point()
```

The coefficients from the optimal model are shown below.

```
lasso_fit =
glmnet(x, y, lambda = lambda_opt)
lasso_fit %>% broom::tidy()
```

```
## # A tibble: 12 × 5
## term step estimate lambda dev.ratio
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 1 -3659. 12.6 0.627
## 2 babysexfemale 1 46.2 12.6 0.627
## 3 bhead 1 77.9 12.6 0.627
## 4 blength 1 71.8 12.6 0.627
## 5 fincome 1 0.252 12.6 0.627
## 6 gaweeks 1 23.1 12.6 0.627
## 7 malformTRUE 1 447. 12.6 0.627
## 8 menarche 1 -29.4 12.6 0.627
## 9 mraceblack 1 -105. 12.6 0.627
## 10 mracepuerto rican 1 -145. 12.6 0.627
## 11 smoken 1 -2.62 12.6 0.627
## 12 wtgain 1 2.32 12.6 0.627
```

To be clear, these don’t come with p-values and it’s really challenging to do inference. These are also different from a usual OLS fit for a multiple linear regression model that uses the same predictors: the lasso penalty affects these even if they’re retained by the model.

A final point is that on the full dataset, lasso doesn’t do you much good. With ~4000 datapoints, the relatively few coefficients are estimated well enough that penalization doesn’t make much of a difference in this example.

For ther first clustering example, we’ll use a dataset containing information about pokemon. The full dataset contains several variables (including some that aren’t numeric, which is a challenge for clustering we won’t address). To make results easy to visualize, we look only at `hp`

and `speed`

; a scatterplot is below.

```
poke_df =
read_csv("./data/pokemon.csv") %>%
janitor::clean_names() %>%
select(hp, speed)
```

`## Rows: 800 Columns: 13`

```
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Name, Type 1, Type 2
## dbl (9): #, Total, HP, Attack, Defense, Sp. Atk, Sp. Def, Speed, Generation
## lgl (1): Legendary
```

```
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

```
poke_df %>%
ggplot(aes(x = hp, y = speed)) +
geom_point()
```

K-means clustering is established enough that it’s implemented in the base R `stats`

package in the `kmeans`

function. This also has a bit of an outdated interface, but there you go. The code chunk below fits the k-means algorithm with three clusters to the data shown above.

```
kmeans_fit =
kmeans(x = poke_df, centers = 3)
```

More recent tools allow interactions with the `kmeans`

output. In particular, we’ll use `broom::augment`

to add cluster assignments to the data, and plot the results.

```
poke_df =
broom::augment(kmeans_fit, poke_df)
poke_df %>%
ggplot(aes(x = hp, y = speed, color = .cluster)) +
geom_point()
```

Clusters broadly interpretable, but this still doesn’t come with inference. Also, at boundaries between clusters, the distinctions can seem a bit … arbitrary.

The code chunk below maps across a few choices for the number of clusters, and then plots the results.

```
clusts =
tibble(k = 2:4) %>%
mutate(
km_fit = map(k, ~kmeans(poke_df, .x)),
augmented = map(km_fit, ~broom::augment(.x, poke_df))
)
clusts %>%
select(-km_fit) %>%
unnest(augmented) %>%
ggplot(aes(hp, speed, color = .cluster)) +
geom_point(aes(color = .cluster)) +
facet_grid(~k)
```

There are metrics that can suggest which of these is the better choice, but we won’t get into that.

A second clustering example uses longitudinally observed data. The process we’ll focus on is:

- for each subject, estimate a simple linear regression
- extract the intercept and slope
- cluster using the intercept and slope

Below we import and plot the trajectory data.

```
traj_data =
read_csv("./data/trajectories.csv")
```

`## Rows: 1600 Columns: 3`

```
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (3): subj, week, value
```

```
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
```

```
traj_data %>%
ggplot(aes(x = week, y = value, group = subj)) +
geom_point() +
geom_path()
```

Next we’ll do some data manipulation. These steps compute the SLRs, extract estimates, and format the data for k-means clustering.

```
int_slope_df =
traj_data %>%
nest(data = week:value) %>%
mutate(
models = map(data, ~lm(value ~ week, data = .x)),
result = map(models, broom::tidy)
) %>%
select(subj, result) %>%
unnest(result) %>%
select(subj, term, estimate) %>%
pivot_wider(
names_from = term,
values_from = estimate
) %>%
rename(int = "(Intercept)", slope = week)
```

A plot of the intercepts and slopes are below. There does seem to be some structure, and we’ll use k-means clustering to try to make that concrete.

```
int_slope_df %>%
ggplot(aes(x = int, y = slope)) +
geom_point()
```

```
km_fit =
kmeans(
x = int_slope_df %>% select(-subj) %>% scale,
centers = 2)
int_slope_df =
broom::augment(km_fit, int_slope_df)
```

The plot below shows the results of k-means based on the intercepts and slopes. This is … not bad, but honestly maybe not what I’d hoped for.

```
int_slope_df %>%
ggplot(aes(x = int, y = slope, color = .cluster)) +
geom_point()
```

Finally, we’ll add the cluster assignments to the original trajectory data and plot based on this. Again, the cluster assignments are okay but maybe not great.

```
left_join(traj_data, int_slope_df) %>%
ggplot(aes(x = week, y = value, group = subj, color = .cluster)) +
geom_point() +
geom_path()
```

`## Joining, by = "subj"`

This example is very much related to “trajectory analysis”, which has become pretty popular recently (maybe because `PROC TRAJ`

exists in SAS …). The basic idea is to use tools from longitudinal data analysis to estimate trajectories underlying data – mixed models rather than SLRs. The subject-level estimates (random effects) are then clustered; cluster means are hopefully interpretable, and cluster assignments are thought to be meaningful. In many cases, though, the distinction between groups is fairly arbitrary.

- Intro to Statistical Learning with R, chapters 6 and 10
- Nice shiny app for k-means
- Good overview of clustering
- Some discussion about tidying results

The code that I produced working examples in lecture is here.