---
title: "Interpreting and Labeling Factors"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Interpreting and Labeling Factors}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 7
)
```

```{r packages, message = FALSE}
library(ackwards)
# Fit the raw dataset (not na.omit()) so bfi25's built-in IPIP item labels are
# captured and shown by top_items(); missing = "listwise" drops incomplete rows.
x <- ackwards(bfi25, k_max = 5, cor = "polychoric", missing = "listwise")
```

Fitting an `ackwards` model gives you a hierarchy of factors with stable but
opaque IDs — `m1f1`, `m2f1`, `m2f2`, and so on. Turning those IDs into something
you can reason about is *interpretive* work: you read each factor's loadings,
decide what construct it represents, and give it a name. This is the part of the
analysis that requires judgment, and it is harder in a bass-ackwards hierarchy
than in a single flat factor solution, because the same construct can appear,
split, and merge across levels.

This article covers the full workflow:

1. **Read** each factor with `top_items()`.
2. **Understand** the sign convention so you don't misread a factor.
3. **Name** factors in a way that respects the hierarchy.
4. **Apply** your names — to a single diagram with `label_template()` and
   `autoplot(node_labels = ...)`, or persistently to the whole object with
   `set_factor_labels()`.

## Reading a factor with `top_items()`

A factor is defined by the items that load strongly on it. `top_items()` lists,
for each factor, the items whose absolute loading meets a threshold, sorted from
strongest to weakest. This is far easier to read than a full item-by-factor
matrix, especially at deeper levels.

```{r top-items-5}
top_items(x, level = 5, cut = 0.5)
```

The `cut` threshold controls how inclusive the listing is. Here we raise it to
`0.5` to keep each factor to its defining items; the default is `0.3`. Raise it
further to isolate only the strongest markers, or lower it to surface weaker
cross-loadings (see below). When a factor has many salient items, `n` caps the
list at the strongest few, and `sort = FALSE` keeps items in their original order
instead of sorting by `|loading|` — useful when your items follow a meaningful
sequence.

```{r top-items-n}
top_items(x, level = 5, cut = 0.3, n = 4)
```

### Cross-loadings are signal

Items that load on more than one factor are not noise to be suppressed — they
often tell you how two factors relate. The `by = "item"` mode inverts the
grouping: instead of listing the items under each factor, it lists, for each
item, the factors it loads on. Lowering the cut then makes cross-loadings easy to
read item by item:

```{r top-items-cross}
top_items(x, level = 3, cut = 0.25, by = "item")
```

An item that appears under two factors at the same level marks a point where the
constructs overlap. Whether that overlap is substantively meaningful or a sign of
overextraction is a judgment call — see `vignette("ackwards-suggest-k")` for the
overextraction discussion.

### Showing item wording instead of codes

Item codes like `A1` or `N3` are compact but opaque. The wording you have been
seeing above — `E4: Make friends easily` — comes for free: `bfi25` ships each
item's IPIP stem as a **variable label** (a `"label"` column attribute),
`ackwards()` captures those labels at fit time, and `top_items()` prints them as
`code: label`.

Your own data often carries the same attribute (packages like **labelled** and
**haven** set it, and survey exports frequently include it). If it does *not*,
attach the wording yourself — with `labelled::var_label()` or base `attr()` — on
the data you actually fit (base row-subsetting such as `na.omit()` drops plain
attributes, which is why we fit the raw `bfi25` above):

```{r own-labels, eval = FALSE}
# tidyverse idiom:
labelled::var_label(my_data$item1) <- "Full wording of item 1"

# or base R, no extra package:
attr(my_data$item1, "label") <- "Full wording of item 1"
```

Labelled items show their wording; unlabelled ones fall back to the bare code, so
a partially labelled data set still prints cleanly. Pass `show_labels = FALSE` to
force the bare codes even when labels are present.

These *variable* labels (item wording) are distinct from the *factor* labels
(the names you give `m1f1`, `m2f1`, … below): one describes your measured items,
the other names the latent factors the hierarchy discovers. Keep the two ideas —
and the word "label" — separate.

## The sign convention: negative does not mean "low"

Before naming anything, understand how `ackwards` orients factors. Loadings are
**sign-aligned to the primary parent** (see `?ackwards`): the level-1 factor is
anchored so its loadings sum positive, and every deeper factor is flipped so its
correlation with its primary parent is positive. This makes the diagram readable,
but it has a consequence for interpretation.

A factor's *sign* is arbitrary in the sense that flipping every loading and the
factor's orientation describes the same dimension. So a column of negative
loadings does **not** mean "low" on that construct — it means the construct's
positive pole was oriented the other way by the alignment step. Read the
*pattern* of items, not the bare sign:

```{r sign-example}
top_items(x, level = 2)
```

If a Neuroticism factor shows up with negative loadings on the anxiety items,
that is an orientation artifact of the alignment, not a "low anxiety" factor.
Name it for the construct (Neuroticism), and if the sign matters for your
downstream use, flip the scores yourself. The values shown by `top_items()` and
`tidy(what = "loadings")` are the aligned values, so they are consistent with the
diagram and the edge table.

## Naming in a hierarchy

In a flat factor solution you name each factor once. In a bass-ackwards hierarchy
you are naming factors at *every* level, and the levels are related — so the
names should be related too. The lineage tells you how.

```{r lineage}
summary(x)
```

The lineage list (`m1f1 → m2f1, m2f2 → ...`) shows each factor's primary
children. Use it to name **top-down**:

- **Upper-level factors are broader.** A level-2 factor that is the primary
  parent of two level-3 factors is the construct they share. Name it for the
  blend, not for one child. In the `bfi25` data above, the level-1 factor is a
  single broad dimension; at level 2 it separates into a Neuroticism factor
  (`m2f2`, defined by the N items) and a broad factor blending the remaining four
  trait families (`m2f1`). The Big Five themselves do not appear cleanly until
  level 5 — so a substantive name for `m2f1` ("broad well-adjustment", say) is
  necessarily coarser than the names you give its descendants.

- **A split is a refinement, not a contradiction.** When a parent factor splits
  into two children, the children carve up the parent's content. Their names
  should read as specializations of the parent's name, so that reading down a
  branch tells a coherent story.

- **Watch for factors that reorganize.** A child whose strongest parent is at the
  *other* side of the level above (a crossing edge), or a factor whose primary
  edge is weak (`|r|` well below the near-1.0 values of stable dimensions), is a
  place where the structure is genuinely rearranging. The edge table makes these
  visible:

```{r edges-weak}
# Primary-parent edges, weakest last: the bottom rows are where structure shifts
tidy(x, what = "edges", primary_only = TRUE, sort = "strength") |> tail()
```

Edges with `|r|` near 1.0 are factors that pass through nearly unchanged — name
the child the same as the parent. The smaller `|r|` values at the bottom flag
where a new, distinct construct is emerging and deserves its own name.

### Borrowing names for the upper levels

The hardest factors to name are usually the broad ones near the top, precisely
because they blend several familiar constructs. Rather than inventing an ad-hoc
label, it often helps to borrow a name from a theory that has already charted the
level *above* your usual constructs. Two are especially handy for personality and
psychopathology data:

- **Big Five metatraits.** Above the five factors sit two higher-order
  dimensions — **Stability** (the shared variance of Agreeableness,
  Conscientiousness, and low Neuroticism) and **Plasticity** (Extraversion and
  Openness) — from DeYoung's work on the metatraits. In a `bfi25` hierarchy these
  are frequently what a two- or three-factor level *is*, so "Stability" /
  "Plasticity" are ready-made names for factors that would otherwise be an
  awkward "broad well-adjustment".
- **HiTOP spectra.** In clinical data, the Hierarchical Taxonomy of
  Psychopathology names the broad bands directly — *internalizing*,
  *externalizing (disinhibited and antagonistic)*, *thought disorder*, *detachment*,
  and *somatoform* — with a general **p-factor** at the apex. A level whose items
  span several disorders is usually one of these spectra, and naming it as such
  keeps your hierarchy legible to readers who already think in HiTOP terms.

The point is not to force your data onto either scheme, but to recognise that an
upper-level factor is often a *known* superordinate construct — and that reusing
its established name communicates far more than a bespoke one.

## Applying your names

Once you have decided on names, attach them to the diagram. `autoplot()` takes a
`node_labels` argument: a named character vector mapping factor IDs to display
strings.

Typing that vector out by hand is tedious and error-prone, so `label_template()`
generates it for you, in the same order the diagram uses, and prints an editable
`c(...)` literal you can paste straight into your script:

```{r label-template}
label_template(x)
```

Copy that literal, fill in your names, and pass it to `autoplot()`. Unspecified
IDs keep their default `m{k}f{j}` label, so you can label just the level you care
about:

```{r node-labels}
autoplot(x, node_labels = c(
  m5f1 = "Neuroticism",
  m5f2 = "Extraversion",
  m5f3 = "Conscientiousness",
  m5f4 = "Agreeableness",
  m5f5 = "Openness"
))
```

### Making the names stick with `set_factor_labels()`

`node_labels` styles a single plot. When you want the same names to follow the
object everywhere — in `print()`, `summary()`, `tidy()`, and `top_items()`, not
just one diagram — attach them once with `set_factor_labels()`. It takes the same
named vector `label_template()` scaffolds, and returns the object so it pipes:

```{r set-factor-labels}
x <- set_factor_labels(x, c(
  m5f1 = "Neuroticism",
  m5f2 = "Extraversion",
  m5f3 = "Conscientiousness",
  m5f4 = "Agreeableness",
  m5f5 = "Openness"
))
```

Now the labels appear wherever factors are listed. `summary()` shows them as
`label (id)`, keeping the stable ID visible so you can still cross-reference the
edge and loading tables:

```{r labelled-summary}
summary(x)
```

`top_items(by = "factor")` uses them on its group headers, and `tidy()` adds a
`factor_label` column (and `from_label`/`to_label` for edges) — but *only* when
labels are set, so unlabelled objects keep their exact previous output:

```{r labelled-tidy}
head(tidy(x, what = "loadings"))
```

`autoplot()` uses stored labels as the node text automatically, so you no longer
need to pass `node_labels` for a labelled object; a call-time `node_labels` entry
still overrides a stored label for that one node. Labels are display only — factor
IDs never change — and they ride along through `prune()`, `boot_edges()`,
`augment()`, and `predict()`. Read them back with `factor_labels(x)`; clear one by
setting it to `NA`, or all of them by passing `NULL`.

### The Forbes letter convention

Forbes (2023) labels nodes by level-letter and within-level index — `A1` for the
single level-1 factor, `B1`/`B2` at level 2, and so on. `label_template()`
produces this convention directly with `style = "forbes"`:

```{r label-forbes}
autoplot(x, node_labels = label_template(x, style = "forbes"))
```

This is useful when you want to refer to nodes by position rather than by
substantive name — for example, in a methods section that walks through the
hierarchy before interpreting it.

### Starting from a blank slate

If you would rather supply every label yourself with no defaults showing through,
`style = "blank"` gives you an all-empty scaffold to fill in:

```{r label-blank}
labs <- label_template(x, style = "blank")
labs["m5f1"] <- "N"
labs["m5f2"] <- "E"
labs["m5f3"] <- "C"
labs["m5f4"] <- "A"
labs["m5f5"] <- "O"
autoplot(x, node_labels = labs)
```

This article is about *what* to put on the diagram.

> **Note:** For *how* the diagram looks — colours, edge thresholds, monochrome
> and publication styling, level labels, and the Forbes pruned-diagram mode — see
> `vignette("ackwards-visualization")`.

## References

DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-informant
sample. *Journal of Personality and Social Psychology, 91*(6), 1138–1151.
https://doi.org/10.1037/0022-3514.91.6.1138

Forbes, M. K. (2023). Improving hierarchical models of individual differences:
An extension of Goldberg's bass-ackward method. *Psychological Methods*.
https://doi.org/10.1037/met0000546

Kotov, R., Krueger, R. F., Watson, D., et al. (2017). The Hierarchical Taxonomy
of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies.
*Journal of Abnormal Psychology, 126*(4), 454–477.
https://doi.org/10.1037/abn0000258
