# Mixed graphical models

## What a mixed graphical model is

A mixed graphical model analyzes continuous and binary variables in the same network. A continuous node can represent a scale score or measured quantity. A binary node can represent symptom presence, group membership, or a yes/no response.

Each edge describes an association that remains above and beyond the other variables. Consider a network with continuous motivation and self-efficacy scores together with binary indicators for assignment completion and high test anxiety. An edge between motivation and assignment completion means that motivation remains associated with the conditional probability of completion after self-efficacy, anxiety, and the other nodes have been considered.

A positive edge indicates that higher values or endorsement tend to occur together after adjustment. A negative edge indicates an inverse adjusted relation. A missing edge means that the selected model did not retain enough unique association for that pair. Edges remain associational and provide no causal direction.

## The data

The example uses three continuous `SRL_GPT` scores: cognitive strategy use (`CSU`), intrinsic value (`IV`), and self-efficacy (`SE`). Self-regulation and test anxiety are represented by binary indicators, `SR_hi` and `TA_hi`, coded 1 at or above their respective medians and 0 below them.

    head(mixed_data)
    #>        CSU       IV       SE SR_hi TA_hi
    #> 1 5.307692 5.666667 5.777778     0     1
    #> 2 5.846154 6.444444 6.000000     1     1
    #> 3 6.615385 6.666667 6.222222     1     0
    #> 4 5.692308 6.555556 6.333333     1     1
    #> 5 4.384615 5.555556 4.888889     0     1
    #> 6 4.846154 5.444444 5.666667     0     0

The self-regulation indicator has an endorsement rate of 0.54, and the test-anxiety indicator has a rate of 0.50. The example coding is pedagogical. Real applications should preserve continuous variables when the research question concerns their full scale and should use binary coding only when the two states have a meaningful interpretation.

## Fitting the network with `psychnet()`

`psychnet()` estimates a mixed graphical model when `method = "mgm"`. It detects 0/1 columns as binary and other numeric columns as Gaussian. The default AND rule retains an edge when both nodewise models select the relation.

    mixed_net <- psychnet(data = mixed_data, method = "mgm")
    mixed_net
    #> <psychnet> mgm network
    #>   nodes: 5   edges: 7   (undirected)
    #>   optimality (KKT residual): 7.58e-08

The fitted network contains 5 nodes and 7 edges. The printed KKT residual is \(7.58 \times 10^{-8}\).

## Inspecting mixed edges with `summary()`

`summary()` prints the fitted model and returns a table with `from`, `to`, and `weight`. The weights are standardized nodewise interaction estimates combined into an undirected network.

    summary(mixed_net)
    #> <psychnet> mgm network
    #>   nodes: 5   edges: 7   (undirected)
    #>   optimality (KKT residual): 7.58e-08
    #>   edge weight: range [-0.168, 0.632], mean 0.330

The largest edge joins intrinsic value and high self-regulation (`IV`-`SR_hi`, 0.632). Higher intrinsic value is associated with higher conditional odds of belonging to the high self-regulation group after the other nodes have been taken into account. The `CSU`-`IV` and `CSU`-`SE` edges are also positive, at 0.518 and 0.485.

The `SR_hi`-`TA_hi` edge is negative (-0.168). High self-regulation is associated with lower conditional odds of high test anxiety after the continuous learning constructs are considered. Test anxiety has no other retained edge.

Edge scales depend on the types of the connected nodes, so magnitudes should be compared cautiously across Gaussian-Gaussian, Gaussian-binary, and binary-binary pairs. For a binary-binary pair, the implementation retains the sign of the nodewise logistic coefficient. Some mixed-model software reports only its magnitude because the categorical-categorical sign depends on coding convention.

## Checking numerical optimality with `certificate()`

`certificate()` reports the largest stationarity residual among the nodewise models. It returns `method`, `certificate`, `kind`, and `certified`.

    certificate(mixed_net)
    #>   method  certificate kind certified
    #> 1    mgm 7.581922e-08  kkt      TRUE

The residual is \(7.58 \times 10^{-8}\) and `certified` is `TRUE`. It is below the default tolerance of \(10^{-6}\) and indicates that the nodewise penalized models satisfy their numerical conditions within that tolerance. The certificate does not assess measurement quality, sampling stability, or causation.

## Describing node position with `net_centralities()`

`net_centralities()` returns `node`, `strength`, and `expected_influence` by default. Strength is the sum of absolute incident weights. Expected influence is the signed sum.

    net_centralities(mixed_net)
    #>    node  strength expected_influence
    #> 1   CSU 1.4539541          1.4539541
    #> 2    IV 1.3473249          1.3473249
    #> 3    SE 0.8777675          0.8777675
    #> 4 SR_hi 1.4471311          1.1108638
    #> 5 TA_hi 0.1681336         -0.1681336

Cognitive strategy use has the largest strength (1.454), closely followed by high self-regulation (1.447). High self-regulation has expected influence 1.111 because its negative edge with high test anxiety reduces the signed total. High test anxiety has strength 0.168 and expected influence -0.168.

Centrality summarizes the fitted edge pattern. It does not place continuous and binary variables on an identical substantive scale and does not identify causal importance.

## Evaluating predictability with `net_predict()`

`net_predict()` uses a metric matched to each node type. Continuous nodes report \(R^2\), the proportion of variance explained. Binary nodes report normalized classification accuracy (`nCC`) and raw `accuracy`. The function returns `node`, `type`, `metric`, `predictability`, and `accuracy`.

    net_predict(mixed_net, data = mixed_data)
    #>    node     type metric predictability  accuracy
    #> 1   CSU gaussian     R2      0.8131079        NA
    #> 2    IV gaussian     R2      0.7757368        NA
    #> 3    SE gaussian     R2      0.7153497        NA
    #> 4 SR_hi   binary    nCC      0.7391304 0.8800000
    #> 5 TA_hi   binary    nCC      0.1466667 0.5733333

Cognitive strategy use has \(R^2 = 0.813\), intrinsic value has 0.776, and self-efficacy has 0.715. The remaining nodes explain a large proportion of each continuous construct’s in-sample variance.

High self-regulation has `nCC = 0.739` and raw accuracy 0.880, which is a large improvement over its marginal classification baseline. High test anxiety has `nCC = 0.147` and accuracy 0.573. Its neighbours add little classification information beyond the base rate. Validation on excluded observations is needed for claims about performance on new data.

## Sensitivity to the edge-combination rule

`psychnet()` accepts `rule = "OR"` to retain an edge when either nodewise model selects it. The OR rule can produce a denser network when the two directed neighbourhood estimates disagree.

    mixed_or <- psychnet(data = mixed_data, method = "mgm", rule = "OR")
    mixed_or
    #> <psychnet> mgm network
    #>   nodes: 5   edges: 7   (undirected)
    #>   optimality (KKT residual): 7.58e-08

    summary(mixed_or)
    #> <psychnet> mgm network
    #>   nodes: 5   edges: 7   (undirected)
    #>   optimality (KKT residual): 7.58e-08
    #>   edge weight: range [-0.168, 0.632], mean 0.330

The OR analysis returns the same 7 edges and weights in this example. The agreement shows that every retained pair satisfies both combination rules. In other samples, pairs selected in only one direction would appear under OR and be removed under AND.

## Visualizing the network with `cograph::splot()`

`cograph::splot()` draws the fitted object when `cograph` is available. Green and red edges encode positive and negative weights, and predictability rings summarize the node-specific metric.

    cograph::splot(mixed_net, psych_styling = TRUE, predictability = TRUE)

The plot provides orientation, while the edge and predictability tables provide the numerical results. Layout distances have no statistical scale.

## Reporting the analysis

A report should identify each node as continuous or binary, define the binary coding, state the sample size, EBIC setting, threshold, combination rule, and missing-data procedure, and report the edge weights numerically. Predictability must be reported with the correct metric for each node type.

The present analysis used 300 complete observations, three Gaussian nodes, two binary nodes, and the default AND rule. The model retained 7 edges. The largest edge was `IV`-`SR_hi` (0.632), and `SR_hi`-`TA_hi` was negative (-0.168). The KKT residual was \(7.58 \times 10^{-8}\). The OR sensitivity analysis returned the same network.

## How the mixed network is estimated

Every node is regressed on the remaining nodes with a model matched to its type. A continuous outcome uses linear regression and a binary outcome uses logistic regression. An \(\ell_1\) penalty filters weak coefficients, and EBIC selects the penalty separately for each node.

Each pair receives two directed estimates. The edge-combination rule determines whether the pair is retained, and the selected directed estimates are averaged to produce an undirected weight. Continuous variables are standardized during estimation so their arbitrary units do not determine the Gaussian coefficients.

The current implementation supports Gaussian and binary 0/1 nodes. Numeric variables with more than two integer levels require an explicit binary recoding or one-hot encoding before fitting.

## Mathematical foundations

A mixed graphical model is a pairwise Markov random field,

\[ p(x_1,\ldots,x_p) \propto \exp\left( \sum_j\phi_j(x_j)+\sum_{j<k}\phi_{jk}(x_j,x_k) \right). \]

A zero pairwise potential removes the corresponding conditional association. For node \(j\), estimation minimizes

\[ \frac{1}{n}\sum_{i=1}^n \ell\left(x_{ij},\beta_0+\mathbf{x}_{i,-j}^{\mathsf T}\boldsymbol\beta\right) +\lambda_j\lVert\boldsymbol\beta\rVert_1, \]

where \(\ell\) is Gaussian or binomial negative log-likelihood. The certificate is the largest KKT residual across the nodewise objectives.

For continuous nodes, predictability is \(R^2=1-\operatorname{RSS}/\operatorname{TSS}\). For a binary node \(j\),

\[ \operatorname{nCC}_j =\frac{\operatorname{CC}_j-m_j}{1-m_j}, \]

where \(m_j\) is the larger marginal class proportion.