# Bayesian Network and Local Dependence Models

> Note: Most examples in this vignette are shown with `eval=FALSE` to keep CRAN build times short. For full rendered output, see the pkgdown site.

    library(exametrika)
    library(igraph)

## Bayesian Network Model (BNM)

BNM represents conditional probabilities between items in a network format. A Directed Acyclic Graph (DAG) must be provided via `adj_matrix`, `adj_file`, or `g` (igraph object).

### Creating the Graph

    DAG <- matrix(
      c(
        "Item01", "Item02",
        "Item02", "Item03",
        "Item02", "Item04",
        "Item03", "Item05",
        "Item04", "Item05"
      ),
      ncol = 2, byrow = TRUE
    )

    # Graph object
    g <- igraph::graph_from_data_frame(DAG)
    g
    #> IGRAPH f7ca7b5 DN-- 5 5 --
    #> + attr: name (v/c)
    #> + edges from f7ca7b5 (vertex names):
    #> [1] Item01->Item02 Item02->Item03 Item02->Item04 Item03->Item05 Item04->Item05

    # Adjacency matrix
    adj_mat <- as.matrix(igraph::get.adjacency(g))
    print(adj_mat)
    #>        Item01 Item02 Item03 Item04 Item05
    #> Item01      0      1      0      0      0
    #> Item02      0      0      1      1      0
    #> Item03      0      0      0      0      1
    #> Item04      0      0      0      0      1
    #> Item05      0      0      0      0      0

### Running BNM

    result.BNM <- BNM(J5S10, adj_matrix = adj_mat)
    result.BNM
    #> Adjacency Matrix
    #>        Item01 Item02 Item03 Item04 Item05
    #> Item01      0      1      0      0      0
    #> Item02      0      0      1      1      0
    #> Item03      0      0      0      0      1
    #> Item04      0      0      0      0      1
    #> Item05      0      0      0      0      0
    #> [1] "Your graph is an acyclic graph."
    #> [1] "Your graph is connected DAG."

    #>
    #> Parameter Learning
    #>        PIRP 1 PIRP 2 PIRP 3 PIRP 4
    #> Item01  0.600
    #> Item02  0.250    0.5
    #> Item03  0.833    1.0
    #> Item04  0.167    0.5
    #> Item05  0.000    NaN  0.333  0.667
    #>
    #> Conditional Correct Response Rate
    #>    Child Item N of Parents   Parent Items       PIRP Conditional CRR
    #> 1      Item01            0     No Parents No Pattern       0.6000000
    #> 2      Item02            1         Item01          0       0.2500000
    #> 3      Item02            1         Item01          1       0.5000000
    #> 4      Item03            1         Item02          0       0.8333333
    #> 5      Item03            1         Item02          1       1.0000000
    #> 6      Item04            1         Item02          0       0.1666667
    #> 7      Item04            1         Item02          1       0.5000000
    #> 8      Item05            2 Item03, Item04         00       0.0000000
    #> 9      Item05            2 Item03, Item04         01        NaN(0/0)
    #> 10     Item05            2 Item03, Item04         10       0.3333333
    #> 11     Item05            2 Item03, Item04         11       0.6666667
    #>
    #> Model Fit Indices
    #>                  value
    #> model_log_like -27.046
    #> bench_log_like  -8.935
    #> null_log_like  -28.882
    #> model_Chi_sq    36.222
    #> null_Chi_sq     39.894
    #> model_df        20.000
    #> null_df         25.000
    #> NFI              0.092
    #> RFI              0.000
    #> IFI              0.185
    #> TLI              0.000
    #> CFI              0.000
    #> RMSEA            0.300
    #> AIC             -3.778
    #> CAIC           -29.829
    #> BIC             -9.829

### Structure Learning with Genetic Algorithm

`BNM_GA()` searches for a DAG suitable for the data using a genetic algorithm:

    BNM_GA(J5S10,
      population = 20, Rs = 0.5, Rm = 0.002, maxParents = 2,
      maxGeneration = 100, crossover = 2, elitism = 2
    )

### Structure Learning with PBIL

    BNM_PBIL(J5S10,
      population = 20, Rs = 0.5, Rm = 0.005, maxParents = 2,
      alpha = 0.05, estimate = 4
    )

## Local Dependence Latent Rank Analysis (LDLRA)

LDLRA combines LRA and BNM to analyze how item dependency networks change across latent ranks. A graph must be specified for each rank.

### Setting Up Rank-Specific Graphs

Graphs can be provided via CSV file, adjacency matrix list, or igraph object list:

    DAG_dat <- matrix(c(
      "From", "To", "Rank",
      "Item01", "Item02", 1,
      "Item04", "Item05", 1,
      "Item01", "Item02", 2,
      "Item02", "Item03", 2,
      "Item04", "Item05", 2,
      "Item08", "Item09", 2,
      "Item08", "Item10", 2,
      "Item09", "Item10", 2,
      "Item08", "Item11", 2,
      "Item01", "Item02", 3,
      "Item02", "Item03", 3,
      "Item04", "Item05", 3,
      "Item08", "Item09", 3,
      "Item08", "Item10", 3,
      "Item09", "Item10", 3,
      "Item08", "Item11", 3,
      "Item02", "Item03", 4,
      "Item04", "Item06", 4,
      "Item04", "Item07", 4,
      "Item05", "Item06", 4,
      "Item05", "Item07", 4,
      "Item08", "Item10", 4,
      "Item08", "Item11", 4,
      "Item09", "Item11", 4,
      "Item02", "Item03", 5,
      "Item04", "Item06", 5,
      "Item04", "Item07", 5,
      "Item05", "Item06", 5,
      "Item05", "Item07", 5,
      "Item09", "Item11", 5,
      "Item10", "Item11", 5,
      "Item10", "Item12", 5
    ), ncol = 3, byrow = TRUE)

    edgeFile <- tempfile(fileext = ".csv")
    write.csv(DAG_dat, edgeFile, row.names = FALSE, quote = TRUE)

### Running LDLRA

    result.LDLRA <- LDLRA(J12S5000, ncls = 5, adj_file = edgeFile)
    result.LDLRA

    plot(result.LDLRA, type = "IRP", nc = 4, nr = 3)
    plot(result.LDLRA, type = "TRP")
    plot(result.LDLRA, type = "LRD")

### Structure Learning for LDLRA with PBIL

`LDLRA_PBIL()` learns item-interaction graphs for each rank automatically:

    result.LDLRA.PBIL <- LDLRA_PBIL(J35S515,
      seed = 123, ncls = 5, method = "R",
      elitism = 1, successiveLimit = 15
    )
    result.LDLRA.PBIL

## Local Dependence Biclustering (LDB)

LDB combines biclustering with Bayesian network models, analyzing relationships between item fields within each rank.

    conf <- c(
      1, 6, 6, 8, 9, 9, 4, 7, 7, 7, 5, 8, 9, 10, 10,
      9, 9, 10, 10, 10, 2, 2, 3, 3, 5, 5, 6, 9, 9, 10,
      1, 1, 7, 9, 10
    )

    edges_data <- data.frame(
      "From Field (Parent) >>>" = c(
        6, 4, 5, 1, 1, 4,
        3, 4, 6, 2, 4, 4,
        3, 6, 4, 1,
        7, 9, 6, 7
      ),
      ">>> To Field (Child)" = c(
        8, 7, 8, 7, 2, 5,
        5, 8, 8, 4, 6, 7,
        5, 8, 5, 8,
        10, 10, 8, 9
      ),
      "At Class/Rank (Locus)" = c(
        2, 2, 2, 2, 2, 2,
        3, 3, 3, 3, 3, 3,
        4, 4, 4, 4,
        5, 5, 5, 5
      )
    )

    edgeFile <- tempfile(fileext = ".csv")
    write.csv(edges_data, file = edgeFile, row.names = FALSE)

    result.LDB <- LDB(U = J35S515, ncls = 5, conf = conf, adj_file = edgeFile)
    result.LDB

    plot(result.LDB, type = "Array")
    plot(result.LDB, type = "TRP")
    plot(result.LDB, type = "LRD")
    plot(result.LDB, type = "RMP", students = 1:9, nc = 3, nr = 3)
    plot(result.LDB, type = "FRP", nc = 3, nr = 2)

FieldPIRP visualizes correct answer counts for each rank and field:

    plot(result.LDB, type = "FieldPIRP")

## Bicluster Network Model (BINET)

BINET combines biclustering with class-level network analysis. Unlike LDB where nodes are fields, in BINET the nodes represent classes.

    conf <- c(
      1, 5, 5, 5, 9, 9, 6, 6, 6, 6, 2, 7, 7, 11, 11,
      7, 7, 12, 12, 12, 2, 2, 3, 3, 4, 4, 4, 8, 8, 12,
      1, 1, 6, 10, 10
    )

    edges_data <- data.frame(
      "From Class (Parent) >>>" = c(
        1, 2, 3, 4, 5, 7,
        2, 4, 6, 8, 10,
        6, 6, 11, 8, 9, 12
      ),
      ">>> To Class (Child)" = c(
        2, 4, 5, 5, 6, 11,
        3, 7, 9, 12, 12,
        10, 8, 12, 12, 11, 13
      ),
      "At Field (Locus)" = c(
        1, 2, 2, 3, 4, 4,
        5, 5, 5, 5, 5,
        7, 8, 8, 9, 9, 12
      )
    )

    edgeFile <- tempfile(fileext = ".csv")
    write.csv(edges_data, file = edgeFile, row.names = FALSE)

    result.BINET <- BINET(
      U = J35S515, ncls = 13, nfld = 12,
      conf = conf, adj_file = edgeFile
    )
    print(result.BINET)

    plot(result.BINET, type = "Array")
    plot(result.BINET, type = "TRP")
    plot(result.BINET, type = "LRD")
    plot(result.BINET, type = "RMP", students = 1:9, nc = 3, nr = 3)
    plot(result.BINET, type = "FRP", nc = 3, nr = 2)

LDPSR shows Passing Student Rates for locally dependent classes:

    plot(result.BINET, type = "LDPSR", nc = 3, nr = 2)

## Model Comparison

Model | Nodes | Locus | Key Feature
--- | --- | --- | ---
LDLRA | Items | Rank | Item dependencies change across ranks
LDB | Fields | Rank | Field-level dependencies within ranks
BINET | Classes | Field | Class transitions within fields

## Reference

Shojima, K. (2022). Test Data Engineering. Springer.