tests/testthat/test-effects_preprocessing_DyNAMi_choice.R

In goldfish: Statistical Network Models for Dynamic Network Data

# context("Effects for model = 'DyNAMi' and subModel = 'choice'")


# test inertia and tie with different subtypes ----
test_that(
  "inertia/tie with objects weighted with all possible options",
  {
    preproData <- estimate(
      dependent.depevents_DyNAMi ~
        inertia(past_network_DyNAMi, weighted = TRUE, subType = "count") +
        tie(covnetwork_DyNAMi, weighted = TRUE, subType = "count") +
        inertia(past_network_DyNAMi, weighted = TRUE, subType = "proportion") +
        tie(covnetwork_DyNAMi, weighted = TRUE, subType = "proportion") +
        inertia(past_network_DyNAMi, weighted = TRUE, subType = "presence") +
        tie(covnetwork_DyNAMi, weighted = TRUE, subType = "presence") +
        inertia(past_network_DyNAMi, weighted = TRUE, subType = "min") +
        tie(covnetwork_DyNAMi, weighted = TRUE, subType = "min") +
        inertia(past_network_DyNAMi, weighted = TRUE, subType = "mean") +
        tie(covnetwork_DyNAMi, weighted = TRUE, subType = "mean") +
        inertia(past_network_DyNAMi, weighted = TRUE, subType = "max") +
        tie(covnetwork_DyNAMi, weighted = TRUE, subType = "max"),
      model = "DyNAMi", subModel = "choice",
      preprocessingOnly = TRUE
    )

    updFun <- function(stat, change) {
      if (!is.null(change)) {
        stat[cbind(change[, "node1"], change[, "node2"])] <- change[, "replace"]
      }
      return(stat)
    }

    for (i in c(1, 3, 5, 7, 9, 11)) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 0, 0, 0,
          0, 0, 0, 0,
          0, 0, 0, 0,
          0, 0, 0, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix inertia"
      )
    }
    for (i in c(2, 4, 6, 8, 10, 12)) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 1, 1, 0,
          1, 0, 1, 0,
          1, 1, 0, 0,
          0, 0, 0, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix tie"
      )
    }


    # Detail for inertia/count
    stat <- preproData$initialStats[, , 1]
    change <- preproData$dependentStatsChange[[2]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 1, 0, 0,
        0, 1, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 1, 0,
        1, 1, 1, 0,
        1, 2, 0, 0,
        1, 1, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia (4/4)"
    )


    # Detail for tie/count
    stat <- preproData$initialStats[, , 2]
    change <- preproData$dependentStatsChange[[2]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        0, 1, 1, 0,
        0, 2, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 0, 1, 0,
        1, 1, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie (4/4)"
    )


    # Final stats for other inertia effects:
    #  proportion, presence, min, mean, mean
    stat <- preproData$initialStats[, , 3]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[3]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 1, 1, 0,
        1, 1, 0, 0,
        1, 1, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia proportion"
    )
    stat <- preproData$initialStats[, , 5]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[5]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 1, 1, 0,
        1, 1, 0, 0,
        1, 1, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia presence"
    )
    stat <- preproData$initialStats[, , 7]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[7]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 1, 1, 0,
        1, 1, 0, 0,
        1, 1, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia min"
    )
    stat <- preproData$initialStats[, , 9]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[9]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 1, 1, 0,
        1, 1, 0, 0,
        1, 1, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia mean"
    )
    stat <- preproData$initialStats[, , 11]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[11]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 1, 1, 0,
        1, 1, 0, 0,
        1, 1, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia max"
    )


    # Final stats for other tie effects: proportion, presence, min, mean, mean
    stat <- preproData$initialStats[, , 4]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[4]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0.5, 1, 0,
        1, 0, 1, 0,
        1, 0.5, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie proportion"
    )
    stat <- preproData$initialStats[, , 6]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[6]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 0, 1, 0,
        1, 1, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie presence"
    )
    stat <- preproData$initialStats[, , 8]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[8]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0, 1, 0,
        1, 0, 1, 0,
        1, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie min"
    )
    stat <- preproData$initialStats[, , 10]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[10]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0.5, 1, 0,
        1, 0, 1, 0,
        1, 0.5, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie mean"
    )
    stat <- preproData$initialStats[, , 12]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[12]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 1, 1, 0,
        1, 0, 1, 0,
        1, 1, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix tie max"
    )
  }
)


# test inertia with a window ----
test_that(
  "inertia computes correct preprocessing objects with window",
  {
    preproData <- estimate(
      dependent.depevents_DyNAMi ~
        inertia(past_network_DyNAMi, weighted = TRUE) +
        inertia(past_network_DyNAMi, window = 2, weighted = TRUE) +
        inertia(past_network_DyNAMi, window = 7, weighted = TRUE),
      model = "DyNAMi", subModel = "choice",
      preprocessingOnly = TRUE
    )

    updFun <- function(stat, change) {
      if (!is.null(change)) {
        stat[cbind(change[, "node1"], change[, "node2"])] <- change[, "replace"]
      }
      return(stat)
    }

    # check intitialization
    for (i in c(1, 2, 3)) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 0, 0, 0,
          0, 0, 0, 0,
          0, 0, 0, 0,
          0, 0, 0, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix inertia with windows"
      )
    }

    # Detail for window 2s
    stat <- preproData$initialStats[, , 2]
    change <- preproData$dependentStatsChange[[2]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia window (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 0.5, 0, 0,
        0, 0.5, 0, 0,
        0, 1, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia window (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia window (4/4)"
    )


    # Detail for window 7s
    stat <- preproData$initialStats[, , 3]
    change <- preproData$dependentStatsChange[[2]][[3]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 1, 0, 0,
        0, 1, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia window (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[3]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 1, 0, 0,
        0, 1, 0, 0,
        0, 1, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia window (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[3]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix inertia window (4/4)"
    )
  }
)


# test alterpop and alterdeg ----
test_that(
  "alterpop/alterdeg with objects weighted with all possible options",
  {
    preproData <- estimate(
      dependent.depevents_DyNAMi ~
        alterpop(past_network_DyNAMi,
          weighted = TRUE, subType = "mean_normalized"
        ) +
        alterdeg(covnetwork_DyNAMi,
          weighted = TRUE, subType = "mean_normalized"
        ) +
        alterpop(past_network_DyNAMi, weighted = TRUE, subType = "min") +
        alterdeg(covnetwork_DyNAMi, weighted = TRUE, subType = "min") +
        alterpop(past_network_DyNAMi, weighted = TRUE, subType = "mean") +
        alterdeg(covnetwork_DyNAMi, weighted = TRUE, subType = "mean") +
        alterpop(past_network_DyNAMi, weighted = TRUE, subType = "max") +
        alterdeg(covnetwork_DyNAMi, weighted = TRUE, subType = "max") +
        alterpop(past_network_DyNAMi,
          weighted = TRUE, subType = "mean_centered"
        ) +
        alterdeg(covnetwork_DyNAMi,
          weighted = TRUE, subType = "mean_centered"
        ),
      model = "DyNAMi", subModel = "choice",
      preprocessingOnly = TRUE
    )

    updFun <- function(stat, change) {
      if (!is.null(change)) {
        stat[cbind(change[, "node1"], change[, "node2"])] <- change[, "replace"]
      }
      return(stat)
    }

    for (i in c(1, 3, 5, 7, 9)) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 0, 0, 0,
          0, 0, 0, 0,
          0, 0, 0, 0,
          0, 0, 0, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix alterpop"
      )
    }
    for (i in c(2, 10)) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 0.5, 0.5, -1.5,
          0.5, 0, 0.5, -1.5,
          0.5, 0.5, 0, -1.5,
          0.5, 0.5, 0.5, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix alterdeg"
      )
    }
    for (i in c(4, 6, 8)) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 2, 2, 0,
          2, 0, 2, 0,
          2, 2, 0, 0,
          2, 2, 2, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix alterdeg"
      )
    }


    # Detail for alterpop mean
    stat <- preproData$initialStats[, , 5]
    change <- preproData$dependentStatsChange[[2]][[5]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 1, 0, 0,
        0, 1, 0, 0,
        0, 1, 0, 0,
        0, 1, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterpop (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[5]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 2, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterpop (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[5]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 3, 3, 0,
        3, 3, 3, 0,
        3, 3, 0, 0,
        3, 3, 3, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterpop (4/4)"
    )


    # Detail for alterdeg mean
    stat <- preproData$initialStats[, , 6]
    change <- preproData$dependentStatsChange[[2]][[6]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 2, 0,
        0, 2, 2, 0,
        0, 2, 0, 0,
        0, 2, 2, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterdeg (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[6]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 2, 0, 0,
        0, 2, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterdeg (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[6]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 1, 2, 0,
        2, 0, 2, 0,
        2, 1, 0, 0,
        2, 2, 2, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterdeg (4/4)"
    )



    # Final stats for other inertia effects:
    #  mean_normalized, min, max, mean_centered
    stat <- preproData$initialStats[, , 1]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[1]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterpop mean centered"
    )
    stat <- preproData$initialStats[, , 2]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[2]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, -0.5, 0.5, 0,
        0.5, -1.5, 0.5, 0,
        0.5, -0.5, 0, 0,
        0.5, 0.5, 0.5, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterdeg mean centered"
    )
    stat <- preproData$initialStats[, , 3]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[3]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 3, 3, 0,
        3, 3, 3, 0,
        3, 3, 0, 0,
        3, 3, 3, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterpop min"
    )
    stat <- preproData$initialStats[, , 4]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[4]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0, 2, 0,
        2, 0, 2, 0,
        2, 0, 0, 0,
        2, 2, 2, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterdeg min"
    )
    stat <- preproData$initialStats[, , 7]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[7]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 3, 3, 0,
        3, 3, 3, 0,
        3, 3, 0, 0,
        3, 3, 3, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterpop max"
    )
    stat <- preproData$initialStats[, , 8]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[8]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 2, 2, 0,
        2, 0, 2, 0,
        2, 2, 0, 0,
        2, 2, 2, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterdeg max"
    )
    stat <- preproData$initialStats[, , 9]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[9]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterpop mean centered"
    )
    stat <- preproData$initialStats[, , 10]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[10]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, -0.5, 0.5, 0,
        0.5, -1.5, 0.5, 0,
        0.5, -0.5, 0, 0,
        0.5, 0.5, 0.5, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alterdeg mean centered"
    )
  }
)


# test size ----
test_that(
  "size with objects weighted with all possible options",
  {
    preproData <- estimate(
      dependent.depevents_DyNAMi ~
        size(interaction_network_DyNAMi, subType = "identity") +
        size(interaction_network_DyNAMi, subType = "squared"),
      model = "DyNAMi", subModel = "choice",
      preprocessingOnly = TRUE
    )

    updFun <- function(stat, change) {
      if (!is.null(change)) {
        stat[cbind(change[, "node1"], change[, "node2"])] <- change[, "replace"]
      }
      return(stat)
    }

    expect_equal(preproData$initialStats[, , 1],
      matrix(c(
        1, 1, 1, 1,
        1, 1, 1, 1,
        1, 1, 1, 1,
        1, 1, 1, 1
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alterdeg"
    )
    expect_equal(preproData$initialStats[, , 2],
      matrix(c(
        1, 1, 1, 1,
        1, 1, 1, 1,
        1, 1, 1, 1,
        1, 1, 1, 1
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alterdeg"
    )

    # Detail for size
    stat <- preproData$initialStats[, , 1]
    change <- preproData$dependentStatsChange[[2]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 1, 1,
        0, 2, 1, 1,
        0, 2, 1, 1,
        0, 2, 1, 1
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 3, 0, 1,
        0, 3, 0, 1,
        0, 3, 0, 1,
        0, 3, 0, 1
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        1, 2, 1, 0,
        1, 2, 1, 0,
        1, 2, 1, 0,
        1, 2, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (4/4)"
    )

    # Detail for size squared
    stat <- preproData$initialStats[, , 2]
    change <- preproData$dependentStatsChange[[2]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 4, 1, 1,
        0, 4, 1, 1,
        0, 4, 1, 1,
        0, 4, 1, 1
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 9, 0, 1,
        0, 9, 0, 1,
        0, 9, 0, 1,
        0, 9, 0, 1
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[2]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        1, 4, 1, 0,
        1, 4, 1, 0,
        1, 4, 1, 0,
        1, 4, 1, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (4/4)"
    )
  }
)


# test alter ----
test_that(
  "alter with objects weighted with all possible options",
  {
    preproData <- estimate(
      dependent.depevents_DyNAMi ~ alter(actors_DyNAMi$attr1, subType = "mean")
        + alter(actors_DyNAMi$attr1, subType = "mean_normalized")
        + alter(actors_DyNAMi$attr1, subType = "mean_squared")
        + alter(actors_DyNAMi$attr1, subType = "min")
        + alter(actors_DyNAMi$attr1, subType = "max")
        + alter(actors_DyNAMi$attr1, subType = "range")
        + alter(actors_DyNAMi$attr1, subType = "mean_centered"),
      model = "DyNAMi", subModel = "choice",
      preprocessingOnly = TRUE
    )

    updFun <- function(stat, change) {
      if (!is.null(change)) {
        stat[cbind(change[, "node1"], change[, "node2"])] <- change[, "replace"]
      }
      return(stat)
    }

    expect_equal(preproData$initialStats[, , 1],
      matrix(c(
        0, 22, 26, 30,
        20, 0, 26, 30,
        20, 22, 0, 30,
        20, 22, 26, 0
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alter mean"
    )
    s <- sd(c(20, 22, 26, 30))
    m <- mean(c(20, 22, 26, 30))
    c <- c((20 - m) / s, (22 - m) / s, (26 - m) / s, (30 - m) / s)
    expect_equal(preproData$initialStats[, , 2],
      matrix(c(
        0, c[2], c[3], c[4],
        c[1], 0, c[3], c[4],
        c[1], c[2], 0, c[4],
        c[1], c[2], c[3], 0
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alter mean normalized"
    )
    expect_equal(preproData$initialStats[, , 3],
      matrix(c(
        0, 484, 676, 900,
        400, 0, 676, 900,
        400, 484, 0, 900,
        400, 484, 676, 0
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alter mean"
    )
    expect_equal(preproData$initialStats[, , 4],
      matrix(c(
        0, 22, 26, 30,
        20, 0, 26, 30,
        20, 22, 0, 30,
        20, 22, 26, 0
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alter min"
    )
    expect_equal(preproData$initialStats[, , 5],
      matrix(c(
        0, 22, 26, 30,
        20, 0, 26, 30,
        20, 22, 0, 30,
        20, 22, 26, 0
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alter max"
    )
    expect_equal(preproData$initialStats[, , 6],
      matrix(c(
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alter range"
    )
    expect_equal(preproData$initialStats[, , 7],
      matrix(c(
        0, 22 - m, 26 - m, 30 - m,
        20 - m, 0, 26 - m, 30 - m,
        20 - m, 22 - m, 0, 30 - m,
        20 - m, 22 - m, 26 - m, 0
      ), 4, 4, TRUE),
      label = "initialization of the statistics matrix alter mean centered"
    )


    # Detail for alter mean
    stat <- preproData$initialStats[, , 1]
    change <- preproData$dependentStatsChange[[2]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 22, 26, 30,
        0, 20, 26, 30,
        0, 21, 0, 30,
        0, 21, 26, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 24, 0, 30,
        0, 23, 0, 30,
        0, 21, 0, 30,
        0, 68 / 3, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 26, 26, 0,
        20, 30, 26, 0,
        20, 26, 0, 0,
        20, 22, 26, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix size (4/4)"
    )


    # Final stats for other alter effects:
    #  mean_normalized, mean_squared,  min, max, range, mean_centered
    stat <- preproData$initialStats[, , 2]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[2]]
      stat <- updFun(stat, change)
    }
    c <- c((20 - m) / s, (22 - m) / s, (26 - m) / s, (30 - m) / s)
    expect_equal(stat,
      matrix(c(
        0, c[3], c[3], 0,
        c[1], c[4], c[3], 0,
        c[1], c[3], 0, 0,
        c[1], c[2], c[3], 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alter mean normalized"
    )
    stat <- preproData$initialStats[, , 3]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[3]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 676, 676, 0,
        400, 900, 676, 0,
        400, 676, 0, 0,
        400, 484, 676, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alter mean squared"
    )
    stat <- preproData$initialStats[, , 4]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[4]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 22, 26, 0,
        20, 30, 26, 0,
        20, 22, 0, 0,
        20, 22, 26, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alter min"
    )
    stat <- preproData$initialStats[, , 5]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[5]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 30, 26, 0,
        20, 30, 26, 0,
        20, 30, 0, 0,
        20, 22, 26, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alter max"
    )
    stat <- preproData$initialStats[, , 6]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[6]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 8, 0, 0,
        0, 0, 0, 0,
        0, 8, 0, 0,
        0, 0, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alter max"
    )
    stat <- preproData$initialStats[, , 7]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[7]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 26 - m, 26 - m, 0,
        20 - m, 30 - m, 26 - m, 0,
        20 - m, 26 - m, 0, 0,
        20 - m, 22 - m, 26 - m, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix alter mean centered"
    )
  }
)



# test same/diff/sim ----
test_that(
  "same/diff/sim with objects weighted with all possible options",
  {
    preproData <- estimate(
      dependent.depevents_DyNAMi ~
        same(actors_DyNAMi$attr2, subType = "proportion")
        + same(actors_DyNAMi$attr2, subType = "count")
        + same(actors_DyNAMi$attr2, subType = "presence")
        + diff(actors_DyNAMi$attr1, subType = "averaged_sum")
        + diff(actors_DyNAMi$attr1, subType = "mean")
        + diff(actors_DyNAMi$attr1, subType = "min")
        + diff(actors_DyNAMi$attr1, subType = "max")
        + sim(actors_DyNAMi$attr1, subType = "averaged_sum")
        + sim(actors_DyNAMi$attr1, subType = "mean")
        + sim(actors_DyNAMi$attr1, subType = "min")
        + sim(actors_DyNAMi$attr1, subType = "max"),
      model = "DyNAMi", subModel = "choice",
      preprocessingOnly = TRUE
    )

    updFun <- function(stat, change) {
      if (!is.null(change)) {
        stat[cbind(change[, "node1"], change[, "node2"])] <- change[, "replace"]
      }
      return(stat)
    }

    for (i in 1:3) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 0, 1, 0,
          0, 0, 0, 1,
          1, 0, 0, 0,
          0, 1, 0, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix sim"
      )
    }
    for (i in 4:7) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, 2, 6, 10,
          2, 0, 4, 8,
          6, 4, 0, 4,
          10, 8, 4, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix diff"
      )
    }
    for (i in 8:11) {
      expect_equal(preproData$initialStats[, , i],
        matrix(c(
          0, -2, -6, -10,
          -2, 0, -4, -8,
          -6, -4, 0, -4,
          -10, -8, -4, 0
        ), 4, 4, TRUE),
        label = "initialization of the statistics matrix same"
      )
    }


    # Detail for sim proportion
    stat <- preproData$initialStats[, , 1]
    change <- preproData$dependentStatsChange[[2]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 0, 1, 0,
        0, 0, 0, 1,
        0, 0.5, 0, 0,
        0, 0.5, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 0.5, 0, 0,
        0, 0, 0, 1,
        0, 0.5, 0, 0,
        0, 1 / 3, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[1]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 0, 1, 0,
        0, 1, 0, 0,
        1, 0, 0, 0,
        0, 1, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same (4/4)"
    )

    # Detail for diff averaged sum
    stat <- preproData$initialStats[, , 4]
    change <- preproData$dependentStatsChange[[2]][[4]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 2, 6, 10,
        0, 2, 4, 8,
        0, 5, 0, 4,
        0, 9, 4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix diff (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[4]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 4, 0, 10,
        0, 3, 0, 8,
        0, 5, 0, 4,
        0, 22 / 3, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix diff (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[4]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, 6, 6, 0,
        2, 8, 4, 0,
        6, 4, 0, 0,
        10, 8, 4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix diff (4/4)"
    )

    # Detail for diff averaged sum
    stat <- preproData$initialStats[, , 8]
    change <- preproData$dependentStatsChange[[2]][[8]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, -2, -6, -10,
        0, -2, -4, -8,
        0, -5, 0, -4,
        0, -9, -4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same (2/4)"
    )
    change <- preproData$dependentStatsChange[[3]][[8]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, -4, 0, -10,
        0, -3, 0, -8,
        0, -5, 0, -4,
        0, -22 / 3, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same (3/4)"
    )
    change <- preproData$dependentStatsChange[[4]][[8]]
    stat <- updFun(stat, change)
    expect_equal(stat,
      matrix(c(
        0, -6, -6, 0,
        -2, -8, -4, 0,
        -6, -4, 0, 0,
        -10, -8, -4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same (4/4)"
    )

    # Final stats for other effects
    stat <- preproData$initialStats[, , 2]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[2]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0, 1, 0,
        0, 1, 0, 0,
        1, 0, 0, 0,
        0, 1, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same count"
    )
    stat <- preproData$initialStats[, , 3]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[3]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 0, 1, 0,
        0, 1, 0, 0,
        1, 0, 0, 0,
        0, 1, 0, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix same presence"
    )
    stat <- preproData$initialStats[, , 5]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[5]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 6, 6, 0,
        2, 8, 4, 0,
        6, 0, 0, 0,
        10, 8, 4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix diff mean"
    )
    stat <- preproData$initialStats[, , 6]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[6]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 2, 6, 0,
        2, 8, 4, 0,
        6, 4, 0, 0,
        10, 8, 4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix diff min"
    )
    stat <- preproData$initialStats[, , 7]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[7]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, 10, 6, 0,
        2, 8, 4, 0,
        6, 4, 0, 0,
        10, 8, 4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix diff max"
    )
    stat <- preproData$initialStats[, , 9]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[9]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, -6, -6, 0,
        -2, -8, -4, 0,
        -6, 0, 0, 0,
        -10, -8, -4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix sim mean"
    )
    stat <- preproData$initialStats[, , 10]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[10]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, -2, -6, 0,
        -2, -8, -4, 0,
        -6, -4, 0, 0,
        -10, -8, -4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix sim min"
    )
    stat <- preproData$initialStats[, , 11]
    for (t in 1:4) {
      change <- preproData$dependentStatsChange[[t]][[11]]
      stat <- updFun(stat, change)
    }
    expect_equal(stat,
      matrix(c(
        0, -10, -6, 0,
        -2, -8, -4, 0,
        -6, -4, 0, 0,
        -10, -8, -4, 0
      ), 4, 4, TRUE),
      label = "update of the statistics matrix sim max"
    )
  }
)