Test Performance Checking

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Last updated: 2022-03-15

Checks: 7 0

Knit directory: cogstruct/analysis/

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---

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Ignored files:
    Ignored:    .Rhistory
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    Ignored:    bnu/images/
    Ignored:    figure/
    Ignored:    sicnu_pilot/_targets/

Untracked files:
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    Untracked:  bnu/TOL.html
    Untracked:  bnu/TOL_files/
    Untracked:  bnu/config/CharFreq.txt
    Untracked:  bnu/config/characters.xlsx
    Untracked:  bnu/config/outlier_criterion.csv
    Untracked:  bnu/difficulty.xlsx
    Untracked:  bnu/difficulty_digit_reasoning.xlsx
    Untracked:  bnu/items.xlsx
    Untracked:  bnu/items_new_score.xlsx
    Untracked:  code/explore_structure.Rmd
    Untracked:  code/extract_reliability.R
    Untracked:  code/extract_reliability_sicnu_pilot.R
    Untracked:  code/reliability.xlsx
    Untracked:  code/reliability_simple.xlsx
    Untracked:  config/selected_indices.xlsx
    Untracked:  output/prob_rl.tsv
    Untracked:  output/reliability.xlsx
    Untracked:  output/reliability_simple.xlsx
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File	Version	Author	Date	Message
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---

tar_load(indices_clean)
tests_included <- deframe(distinct(indices_clean, game_name_abbr, game_name))

render_content <- function(file, ...) {
  knitr::knit(
    text = knitr::knit_expand(file, ...),
    quiet = TRUE
  )
}
purrr::imap_chr(
  tests_included,
  ~ render_content(
    file = here::here("archetypes/child_check_index.Rmd"),
    game_name_abbr = .x,
    game_name = .y
  )
) |> 
  str_c(collapse = "\n\n") |> 
  cat()

快速归类PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "CRTPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: CRTPro
• Sample Size: 88
• Index Names:
  • nc
  • mrt
  • rtsd

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

捉虫高级简版

data <- indices_clean |>
  filter(
    game_name_abbr == "TOVAS",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: TOVAS
• Sample Size: 89
• Index Names:
  • nc
  • mrt
  • rtsd
  • dprime
  • c
  • commissions
  • omissions

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

塔罗牌

data <- indices_clean |>
  filter(
    game_name_abbr == "WxPred",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: WxPred
• Sample Size: 89
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

数感

data <- indices_clean |>
  filter(
    game_name_abbr == "NsymNCmp",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: NsymNCmp
• Sample Size: 90
• Index Names:
  • pc
  • mrt
  • w

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

数字卡片

data <- indices_clean |>
  filter(
    game_name_abbr == "Digit3back",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Digit3back
• Sample Size: 83
• Index Names:
  • pc
  • mrt
  • dprime
  • c

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

格子卡片

data <- indices_clean |>
  filter(
    game_name_abbr == "Grid2back",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Grid2back
• Sample Size: 83
• Index Names:
  • pc
  • mrt
  • dprime
  • c

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

美术卡片

data <- indices_clean |>
  filter(
    game_name_abbr == "Paint2back",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Paint2back
• Sample Size: 85
• Index Names:
  • pc
  • mrt
  • dprime
  • c

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

文字卡片

data <- indices_clean |>
  filter(
    game_name_abbr == "Verbal3back",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Verbal3back
• Sample Size: 84
• Index Names:
  • pc
  • mrt
  • dprime
  • c

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

魔术师高级

data <- indices_clean |>
  filter(
    game_name_abbr == "Nback3",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Nback3
• Sample Size: 89
• Index Names:
  • pc
  • mrt
  • dprime
  • c

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

变色魔块PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "StopSigPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: StopSigPro
• Sample Size: 88
• Index Names:
  • rt_nth
  • ssrt
  • pc_all
  • pc_go
  • pc_stop

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

多彩文字PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "ColStrpPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: ColStrpPro
• Sample Size: 90
• Index Names:
  • mrt_inc
  • mrt_con
  • pc_inc
  • pc_con
  • cong_eff_rt
  • cong_eff_pc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

卡片分类PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "CardSortPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: CardSortPro
• Sample Size: 86
• Index Names:
  • mrt_repeat
  • mrt_switch
  • pc_repeat
  • pc_switch
  • switch_cost_rt_spe
  • switch_cost_pc_spe

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

候鸟迁徙PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "BirdsPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: BirdsPro
• Sample Size: 90
• Index Names:
  • mrt_inc
  • mrt_con
  • pc_inc
  • pc_con
  • cong_eff_rt
  • cong_eff_pc
  • mrt_repeat
  • mrt_switch
  • pc_repeat
  • pc_switch
  • switch_cost_rt_spe
  • switch_cost_pc_spe

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

方向临摹

data <- indices_clean |>
  filter(
    game_name_abbr == "JLO",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: JLO
• Sample Size: 102
• Index Names:
  • nc
  • mean_ang_err
  • mean_log_err

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

按图索骥

data <- indices_clean |>
  filter(
    game_name_abbr == "HOP",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: HOP
• Sample Size: 102
• Index Names:
  • mean_dist_err_allo
  • mean_dist_err_both
  • mean_dist_err_ego
  • mean_log_err_allo
  • mean_log_err_both
  • mean_log_err_ego

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

萤火虫PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "MOTPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: MOTPro
• Sample Size: 102
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

火眼金睛

data <- indices_clean |>
  filter(
    game_name_abbr == "AttSrc",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: AttSrc
• Sample Size: 102
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

位置记忆PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "SSTMPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SSTMPro
• Sample Size: 102
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

宇宙黑洞

data <- indices_clean |>
  filter(
    game_name_abbr == "LocMemStd",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: LocMemStd
• Sample Size: 102
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

过目不忘PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "FWSPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FWSPro
• Sample Size: 102
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

人工语言-中级

data <- indices_clean |>
  filter(
    game_name_abbr == "AscLanMd",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: AscLanMd
• Sample Size: 102
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

各得其所

data <- indices_clean |>
  filter(
    game_name_abbr == "LdnTwr",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: LdnTwr
• Sample Size: 98
• Index Names:
  • prop_perfect
  • mrt_init

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

连点成画PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "FPTPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FPTPro
• Sample Size: 102
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

蝴蝶照相机

data <- indices_clean |>
  filter(
    game_name_abbr == "SCSpan",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SCSpan
• Sample Size: 102
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

密码箱

data <- indices_clean |>
  filter(
    game_name_abbr == "KeepTrack",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: KeepTrack
• Sample Size: 102
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

超级秒表PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "SRTPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SRTPro
• Sample Size: 99
• Index Names:
  • mrt
  • rtsd

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

一心二用PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "DualTaskPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: DualTaskPro
• Sample Size: 99
• Index Names:
  • nc
  • mrt
  • rtsd
  • dprime
  • c
  • commissions
  • omissions

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

连续再认

data <- indices_clean |>
  filter(
    game_name_abbr == "ConRec",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: ConRec
• Sample Size: 99
• Index Names:
  • nc
  • mrt
  • rtsd
  • dprime
  • c
  • commissions
  • omissions

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

图片记忆

data <- indices_clean |>
  filter(
    game_name_abbr == "BPS",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: BPS
• Sample Size: 99
• Index Names:
  • pc
  • p_sim_foil
  • p_sim_lure
  • p_sim_target
  • bps_score

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

言语记忆

data <- indices_clean |>
  filter(
    game_name_abbr == "DRM",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: DRM
• Sample Size: 100
• Index Names:
  • tm_dprime
  • tm_bias
  • fm_dprime
  • fm_bias

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

强化学习

data <- indices_clean |>
  filter(
    game_name_abbr == "ProbRL",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: ProbRL
• Sample Size: 99
• Index Names:
  • pc_approach
  • pc_avoid
  • pc_test

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

语义判断

data <- indices_clean |>
  filter(
    game_name_abbr == "Seman",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Seman
• Sample Size: 99
• Index Names:
  • nc
  • mrt
  • rtsd
  • dprime
  • c
  • commissions
  • omissions

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

声调判断

data <- indices_clean |>
  filter(
    game_name_abbr == "Tone",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Tone
• Sample Size: 98
• Index Names:
  • nc
  • mrt
  • rtsd
  • dprime
  • c
  • commissions
  • omissions

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

太空飞船PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "FlkrPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FlkrPro
• Sample Size: 99
• Index Names:
  • mrt_inc
  • mrt_con
  • pc_inc
  • pc_con
  • cong_eff_rt
  • cong_eff_pc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

察颜观色PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "FacesPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FacesPro
• Sample Size: 99
• Index Names:
  • mrt_inc
  • mrt_con
  • pc_inc
  • pc_con
  • cong_eff_rt
  • cong_eff_pc
  • mrt_repeat
  • mrt_switch
  • pc_repeat
  • pc_switch
  • switch_cost_rt_spe
  • switch_cost_pc_spe

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

顺背数PRO

data <- indices_clean |>
  filter(
    game_name_abbr == "FDSPro",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FDSPro
• Sample Size: 99
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

图形推理

data <- indices_clean |>
  filter(
    game_name_abbr == "NVR",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: NVR
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

数字推理

data <- indices_clean |>
  filter(
    game_name_abbr == "DR",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: DR
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

文字推理

data <- indices_clean |>
  filter(
    game_name_abbr == "VR",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: VR
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

三维心理旋转测试

data <- indices_clean |>
  filter(
    game_name_abbr == "MR3D",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: MR3D
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

时长分辨

data <- indices_clean |>
  filter(
    game_name_abbr == "DD",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: DD
• Sample Size: 99
• Index Names:
  • threshold

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

时间顺序判断

data <- indices_clean |>
  filter(
    game_name_abbr == "TOJ",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: TOJ
• Sample Size: 99
• Index Names:
  • threshold

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

节奏感知

data <- indices_clean |>
  filter(
    game_name_abbr == "RP",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: RP
• Sample Size: 99
• Index Names:
  • threshold

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

专注大师_中级

data <- indices_clean |>
  filter(
    game_name_abbr == "CalcSpdMed",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: CalcSpdMed
• Sample Size: 94
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

眼耳并用

data <- indices_clean |>
  filter(
    game_name_abbr == "Dual2back",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Dual2back
• Sample Size: 99
• Index Names:
  • pc_aud
  • pc_both
  • pc_vis
  • mrt_aud
  • mrt_both
  • mrt_vis
  • dprime_aud
  • dprime_both
  • dprime_vis
  • c_aud
  • c_both
  • c_vis

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

变戏法

data <- indices_clean |>
  filter(
    game_name_abbr == "AntiSac",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: AntiSac
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

舒尔特方格（中级）

data <- indices_clean |>
  filter(
    game_name_abbr == "SchulteMed",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SchulteMed
• Sample Size: 99
• Index Names:
  • nc_cor

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

欢乐餐厅

data <- indices_clean |>
  filter(
    game_name_abbr == "AscMem",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: AscMem
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

登陆月球（中级）

data <- indices_clean |>
  filter(
    game_name_abbr == "NLEMed",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: NLEMed
• Sample Size: 99
• Index Names:
  • mean_abs_err
  • mean_log_err

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

速算师（中级）

data <- indices_clean |>
  filter(
    game_name_abbr == "CalcMed",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: CalcMed
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

图形折叠

data <- indices_clean |>
  filter(
    game_name_abbr == "FR",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FR
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

远距离联想

data <- indices_clean |>
  filter(
    game_name_abbr == "RAT",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: RAT
• Sample Size: 99
• Index Names:
  • nc

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

幸运小球

data <- indices_clean |>
  filter(
    game_name_abbr == "OCSpan",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: OCSpan
• Sample Size: 99
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

打靶场

data <- indices_clean |>
  filter(
    game_name_abbr == "RSpan",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: RSpan
• Sample Size: 99
• Index Names:
  • nc
  • max_span
  • mean_span_pcu
  • mean_span_anu

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

雪花收藏家

data <- indices_clean |>
  filter(
    game_name_abbr == "DirectSrc",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: DirectSrc
• Sample Size: 99
• Index Names:
  • nc_cor

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

我是大厨

data <- indices_clean |>
  filter(
    game_name_abbr == "MSynWin",
    if_all(contains("test"), ~ !is.infinite(.x)),
    !(is.na(test) & is.na(retest))
  ) |>
  group_by(index_name) |>
  mutate(
    across(
      contains("test"),
      list(
        is_outlier = ~ .x %in% boxplot.stats(.x)$out
      )
    )
  ) |>
  ungroup() |>
  mutate(
    is_outlier = test_is_outlier | retest_is_outlier,
    .keep = "unused"
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: MSynWin
• Sample Size: 99
• Index Names:
  • score_total
  • score_aud
  • score_mem
  • score_vis

Pairwise Correlation of indices

data |>
  pivot_wider(
    id_cols = user_id,
    names_from = index_name,
    values_from = test
  ) |>
  select(-user_id) |>
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |>
  group_by(index_name) |>
  group_modify(
    ~ tibble(
      n = nrow(.x),
      n_no_outlier = .x |>
        filter(!is_outlier) |>
        nrow(),
      icc = .x |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      icc_no_outlier = .x |>
        filter(!is_outlier) |>
        select(contains("test")) |>
        psych::ICC() |>
        pluck("results", "ICC", 2),
      r = cor(.x$test, .x$retest),
      r_no_outlier = .x |>
        filter(!is_outlier) |>
        summarise(r = cor(test, retest)) |>
        pull(r)
    )
  ) |>
  ungroup()
data_test_retest |>
  ggpubr::ggscatter("test", "retest", color = "is_outlier") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf,
      label = str_glue(
        "N = {n} ({n_no_outlier})\n",
        "r = {round(r, 2)} ({round(r_no_outlier, 2)})\n",
        "icc = {round(icc, 2)} ({round(icc_no_outlier, 2)})"
      )
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  scale_color_grey() +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1) +
  labs(
    color = "Outlier",
    caption = "Values in brackets are those without outliers."
  )

Session information

sessionInfo()

R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
 [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
 [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
[10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] targets_0.10.0  forcats_0.5.1   stringr_1.4.0   dplyr_1.0.8    
 [5] purrr_0.3.4     readr_2.1.2     tidyr_1.2.0     tibble_3.1.6   
 [9] ggplot2_3.3.5   tidyverse_1.3.1 workflowr_1.7.0

loaded via a namespace (and not attached):
  [1] minqa_1.2.4         colorspace_2.0-3    ggsignif_0.6.3     
  [4] ellipsis_0.3.2      rprojroot_2.0.2     fs_1.5.2           
  [7] rstudioapi_0.13     ggpubr_0.4.0        farver_2.1.0       
 [10] fansi_1.0.2         lubridate_1.8.0     xml2_1.3.3         
 [13] codetools_0.2-18    splines_4.1.2       mnormt_2.0.2       
 [16] knitr_1.37          jsonlite_1.8.0      nloptr_2.0.0       
 [19] broom_0.7.12        dbplyr_2.1.1        compiler_4.1.2     
 [22] httr_1.4.2          backports_1.4.1     assertthat_0.2.1   
 [25] Matrix_1.4-0        fastmap_1.1.0       cli_3.2.0          
 [28] later_1.3.0         htmltools_0.5.2     tools_4.1.2        
 [31] igraph_1.2.11       gtable_0.3.0        glue_1.6.2         
 [34] Rcpp_1.0.8          carData_3.0-5       cellranger_1.1.0   
 [37] jquerylib_0.1.4     vctrs_0.3.8         nlme_3.1-155       
 [40] psych_2.1.9         xfun_0.30           ps_1.6.0           
 [43] lme4_1.1-28         rvest_1.0.2         lifecycle_1.0.1    
 [46] rstatix_0.7.0       getPass_0.2-2       MASS_7.3-55        
 [49] scales_1.1.1        hms_1.1.1           promises_1.2.0.1   
 [52] parallel_4.1.2      RColorBrewer_1.1-2  qs_0.25.3          
 [55] yaml_2.3.5          sass_0.4.0          reshape_0.8.8      
 [58] stringi_1.7.6       highr_0.9           boot_1.3-28        
 [61] rlang_1.0.2         pkgconfig_2.0.3     evaluate_0.15      
 [64] lattice_0.20-45     labeling_0.4.2      processx_3.5.2     
 [67] tidyselect_1.1.2    here_1.0.1          GGally_2.1.2       
 [70] plyr_1.8.6          magrittr_2.0.2      R6_2.5.1           
 [73] generics_0.1.2      base64url_1.4       DBI_1.1.2          
 [76] pillar_1.7.0        haven_2.4.3         whisker_0.4        
 [79] withr_2.5.0         abind_1.4-5         modelr_0.1.8       
 [82] crayon_1.5.0        car_3.0-12          utf8_1.2.2         
 [85] tmvnsim_1.0-2       RApiSerialize_0.1.0 tzdb_0.2.0         
 [88] rmarkdown_2.12      grid_4.1.2          readxl_1.3.1       
 [91] data.table_1.14.2   callr_3.7.0         git2r_0.29.0       
 [94] reprex_2.0.1        digest_0.6.29       httpuv_1.6.5       
 [97] RcppParallel_5.1.5  munsell_0.5.0       stringfish_0.15.5  
[100] bslib_0.3.1