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Add the following lines to .Renviron
using usethis::edit_r_environ()
:
Metaculus_username="yourUsername"
Metaculus_password="yourPassword"
Then, login:
questions_myPredictions_byDiff %>%
dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_me_q2_abs_odds) %>%
dplyr::mutate(diff_me_q2_abs_odds = round(diff_me_q2_abs_odds, 1)) %>%
dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
head() %>%
knitr::kable()
id | title | my_prediction | community_q2 | community_q2_pre_me | diff_me_q2_abs_odds |
---|---|---|---|---|---|
1634 | Will US Income Inequality Increase by 2025? | 0.01 | 0.51 | 0.53 | 103.0 |
1332 | Will global malaria mortality rates be reduced by 90% when compared with 2015 rates, by 2030? | 0.01 | 0.44 | 0.46 | 77.8 |
10574 | Will a 8-year gross world product doubling complete before a 2-year doubling does? | 0.99 | 0.66 | 0.75 | 51.0 |
8334 | Will spending on US office construction be less than $77 Billion USD in 2022? | 0.01 | 0.30 | 0.30 | 42.4 |
10084 | Will Sweden join NATO before 2024? | 0.14 | 0.87 | 0.42 | 41.1 |
10085 | Will Finland join NATO before 2024? | 0.26 | 0.90 | 0.83 | 25.6 |
questions_myPredictions_byDiff %>%
dplyr::arrange(dplyr::desc(diff_me_q2_abs_odds)) %>%
dplyr::slice_head(n = 10) %>%
ggplot2::ggplot() +
ggplot2::geom_col(
ggplot2::aes(x = reorder(factor(id), -diff_me_q2_abs_odds),
y = diff_me_q2_abs_odds,
fill = reorder(factor(id), -diff_me_q2_abs_odds))
) +
ggplot2::theme_classic() +
ggplot2::labs(x = "ID",
y = "Odds difference between me and community q2") +
ggplot2::guides(fill = FALSE) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
questions_myPredictions_byDiff %>%
dplyr::select(id, title, my_prediction, community_q2, community_q2_pre_me, diff_comm_q2_pre_me_abs_odds) %>%
dplyr::mutate(diff_comm_q2_pre_me_abs_odds = round(diff_comm_q2_pre_me_abs_odds, 1)) %>%
dplyr::arrange(dplyr::desc(diff_comm_q2_pre_me_abs_odds)) %>%
head() %>%
knitr::kable()
id | title | my_prediction | community_q2 | community_q2_pre_me | diff_comm_q2_pre_me_abs_odds |
---|---|---|---|---|---|
10084 | Will Sweden join NATO before 2024? | 0.14 | 0.87 | 0.42 | 9.2 |
9994 | Will at least 5 million refugees from Ukraine seek assistance from other countries? | 0.99 | 0.99 | 0.94 | 6.3 |
10057 | Will Russia control Chernihiv on June 1, 2022? | 0.01 | 0.01 | 0.05 | 5.2 |
10667 | Will Elon Musk hold major political office in the United States before February 2033? | 0.01 | 0.04 | 0.16 | 4.6 |
10004 | Will a major nuclear power plant in Germany be operational on June 1, 2023? | 0.10 | 0.25 | 0.55 | 3.7 |
10492 | Will Mariupol be under Russian control on June 1, 2022? | 0.99 | 0.95 | 0.85 | 3.4 |
Let’s see some Brier statistics on resolved questions:
# questions_myPredictions_resolved <- MetaculR_myPredictions_Resolved(guessed_by = Metaculus_response_login$Metaculus_user_id)
brier_me <- MetaculR_brier(questions_myPredictions)
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
#> Warning in x$results$possibilities$type == "binary" & !is.na(x$results$resolution) & : longer object length is not a multiple
#> of shorter object length
brier_me$brier_df %>%
ggplot2::ggplot() +
ggplot2::geom_col(
ggplot2::aes(x = name,
y = value,
fill = ID),
position = "dodge2"
) +
ggplot2::geom_text(
ggplot2::aes(x = name,
y = value,
label = round(value, 3)),
size = 2,
position = ggplot2::position_dodge2(width = 1), #"dodge2",
vjust = 0.5,
hjust = -0.25,
angle = 90) +
ggplot2::theme_classic() +
ggplot2::labs(x = "Statistic",
y = "Value") +
ggplot2::coord_cartesian(ylim = c(-1, 1)) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
#> Warning: Removed 17 rows containing missing values (geom_col).
#> Warning: Removed 17 rows containing missing values (geom_text).
brier_me$brier_bins_df %>%
ggplot2::ggplot() +
ggplot2::geom_col(
ggplot2::aes(x = centers,
y = freqs,
fill = ID),
position = ggplot2::position_dodge2(width = 0.1, preserve = "single")
) +
ggplot2::geom_text(
ggplot2::aes(x = centers,
y = freqs,
label = round(freqs, 3)),
size = 2,
position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
# vjust = 0.5,
hjust = -0.25,
angle = 90) +
ggplot2::theme_classic() +
ggplot2::coord_cartesian(ylim = c(0, 1))
brier_me$brier_bins_df %>%
ggplot2::ggplot() +
ggplot2::geom_pointrange(
ggplot2::aes(x = centers,
y = obars,
ymin = ci_low,
ymax = ci_high,
color = ID),
position = ggplot2::position_dodge2(width = 0.02)
) +
ggplot2::geom_line(
ggplot2::aes(x = centers,
y = ideal)
) +
ggplot2::geom_text(
ggplot2::aes(x = centers,
y = obars,
label = format(round(obars, 3), nsmall = 3)),
size = 2,
position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
vjust = -0.5,
hjust = -0.25,
angle = 90) +
ggplot2::theme_classic() +
ggplot2::coord_cartesian(ylim = c(0, max(brier_me$brier_bins_df $obars) * 1.1))
For questions you may not have predicted.
Let’s see some Brier statistics on resolved questions:
questions_recent_resolved <-
MetaculR_questions(
order_by = "-resolve_time",
status= "resolved",
guessed_by = "",
pages = 32,
offset = 0)
brier_recent_resolved$brier_df %>%
ggplot2::ggplot() +
ggplot2::geom_col(
ggplot2::aes(x = name,
y = value,
fill = ID),
position = "dodge2"
) +
ggplot2::geom_text(
ggplot2::aes(x = name,
y = value,
label = round(value, 3)),
size = 2,
position = ggplot2::position_dodge2(width = 1), #"dodge2",
vjust = 0.5,
hjust = -0.25,
angle = 90) +
ggplot2::theme_classic() +
ggplot2::labs(x = "Statistic",
y = "Value") +
ggplot2::coord_cartesian(ylim = c(-1, 1)) +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 45, vjust = 1, hjust = 1))
#> Warning: Removed 23 rows containing missing values (geom_col).
#> Warning: Removed 23 rows containing missing values (geom_text).
brier_recent_resolved$brier_bins_df %>%
ggplot2::ggplot() +
ggplot2::geom_col(
ggplot2::aes(x = centers,
y = freqs,
fill = ID),
position = ggplot2::position_dodge2(width = 0.1)
) +
ggplot2::geom_text(
ggplot2::aes(x = centers,
y = freqs,
label = round(freqs, 3)),
size = 2,
position = ggplot2::position_dodge2(width = 0.1), #"dodge2",
# vjust = 0.5,
hjust = -0.25,
angle = 90) +
ggplot2::theme_classic() +
ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$freqs) * 1.1))
brier_recent_resolved$brier_bins_df %>%
ggplot2::ggplot() +
ggplot2::geom_pointrange(
ggplot2::aes(x = centers,
y = obars,
ymin = ci_low,
ymax = ci_high,
color = ID),
position = ggplot2::position_dodge2(width = 0.02)
) +
ggplot2::geom_line(
ggplot2::aes(x = centers,
y = ideal)
) +
ggplot2::geom_text(
ggplot2::aes(x = centers,
y = obars,
label = format(round(obars, 3), nsmall = 3)),
size = 2,
position = ggplot2::position_dodge2(width = 0.02), #"dodge2",
vjust = -0.5,
hjust = -0.25,
angle = 90) +
ggplot2::theme_classic() +
ggplot2::coord_cartesian(ylim = c(0, max(brier_recent_resolved$brier_bins_df$obars) * 1.1))
Various sports now have an “Excitement Index” to identify games that interest fans–maybe we can do the same for predictions?
questions_myPredictions_byExcitement <- MetaculR_excitement(questions_myPredictions)
questions_myPredictions_byExcitement %>%
dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
dplyr::arrange(dplyr::desc(Total_Change)) %>%
head() %>%
knitr::kable()
id | title | Total_Change | Total_logodds_Change | Total_Change_Even | Total_logodds_Change_Even |
---|---|---|---|---|---|
10448 | Will Russia use chemical weapons in Ukraine in 2022? | 2.03 | 9.17 | 0.71 | 3.33 |
10085 | Will Finland join NATO before 2024? | 0.71 | 4.26 | 0.18 | 1.13 |
10057 | Will Russia control Chernihiv on June 1, 2022? | 0.71 | 6.53 | 0.09 | 0.89 |
10084 | Will Sweden join NATO before 2024? | 0.69 | 3.78 | 0.22 | 1.19 |
9994 | Will at least 5 million refugees from Ukraine seek assistance from other countries? | 0.47 | 5.17 | 0.19 | 1.38 |
10246 | Will a coup or regime change take place in Russia in 2022 or 2023? | 0.46 | 3.67 | 0.22 | 1.75 |
questions_myPredictions_byExcitement %>%
dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
dplyr::arrange(dplyr::desc(Total_logodds_Change)) %>%
head() %>%
knitr::kable()
id | title | Total_Change | Total_logodds_Change | Total_Change_Even | Total_logodds_Change_Even |
---|---|---|---|---|---|
10448 | Will Russia use chemical weapons in Ukraine in 2022? | 2.03 | 9.17 | 0.71 | 3.33 |
10057 | Will Russia control Chernihiv on June 1, 2022? | 0.71 | 6.53 | 0.09 | 0.89 |
9994 | Will at least 5 million refugees from Ukraine seek assistance from other countries? | 0.47 | 5.17 | 0.19 | 1.38 |
10094 | Will Russia control Kyiv on June 1, 2022? | 0.17 | 4.50 | 0.03 | 0.95 |
10085 | Will Finland join NATO before 2024? | 0.71 | 4.26 | 0.18 | 1.13 |
10269 | Will Kyiv be under Russian control before 2023? | 0.34 | 4.03 | 0.04 | 0.72 |
What were the most exciting resolved questions?
questions_recent_byExcitement <- MetaculR_excitement(questions_recent_resolved)
questions_recent_byExcitement %>%
dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
dplyr::arrange(dplyr::desc(Total_Change_Even)) %>%
head() %>%
knitr::kable()
id | title | Total_Change | Total_logodds_Change | Total_Change_Even | Total_logodds_Change_Even |
---|---|---|---|---|---|
6763 | Will Marine Le Pen win the 2022 French presidential election? | 0.30 | 3.16 | 0.16 | 1.79 |
10282 | Will Russia default on its debt in 2022? | 0.46 | 2.09 | 0.12 | 0.49 |
2511 | Will Emmanuel Macron be re-elected President of France in 2022? | 0.25 | 2.24 | 0.11 | 0.93 |
9182 | Will more than 20,000 people die within 28 days of a positive Covid test in the UK in 2022? | 0.20 | 1.33 | 0.03 | 0.17 |
9609 | Will Viktor Orbán win the 2022 Hungarian Parliamentary Election? | 0.17 | 0.87 | 0.03 | 0.15 |
9790 | Will Éric Zemmour be in the 2nd round of the 2022 French presidential election? | 0.15 | 3.50 | 0.03 | 0.55 |
What are the most exciting questions that are still open?
questions_recent_open <-
MetaculR_questions(
order_by = "close_time",
status = "open",
guessed_by = "")
questions_recent_open_byExcitement <- MetaculR_excitement(questions_recent_open)
questions_recent_open_byExcitement %>%
dplyr::mutate(Total_logodds_Change = round(Total_logodds_Change, 2),
Total_logodds_Change_Even = round(Total_logodds_Change_Even, 2)) %>%
dplyr::arrange(dplyr::desc(Total_logodds_Change_Even)) %>%
head() %>%
knitr::kable()
id | title | Total_Change | Total_logodds_Change | Total_Change_Even | Total_logodds_Change_Even |
---|---|---|---|---|---|
10448 | Will Russia use chemical weapons in Ukraine in 2022? | 2.03 | 9.17 | 0.71 | 3.33 |
9459 | Will Russian troops enter Kyiv, Ukraine before December 31, 2022? | 0.61 | 3.59 | 0.43 | 2.86 |
10040 | Will YouTube be blocked in Russia in 2022? | 0.74 | 3.44 | 0.39 | 1.89 |
10423 | Will Belarus invade Ukraine before June 1, 2022? | 0.56 | 5.74 | 0.14 | 1.75 |
10255 | Will Sweden’s government initiate the process of joining NATO in 2022? | 0.84 | 5.16 | 0.24 | 1.57 |
8899 | Will Hungary declare a referendum in 2022 to exit the EU? | 0.03 | 2.11 | 0.02 | 1.41 |
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.