高校数学の質問スレ（医者・東大卒専用） Part438

高校数学の質問スレ（医者・東大卒専用） Part438 (899ﾚｽ)
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844: １３２人目の素数さん [sage] 2025/05/24(土) 08:18:19.92 ID:VetM3rz7

# Example data
data <- data.frame(
donation = c(0, 1000, 2000, 0, 3000, 0, 4000, 0, 5000, 0),
score = c(90, 40, 35, 88, 30, 85, 25, 92, 20, 89),
parent = c(0, 1, 1, 0, 1, 0, 1, 0, 1, 0),
admission = as.factor(c(0, 1, 1, 0, 1, 0, 1, 0, 1, 0))
)

# New observation to predict
newdata <- data.frame(
donation = 2500,
score = 40,
parent = 1
)

# Fit model and obtain results
set.seed(123)
result <- fit_bayesian_logistic_jags(
data = data,
formula = admission ~ donation + score + parent,
newdata = newdata
)

# Extract variable names including intercept
var_names <- colnames(model.matrix(admission ~ donation + score + parent, data))

# Extract beta coefficient summaries
beta_stats <- result$summary$statistics[grep("^beta\\[", rownames(result$summary$statistics)), c("Mean", "SD")]
beta_quants <- result$summary$quantiles[grep("^beta\\[", rownames(result$summary$quantiles)), c("2.5%", "97.5%")]

# Rename row names using variable names
rownames(beta_stats) <- var_names
rownames(beta_quants) <- var_names

# Display results
print(beta_stats)
print(beta_quants)
cat("Predicted probability:", round(result$predicted_prob, 3), "\n")

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