‚Z”Šw‚ÌŽ¿–âƒXƒŒiˆãŽÒE“Œ‘呲ê—pj Part438 (991Ú½)
㉺‘OŽŸ1-V
’Šo‰ðœ •KŽ€Áª¯¶°(–{‰Æ) (‚×) Ž©ID Ú½žx ‚ ‚Ú[‚ñ
840: 05/20(‰Î)23:31 ID:gwaBTE4C(1) AAS
library(R2jags)
# ƒf[ƒ^
data <- list(
nA1 = 100, rA1 = 80, # Study1: Ž¡—ÃA
nB1 = 100, rB1 = 40, # Study1: Ž¡—ÃB
nA2 = 100, rA2 = 10, # Study2: Ž¡—ÃA
nC2 = 100, rC2 = 5 # Study2: Ž¡—ÃC
)
# JAGSƒ‚ƒfƒ‹itextConnectionŽg—pj
model_code <- "
model {
# –Þ“xŠÖ”
rA1 ~ dbin(pA1, nA1)
rB1 ~ dbin(pB1, nB1)
rA2 ~ dbin(pA2, nA2)
rC2 ~ dbin(pC2, nC2)
# Ž¡—ÃA‚ÌŠK‘wƒ‚ƒfƒ‹
mu_A ~ dbeta(1, 1)
tau_A ~ dgamma(0.001, 0.001)
pA1 ~ dbeta(mu_A * tau_A, (1 - mu_A) * tau_A)
pA2 ~ dbeta(mu_A * tau_A, (1 - mu_A) * tau_A)
sigma_A <- 1 / sqrt(tau_A) # SD‚ɕϊ·
# Ž¡—ÃB‚ÆC‚àŠK‘w‰»i•½‹ÏƒŠƒXƒN‚ð•ÊX‚É„’èj
mu_B ~ dbeta(1, 1)
mu_C ~ dbeta(1, 1)
pB1 ~ dbeta(mu_B * 100, (1 - mu_B) * 100) # ‚‚¢¸“x‚ð‰¼’è
pC2 ~ dbeta(mu_C * 100, (1 - mu_C) * 100)
# ƒŠƒXƒN·
RD_A1_B1 <- pA1 - pB1
RD_A2_C2 <- pA2 - pC2
RD_B1_C2 <- pB1 - pC2 # B vs C‚Ì’¼Ú”äŠr
}
"
# JAGSŽÀs
jags_model <-
(textConnection(model_code),
data = data, n.chains = 3, quiet=TRUE)
update(jags_model, 3000) #, progress.bar="none")
jags_samples <- coda.samples(jags_model,
c("mu_A", "sigma_A", "RD_A1_B1", "RD_A2_C2", "RD_B1_C2", "pA1", "pA2", "pB1", "pC2"),
n.iter=10000) # , progress.bar="none")
gelman.plot(jags_samples)
plot(jags_samples)
summary(jags_samples)
jags_samples |> as.matrix() |> as.data.frame() -> js
names(js)
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