190728_学力差の検討
今回の課題
- 学級規模に関係なく，どこで学力差が生じるのか
- (a)は学級規模で説明されるのか
- 学力の変動の大きさは，prior achievementで説明されるのか
- (a)に対する(b)と(c)の交互作用は見られるか
とりあえず国語をやってる
NRTデータを読み込む
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/04_NRT/SY2018")
nrt.all <- read.csv("nrt.csv", fileEncoding = "Shift_JIS")
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/190700_Anal")国語のデータに限定する
nrt.koku <- nrt.all %>%
dplyr::select(c("renban", "sho.sid",
"koku.ss.1213", #1-2年生追加
"koku.ss.1314",
"koku.ss.1415",
"koku.ss.1516",
"koku.ss.1617",
"koku.ss.1718" #6-7年生追加
))
colnames(nrt.koku) <- c("renban", "sid",
"g12.koku",
"g23.koku",
"g34.koku",
"g45.koku",
"g56.koku",
"g67.koku"
)
nrt.koku <- nrt.koku%>%
mutate(id = row_number())
# write.csv(nrt.koku, "nrt_koku.csv")全体について，学年間の分散比を求めてみる
library(psych)
g12.ds <- describe(nrt.koku$g12.koku)
g23.ds <- describe(nrt.koku$g23.koku)
g34.ds <- describe(nrt.koku$g34.koku)
g45.ds <- describe(nrt.koku$g45.koku)
g56.ds <- describe(nrt.koku$g56.koku)
g67.ds <- describe(nrt.koku$g67.koku)
#平均差と分散比
d23 <- matrix(c(g23.ds[,3] - g12.ds[,3], g23.ds[,4]^2 / g12.ds[,4]^2), nrow=1, ncol=2)
d34<- matrix(c(g34.ds[,3] - g23.ds[,3], g34.ds[,4]^2 / g23.ds[,4]^2), nrow=1, ncol=2)
d45<- matrix(c(g45.ds[,3] - g34.ds[,3], g45.ds[,4]^2 / g34.ds[,4]^2), nrow=1, ncol=2)
d56 <- matrix(c(g56.ds[,3] - g45.ds[,3], g56.ds[,4]^2 / g45.ds[,4]^2), nrow=1, ncol=2)
d67 <- matrix(c(g67.ds[,3] - g56.ds[,3], g67.ds[,4]^2 / g56.ds[,4]^2), nrow=1, ncol=2)
diff.1 <- rbind(d23, d34, d45, d56, d67)
colnames(diff.1) <- c("M.Diff", "V.Ratio")
rownames(diff.1) <- c("d23", "d34", "d45", "d56", "d67")
diff.1## M.Diff V.Ratio
## d23 -0.394416929 0.9156668
## d34 0.122917605 1.3135990
## d45 -0.330789788 0.9198229
## d56 -0.509411453 0.7934490
## d67 0.003895468 1.4200221変動係数(標準偏差/平均)を求めてみる
cv2 <- g12.ds[,4] / g12.ds[,3]
cv3 <- g23.ds[,4] / g23.ds[,3]
cv4 <- g34.ds[,4] / g34.ds[,3]
cv5 <- g45.ds[,4] / g45.ds[,3]
cv6 <- g56.ds[,4] / g56.ds[,3]
cv7 <- g67.ds[,4] / g67.ds[,3]
cv.1 <- matrix(c(cv2, cv3, cv4, cv5, cv6, cv7), nrow=6, ncol=1)
colnames(cv.1) <- c("cv")
rownames(cv.1) <- c("1-2","2-3","3-4","4-5","5-6","6-7")
cv.1## cv
## 1-2 0.1638892
## 2-3 0.1579863
## 3-4 0.1806554
## 4-5 0.1743408
## 5-6 0.1567992
## 6-7 0.1868354変動係数の学年間の比を求めてみる
cv.ratio23 <- cv.1[2]/ cv.1[1]
cv.ratio34 <- cv.1[3]/ cv.1[2]
cv.ratio45 <- cv.1[4]/ cv.1[3]
cv.ratio56 <- cv.1[5]/ cv.1[4]
cv.ratio67 <- cv.1[6]/ cv.1[5]
cv.ratio1 <- matrix(c(cv.ratio23, cv.ratio34, cv.ratio45, cv.ratio56, cv.ratio67), nrow=5, ncol=1)
colnames(cv.ratio1) <- c("cv.rario")
rownames(cv.ratio1) <- c("2-3", "3-4","4-5","5-6", "6-7")
cv.ratio1## cv.rario
## 2-3 0.9639824
## 3-4 1.1434877
## 4-5 0.9650466
## 5-6 0.8993828
## 6-7 1.1915586学校ごとに変動係数を求める
g12.mean <- nrt.koku[c("sid", "g12.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(avg.12 = mean(g12.koku))
g12.sd <- nrt.koku[c("sid", "g12.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(sd.12 = sd(g12.koku))
g12.msd <-data.frame(dplyr::inner_join(g12.mean, g12.sd, by = "sid"))
g23.mean <- nrt.koku[c("sid", "g23.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(avg.23 = mean(g23.koku))
g23.sd <- nrt.koku[c("sid", "g23.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(sd.23 = sd(g23.koku))
g23.msd <-data.frame(dplyr::inner_join(g23.mean, g23.sd, by = "sid"))
g34.mean <- nrt.koku[c("sid", "g34.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(avg.34 = mean(g34.koku))
g34.sd <- nrt.koku[c("sid", "g34.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(sd.34 = sd(g34.koku))
g34.msd <-data.frame(dplyr::inner_join(g34.mean, g34.sd, by = "sid"))
g45.mean <- nrt.koku[c("sid", "g45.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(avg.45 = mean(g45.koku))
g45.sd <- nrt.koku[c("sid", "g45.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(sd.45 = sd(g45.koku))
g45.msd <-data.frame(dplyr::inner_join(g45.mean, g45.sd, by = "sid"))
g56.mean <- nrt.koku[c("sid", "g56.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(avg.56 = mean(g56.koku))
g56.sd <- nrt.koku[c("sid", "g56.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(sd.56 = sd(g56.koku))
g56.msd <-data.frame(dplyr::inner_join(g56.mean, g56.sd, by = "sid"))
g67.mean <- nrt.koku[c("sid", "g67.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(avg.67 = mean(g67.koku))
g67.sd <- nrt.koku[c("sid", "g67.koku")] %>% na.omit() %>% group_by(sid) %>% summarise(sd.67 = sd(g67.koku))
g67.msd <-data.frame(dplyr::inner_join(g67.mean, g67.sd, by = "sid"))
g123.msd <- data.frame(dplyr::full_join(g12.msd, g23.msd, by = "sid"))
g1234.msd <- data.frame(dplyr::full_join(g123.msd, g34.msd, by = "sid"))
g12345.msd <- data.frame(dplyr::full_join(g1234.msd, g45.msd, by = "sid"))
g123456.msd <- data.frame(dplyr::full_join(g12345.msd, g56.msd, by = "sid"))
g17.msd <- data.frame(dplyr::full_join(g123456.msd, g67.msd, by = "sid"))
g17.msd <- g17.msd %>% dplyr::mutate(cv12 = sd.12 / avg.12)
g17.msd <- g17.msd %>% dplyr::mutate(cv23 = sd.23 / avg.23)
g17.msd <- g17.msd %>% dplyr::mutate(cv34 = sd.34 / avg.34)
g17.msd <- g17.msd %>% dplyr::mutate(cv45 = sd.45 / avg.45)
g17.msd <- g17.msd %>% dplyr::mutate(cv56 = sd.56 / avg.56)
g17.msd <- g17.msd %>% dplyr::mutate(cv67 = sd.67 / avg.67)
g17.msd <- g17.msd %>% dplyr::mutate(cvr.23 = cv23 / cv12)
g17.msd <- g17.msd %>% dplyr::mutate(cvr.34 = cv34 / cv23)
g17.msd <- g17.msd %>% dplyr::mutate(cvr.45 = cv45 / cv34)
g17.msd <- g17.msd %>% dplyr::mutate(cvr.56 = cv56 / cv45)
g17.msd <- g17.msd %>% dplyr::mutate(cvr.67 = cv67 / cv56)
#head(g16.msd)
hist(g17.msd$cvr.23, breaks=seq(0,4,0.2), main="cvr.23", xlab="cvr", ylim=c(0,120), xlim=c(0,4))
hist(g17.msd$cvr.34, breaks=seq(0,4,0.2), main="cvr.34", xlab="cvr", ylim=c(0,120), xlim=c(0,4))
hist(g17.msd$cvr.45, breaks=seq(0,4,0.2), main="cvr.45", xlab="cvr", ylim=c(0,120), xlim=c(0,4))
hist(g17.msd$cvr.56, breaks=seq(0,4,0.2), main="cvr.56", xlab="cvr", ylim=c(0,120), xlim=c(0,4))
hist(g17.msd$cvr.67, breaks=seq(0,4,0.2), main="cvr.56", xlab="cvr", ylim=c(0,120), xlim=c(0,4))
## 学級規模データの読み込み
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/01_CSNC")
csnc.all <- read_excel("sho_csnc.xlsx")
# 学校データ整形
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/190700_Anal")
#### 統廃合のない学校のみを対象 複式設置校を除外
csnc.taisho_ <- dplyr::filter(csnc.all, taisho.g1 == 1 &
togo == 0 & nonrt == 0 & fuku == 0)
# 学校データを数値型にする
csnc.taisho <- select(csnc.taisho_,(c("taisho", "sid.new",
"nc.g1", "csmean.g1",
"nc.g2", "csmean.g2",
"nc.g3", "csmean.g3",
"nc.g4", "csmean.g4",
"nc.g5", "csmean.g5",
"nc.g6", "csmean.g6"
)))
csnc.taisho$taisho <- as.numeric(csnc.taisho$taisho)
csnc.taisho$sid.new <- as.numeric(csnc.taisho$sid.new)
csnc.taisho$nc.g1 <- as.numeric(csnc.taisho$nc.g1)
csnc.taisho$csmean.g1 <- as.numeric(csnc.taisho$csmean.g1)
csnc.taisho$nc.g2 <- as.numeric(csnc.taisho$nc.g2)
csnc.taisho$csmean.g2 <- as.numeric(csnc.taisho$csmean.g2)
csnc.taisho$nc.g3 <- as.numeric(csnc.taisho$nc.g3)
csnc.taisho$csmean.g3 <- as.numeric(csnc.taisho$csmean.g3)
csnc.taisho$nc.g4 <- as.numeric(csnc.taisho$nc.g4)
csnc.taisho$csmean.g4 <- as.numeric(csnc.taisho$csmean.g4)
csnc.taisho$nc.g5 <- as.numeric(csnc.taisho$nc.g5)
csnc.taisho$csmean.g5 <- as.numeric(csnc.taisho$csmean.g5)
csnc.taisho$nc.g6 <- as.numeric(csnc.taisho$nc.g6)
csnc.taisho$csmean.g6 <- as.numeric(csnc.taisho$csmean.g6)
csnc.nona <- na.omit(csnc.taisho)
colnames(csnc.nona) <- c("taisho", "sid",
"nc.g1", "cs.g1",
"nc.g2", "cs.g2",
"nc.g3", "cs.g3",
"nc.g4", "cs.g4",
"nc.g5", "cs.g5",
"nc.g6", "cs.g6"
)
csnc <- csnc.nona[,2:14]
## 学級規模を中心化する
### 各学年での平均
cs.m.g1 <- mean(csnc$cs.g1)
cs.m.g2 <- mean(csnc$cs.g2)
cs.m.g3 <- mean(csnc$cs.g3)
cs.m.g4 <- mean(csnc$cs.g4)
cs.m.g5 <- mean(csnc$cs.g5)
cs.m.g6 <- mean(csnc$cs.g6)
### 各学年の平均の平均
csm <- matrix(c(cs.m.g1, cs.m.g2, cs.m.g3, cs.m.g4, cs.m.g5, cs.m.g6), nrow=6, ncol=1)
csnc$cs.c.g1 <- csnc$cs.g1 - mean(csm)
csnc$cs.c.g2 <- csnc$cs.g2 - mean(csm)
csnc$cs.c.g3 <- csnc$cs.g3 - mean(csm)
csnc$cs.c.g4 <- csnc$cs.g4 - mean(csm)
csnc$cs.c.g5 <- csnc$cs.g5 - mean(csm)
csnc$cs.c.g6 <- csnc$cs.g6 - mean(csm)
## 学級規模変動差分データ列作成
csnc$cs.d12 <- csnc$cs.g2 - csnc$cs.g1
csnc$cs.d23 <- csnc$cs.g3 - csnc$cs.g2
csnc$cs.d34 <- csnc$cs.g4 - csnc$cs.g3
csnc$cs.d45 <- csnc$cs.g5 - csnc$cs.g4
csnc$cs.d56 <- csnc$cs.g6 - csnc$cs.g5 学級規模データと学校ごとの変動係数データを結合する
# 2-3年生
cs.23 <- csnc[c("sid", "nc.g2", "cs.g2", "cs.c.g2", "cs.d12")]
cvr.23 <- g17.msd[c("sid", "avg.12", "avg.23", "cvr.23")]
cs.cvr.23<-na.omit(data.frame(dplyr::inner_join(cs.23, cvr.23, by = "sid")))
#3-4年生
cs.34 <- csnc[c("sid", "nc.g3", "cs.g3", "cs.c.g3", "cs.d23")]
cvr.34 <- g17.msd[c("sid", "avg.23", "avg.34", "cvr.34")]
cs.cvr.34<-na.omit(data.frame(dplyr::inner_join(cs.34, cvr.34, by = "sid")))
# 4-5年生
cs.45 <- csnc[c("sid", "nc.g4", "cs.g4", "cs.c.g4", "cs.d34")]
cvr.45 <- g17.msd[c("sid", "avg.34", "avg.45", "cvr.45")]
cs.cvr.45<-na.omit(data.frame(dplyr::inner_join(cs.45, cvr.45, by = "sid")))
# 5-6年生
cs.56 <- csnc[c("sid", "nc.g5", "cs.g5", "cs.c.g5", "cs.d45")]
cvr.56 <- g17.msd[c("sid", "avg.45", "avg.56", "cvr.56")]
cs.cvr.56<-na.omit(data.frame(dplyr::inner_join(cs.56, cvr.56, by = "sid")))
# 6-7年生
cs.67 <- csnc[c("sid", "nc.g6", "cs.g6", "cs.c.g6", "cs.d56")]
cvr.67 <- g17.msd[c("sid", "avg.56", "avg.67", "cvr.67")]
cs.cvr.67<-na.omit(data.frame(dplyr::inner_join(cs.67, cvr.67, by = "sid")))学級規模と変動係数比の散布図を描いてみる
#plot(cs.cvr.12$cs.g2, cs.cvr.12$cvr.12, xlim = c(0, 50), ylim = c(0,3), main = "cs.cvr.23")
plot(cs.cvr.23$cs.g2, cs.cvr.23$cvr.23, xlim = c(0, 50), ylim = c(0,3), main = "cs.cvr.23")
plot(cs.cvr.34$cs.g3, cs.cvr.34$cvr.34, xlim = c(0, 50), ylim = c(0,3), main = "cs.cvr.34")
plot(cs.cvr.45$cs.g4, cs.cvr.45$cvr.45, xlim = c(0, 50), ylim = c(0,3), main = "cs.cvr.45")
plot(cs.cvr.56$cs.g5, cs.cvr.56$cvr.56, xlim = c(0, 50), ylim = c(0,3), main = "cs.cvr.56")
plot(cs.cvr.67$cs.g6, cs.cvr.67$cvr.67, xlim = c(0, 50), ylim = c(0,3), main = "cs.cvr.67")
学級規模の変動と変動係数比の散布図を描いてみる
plot(cs.cvr.23$cs.d12, cs.cvr.23$cvr.23, xlim = c(-15, 15), ylim = c(0,3), main = "cs_d.cvr.23")
plot(cs.cvr.34$cs.d23, cs.cvr.34$cvr.34, xlim = c(-15, 15), ylim = c(0,3), main = "cs_d.cvr.34")
plot(cs.cvr.45$cs.d34, cs.cvr.45$cvr.45, xlim = c(-15, 15), ylim = c(0,3), main = "cs_d.cvr.45")
plot(cs.cvr.56$cs.d45, cs.cvr.56$cvr.56, xlim = c(-15, 15), ylim = c(0,3), main = "cs_d.cvr.56")
plot(cs.cvr.67$cs.d56, cs.cvr.67$cvr.67, xlim = c(-15, 15), ylim = c(0,3), main = "cs_d.cvr.56")
以下のことを検討する
- 変動係数は学級規模で説明されるのか
- 変動係数は，prior achievementで説明されるのか
- 変動係数の大きさに対する(a)と(b)の交互作用は見られるか
# 学級規模の中心化
cs.cvr.23$cs.c.g2 <- cs.cvr.23$cs.g2 - mean(cs.cvr.23$cs.g2)
cs.cvr.34$cs.c.g3 <- cs.cvr.34$cs.g3 - mean(cs.cvr.34$cs.g3)
cs.cvr.45$cs.c.g4 <- cs.cvr.45$cs.g4 - mean(cs.cvr.45$cs.g4)
cs.cvr.56$cs.c.g5 <- cs.cvr.56$cs.g5 - mean(cs.cvr.56$cs.g5)
cs.cvr.67$cs.c.g6 <- cs.cvr.67$cs.g6 - mean(cs.cvr.67$cs.g6)
# Prior achievementの中心化
cs.cvr.23$avg.c.12 <- cs.cvr.23$avg.12 - mean(cs.cvr.23$avg.12)
cs.cvr.34$avg.c.23 <- cs.cvr.34$avg.23 - mean(cs.cvr.34$avg.23)
cs.cvr.45$avg.c.34 <- cs.cvr.45$avg.34 - mean(cs.cvr.45$avg.34)
cs.cvr.56$avg.c.45 <- cs.cvr.56$avg.45 - mean(cs.cvr.56$avg.45)
cs.cvr.67$avg.c.56 <- cs.cvr.67$avg.56 - mean(cs.cvr.67$avg.56)
head(cs.cvr.23)## sid nc.g2 cs.g2 cs.c.g2 cs.d12 avg.12 avg.23 cvr.23
## 34 18034 2 32.5 10.0368056 0 49.71429 51.42857 0.9703556
## 35 18035 1 23.0 0.5368056 0 50.31818 52.36364 0.8141486
## 36 18036 4 27.5 5.0368056 0 53.63636 52.81818 0.9434250
## 37 18037 1 18.0 -4.4631944 0 54.35714 53.85714 0.9311345
## 38 18050 3 26.0 3.5368056 0 51.43662 48.88732 0.9881712
## 39 18051 2 25.5 3.0368056 0 49.60417 52.66667 0.7358570
## avg.c.12
## 34 -4.2219832
## 35 -3.6180871
## 36 -0.2999053
## 37 0.4208740
## 38 -2.4996492
## 39 -4.3321022Prior achievementと変動係数比の散布図を描いてみる
plot(cs.cvr.23$avg.c.12, cs.cvr.23$cvr.23, xlim = c(-15, 15), ylim = c(0,3), main = "Prior_1, cvr.23")
plot(cs.cvr.34$avg.c.23, cs.cvr.34$cvr.34, xlim = c(-15, 15), ylim = c(0,3), main = "Prior_2, cvr.34")
plot(cs.cvr.45$avg.c.34, cs.cvr.45$cvr.45, xlim = c(-15, 15), ylim = c(0,3), main = "Prior_3, cvr.45")
plot(cs.cvr.56$avg.c.45, cs.cvr.56$cvr.56, xlim = c(-15, 15), ylim = c(0,3), main = "Prior_4, cvr.56")
plot(cs.cvr.67$avg.c.56, cs.cvr.67$cvr.67, xlim = c(-15, 15), ylim = c(0,3), main = "Prior_5, cvr.56")
回帰分析をしてみる
# 2年生終了時
library(brms)## Loading required package: Rcpp## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha## Loading 'brms' package (version 2.7.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').
## Run theme_set(theme_default()) to use the default bayesplot theme.##
## Attaching package: 'brms'## The following object is masked from 'package:psych':
##
## csres.23 <- brm(cvr.23 ~ cs.c.g2 + avg.c.12 + cs.c.g2:avg.c.12,
data =cs.cvr.23,
prior = c(set_prior("normal(0,10)", class = "b")),
chains = 4,
iter = 10000,
warmup = 3000
)## Compiling the C++ model## Start sampling##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 3.3e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.33 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 1: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 1: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 1: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 1: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 1: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 1: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 1: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 1: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 1: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 1: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 1: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 0.262332 seconds (Warm-up)
## Chain 1: 0.394777 seconds (Sampling)
## Chain 1: 0.657109 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 1.5e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 2: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 2: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 2: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 2: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 2: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 2: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 2: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 2: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 2: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 2: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 2: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 2:
## Chain 2: Elapsed Time: 0.273983 seconds (Warm-up)
## Chain 2: 0.405375 seconds (Sampling)
## Chain 2: 0.679358 seconds (Total)
## Chain 2:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 1.4e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
## Chain 3:
## Chain 3: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 3: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 3: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 3: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 3: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 3: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 3: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 3: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 3: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 3: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 3: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 3: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 3:
## Chain 3: Elapsed Time: 0.252963 seconds (Warm-up)
## Chain 3: 0.389865 seconds (Sampling)
## Chain 3: 0.642828 seconds (Total)
## Chain 3:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 1.3e-05 seconds
## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
## Chain 4: Adjust your expectations accordingly!
## Chain 4:
## Chain 4:
## Chain 4: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 4: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 4: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 4: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 4: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 4: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 4: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 4: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 4: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 4: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 4: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 4: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 4:
## Chain 4: Elapsed Time: 0.292435 seconds (Warm-up)
## Chain 4: 0.425332 seconds (Sampling)
## Chain 4: 0.717767 seconds (Total)
## Chain 4:print(res.23, digits = 3)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: cvr.23 ~ cs.c.g2 + avg.c.12 + cs.c.g2:avg.c.12
## Data: cs.cvr.23 (Number of observations: 120)
## Samples: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup samples = 28000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 0.993 0.023 0.948 1.038 31334 1.000
## cs.c.g2 0.007 0.003 0.000 0.014 33679 1.000
## avg.c.12 0.040 0.008 0.024 0.056 30018 1.000
## cs.c.g2:avg.c.12 -0.002 0.001 -0.004 0.000 37105 1.000
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.245 0.016 0.216 0.280 29552 1.000
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).# 3年生終了時
res.34 <- brm(cvr.34 ~ cs.c.g3 + avg.c.23 + cs.c.g3:avg.c.23,
data =cs.cvr.34,
prior = c(set_prior("normal(0,10)", class = "b")),
chains = 4,
iter = 10000,
warmup = 3000
)## Compiling the C++ model## recompiling to avoid crashing R session## Start sampling##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 3.5e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 1: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 1: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 1: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 1: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 1: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 1: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 1: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 1: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 1: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 1: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 1: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 0.284669 seconds (Warm-up)
## Chain 1: 0.395939 seconds (Sampling)
## Chain 1: 0.680608 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 1.4e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 2: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 2: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 2: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 2: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 2: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 2: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 2: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 2: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 2: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 2: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 2: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 2:
## Chain 2: Elapsed Time: 0.306742 seconds (Warm-up)
## Chain 2: 0.398695 seconds (Sampling)
## Chain 2: 0.705437 seconds (Total)
## Chain 2:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 1.2e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
## Chain 3:
## Chain 3: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 3: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 3: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 3: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 3: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 3: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 3: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 3: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 3: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 3: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 3: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 3: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 3:
## Chain 3: Elapsed Time: 0.296691 seconds (Warm-up)
## Chain 3: 0.394632 seconds (Sampling)
## Chain 3: 0.691323 seconds (Total)
## Chain 3:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 1.3e-05 seconds
## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
## Chain 4: Adjust your expectations accordingly!
## Chain 4:
## Chain 4:
## Chain 4: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 4: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 4: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 4: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 4: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 4: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 4: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 4: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 4: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 4: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 4: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 4: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 4:
## Chain 4: Elapsed Time: 0.295688 seconds (Warm-up)
## Chain 4: 0.389054 seconds (Sampling)
## Chain 4: 0.684742 seconds (Total)
## Chain 4:print(res.34, digits = 3)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: cvr.34 ~ cs.c.g3 + avg.c.23 + cs.c.g3:avg.c.23
## Data: cs.cvr.34 (Number of observations: 120)
## Samples: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup samples = 28000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 1.198 0.019 1.160 1.235 29921 1.000
## cs.c.g3 0.000 0.003 -0.005 0.005 32674 1.000
## avg.c.23 0.048 0.007 0.034 0.063 29246 1.000
## cs.c.g3:avg.c.23 -0.000 0.001 -0.002 0.002 37397 1.000
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.202 0.013 0.178 0.231 28588 1.000
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).# 4年生終了時
res.45 <- brm(cvr.45 ~ cs.c.g4 + avg.c.34 + cs.c.g4:avg.c.34,
data =cs.cvr.45,
prior = c(set_prior("normal(0,10)", class = "b")),
chains = 4,
iter = 10000,
warmup = 3000
)## Compiling the C++ model## recompiling to avoid crashing R session## Start sampling##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 3.4e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 1: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 1: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 1: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 1: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 1: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 1: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 1: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 1: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 1: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 1: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 1: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 0.353847 seconds (Warm-up)
## Chain 1: 0.511305 seconds (Sampling)
## Chain 1: 0.865152 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 2.1e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 2: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 2: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 2: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 2: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 2: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 2: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 2: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 2: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 2: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 2: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 2: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 2:
## Chain 2: Elapsed Time: 0.356933 seconds (Warm-up)
## Chain 2: 0.422901 seconds (Sampling)
## Chain 2: 0.779834 seconds (Total)
## Chain 2:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 2.2e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
## Chain 3:
## Chain 3: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 3: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 3: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 3: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 3: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 3: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 3: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 3: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 3: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 3: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 3: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 3: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 3:
## Chain 3: Elapsed Time: 0.348234 seconds (Warm-up)
## Chain 3: 0.461129 seconds (Sampling)
## Chain 3: 0.809363 seconds (Total)
## Chain 3:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 2.1e-05 seconds
## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
## Chain 4: Adjust your expectations accordingly!
## Chain 4:
## Chain 4:
## Chain 4: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 4: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 4: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 4: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 4: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 4: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 4: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 4: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 4: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 4: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 4: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 4: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 4:
## Chain 4: Elapsed Time: 0.338421 seconds (Warm-up)
## Chain 4: 0.444137 seconds (Sampling)
## Chain 4: 0.782558 seconds (Total)
## Chain 4:print(res.45, digits = 3)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: cvr.45 ~ cs.c.g4 + avg.c.34 + cs.c.g4:avg.c.34
## Data: cs.cvr.45 (Number of observations: 120)
## Samples: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup samples = 28000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 0.968 0.019 0.931 1.005 25313 1.000
## cs.c.g4 -0.001 0.003 -0.006 0.005 32869 1.000
## avg.c.34 0.016 0.006 0.004 0.028 27118 1.000
## cs.c.g4:avg.c.34 0.001 0.001 -0.001 0.002 36952 1.000
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.203 0.013 0.179 0.231 24763 1.000
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).# 5年生終了時
res.56 <- brm(cvr.56 ~ cs.c.g5 + avg.c.45 + cs.c.g5:avg.c.45,
data =cs.cvr.56,
prior = c(set_prior("normal(0,10)", class = "b")),
chains = 4,
iter = 10000,
warmup = 3000
)## Compiling the C++ model## recompiling to avoid crashing R session## Start sampling##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 3.7e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 1: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 1: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 1: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 1: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 1: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 1: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 1: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 1: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 1: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 1: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 1: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 0.561855 seconds (Warm-up)
## Chain 1: 0.751285 seconds (Sampling)
## Chain 1: 1.31314 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 1.4e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 2: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 2: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 2: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 2: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 2: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 2: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 2: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 2: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 2: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 2: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 2: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 2:
## Chain 2: Elapsed Time: 0.570974 seconds (Warm-up)
## Chain 2: 0.747225 seconds (Sampling)
## Chain 2: 1.3182 seconds (Total)
## Chain 2:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 1.4e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
## Chain 3:
## Chain 3: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 3: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 3: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 3: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 3: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 3: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 3: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 3: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 3: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 3: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 3: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 3: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 3:
## Chain 3: Elapsed Time: 0.599565 seconds (Warm-up)
## Chain 3: 0.780167 seconds (Sampling)
## Chain 3: 1.37973 seconds (Total)
## Chain 3:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 2.5e-05 seconds
## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
## Chain 4: Adjust your expectations accordingly!
## Chain 4:
## Chain 4:
## Chain 4: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 4: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 4: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 4: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 4: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 4: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 4: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 4: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 4: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 4: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 4: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 4: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 4:
## Chain 4: Elapsed Time: 0.600028 seconds (Warm-up)
## Chain 4: 0.790163 seconds (Sampling)
## Chain 4: 1.39019 seconds (Total)
## Chain 4:print(res.56, digits = 3)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: cvr.56 ~ cs.c.g5 + avg.c.45 + cs.c.g5:avg.c.45
## Data: cs.cvr.56 (Number of observations: 170)
## Samples: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup samples = 28000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 0.905 0.009 0.886 0.923 21929 1.000
## cs.c.g5 -0.001 0.001 -0.003 0.002 31997 1.000
## avg.c.45 0.009 0.004 0.002 0.016 24824 1.000
## cs.c.g5:avg.c.45 -0.000 0.000 -0.001 0.000 30259 1.000
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.123 0.007 0.110 0.137 20800 1.000
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).# 6年生終了時
res.67 <- brm(cvr.67 ~ cs.c.g6 + avg.c.56 + cs.c.g6:avg.c.56,
data =cs.cvr.67,
prior = c(set_prior("normal(0,10)", class = "b")),
chains = 4,
iter = 10000,
warmup = 3000
)## Compiling the C++ model## recompiling to avoid crashing R session## Start sampling##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 1).
## Chain 1:
## Chain 1: Gradient evaluation took 3e-05 seconds
## Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
## Chain 1: Adjust your expectations accordingly!
## Chain 1:
## Chain 1:
## Chain 1: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 1: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 1: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 1: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 1: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 1: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 1: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 1: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 1: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 1: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 1: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 1: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 1:
## Chain 1: Elapsed Time: 0.381388 seconds (Warm-up)
## Chain 1: 0.511142 seconds (Sampling)
## Chain 1: 0.89253 seconds (Total)
## Chain 1:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 2).
## Chain 2:
## Chain 2: Gradient evaluation took 2.5e-05 seconds
## Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
## Chain 2: Adjust your expectations accordingly!
## Chain 2:
## Chain 2:
## Chain 2: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 2: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 2: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 2: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 2: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 2: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 2: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 2: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 2: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 2: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 2: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 2: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 2:
## Chain 2: Elapsed Time: 0.392612 seconds (Warm-up)
## Chain 2: 0.716389 seconds (Sampling)
## Chain 2: 1.109 seconds (Total)
## Chain 2:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 3).
## Chain 3:
## Chain 3: Gradient evaluation took 1.4e-05 seconds
## Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
## Chain 3: Adjust your expectations accordingly!
## Chain 3:
## Chain 3:
## Chain 3: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 3: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 3: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 3: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 3: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 3: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 3: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 3: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 3: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 3: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 3: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 3: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 3:
## Chain 3: Elapsed Time: 0.454163 seconds (Warm-up)
## Chain 3: 0.766771 seconds (Sampling)
## Chain 3: 1.22093 seconds (Total)
## Chain 3:
##
## SAMPLING FOR MODEL 'd35359081d7733aebc9e00ac9119bde7' NOW (CHAIN 4).
## Chain 4:
## Chain 4: Gradient evaluation took 1.4e-05 seconds
## Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
## Chain 4: Adjust your expectations accordingly!
## Chain 4:
## Chain 4:
## Chain 4: Iteration: 1 / 10000 [ 0%] (Warmup)
## Chain 4: Iteration: 1000 / 10000 [ 10%] (Warmup)
## Chain 4: Iteration: 2000 / 10000 [ 20%] (Warmup)
## Chain 4: Iteration: 3000 / 10000 [ 30%] (Warmup)
## Chain 4: Iteration: 3001 / 10000 [ 30%] (Sampling)
## Chain 4: Iteration: 4000 / 10000 [ 40%] (Sampling)
## Chain 4: Iteration: 5000 / 10000 [ 50%] (Sampling)
## Chain 4: Iteration: 6000 / 10000 [ 60%] (Sampling)
## Chain 4: Iteration: 7000 / 10000 [ 70%] (Sampling)
## Chain 4: Iteration: 8000 / 10000 [ 80%] (Sampling)
## Chain 4: Iteration: 9000 / 10000 [ 90%] (Sampling)
## Chain 4: Iteration: 10000 / 10000 [100%] (Sampling)
## Chain 4:
## Chain 4: Elapsed Time: 0.445212 seconds (Warm-up)
## Chain 4: 0.589446 seconds (Sampling)
## Chain 4: 1.03466 seconds (Total)
## Chain 4:print(res.67, digits = 3)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: cvr.67 ~ cs.c.g6 + avg.c.56 + cs.c.g6:avg.c.56
## Data: cs.cvr.67 (Number of observations: 170)
## Samples: 4 chains, each with iter = 10000; warmup = 3000; thin = 1;
## total post-warmup samples = 28000
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## Intercept 1.221 0.015 1.191 1.251 23383 1.000
## cs.c.g6 -0.001 0.002 -0.006 0.003 33594 1.000
## avg.c.56 0.007 0.007 -0.006 0.021 26496 1.000
## cs.c.g6:avg.c.56 -0.000 0.001 -0.002 0.001 35905 1.000
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat
## sigma 0.197 0.011 0.177 0.220 23473 1.000
##
## Samples were drawn using sampling(NUTS). For each parameter, Eff.Sample
## is a crude measure of effective sample size, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).