学区SESを込みにした学級規模と学力の推移
学校データ（クラスサイズとSES）のデータの整形
データを読み込む
library(readxl)
hensei2012 <- read_excel("191023_Hensei2012.xlsx", sheet = 1)
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/01_CSNC")
hensei2013 <- read_excel("sho_csnc.xlsx", sheet = 1)
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/191023_CS_SES")データを整形する
colnames(hensei2012) <- c("shu", "city", "schl", "schlname", "n.g1",
"v01", "v02", "nc.g1", "csmean.g1")
hensei2012 <- subset(hensei2012, v01 != "対象外")
hensei2012 <- subset(hensei2012, v01 != "複式")
hensei2012 <- hensei2012[c("schlname", "nc.g1", "csmean.g1")]
hensei2012$nc.g1 <- as.numeric(hensei2012$nc.g1)
hensei2012$csmean.g1 <- as.numeric(hensei2012$csmean.g1)
hensei2013 <- hensei2013[,-c(9:10)]
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncsnc.6y <- full_join(hensei2013, hensei2012, by = "schlname")
csnc.6y <- csnc.6y[,c(1:8, 19:20, 9:18)]SESデータを結合する
setwd("/Users/koyo/Dropbox/191004_SES_Trial/000_Gakku_SES")
ses <- read.csv("Gakku_SES.csv", fileEncoding = "Shift_JIS", stringsAsFactors=FALSE)
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/191023_CS_SES")
ses <- ses[c(5:13)]
colnames(ses) <- c("schlname", "setai.n", "nenshu.m", "nenshu.sd",
"sotsu.n", "tan.n", "dai.n", "tan.p", "dai.p")
csnc.ses <- full_join(csnc.6y, ses, by = "schlname")
write.csv(csnc.ses, "csnc.ses.csv", fileEncoding = "Shift_JIS")学力データの整形
NRTのデータを読み込む
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/04_NRT/SY2018")
nrt.all <- read.csv("nrt.csv", fileEncoding = "Shift_JIS")
setwd("/Users/koyo/Dropbox/000078_CSKAKEN/191023_CS_SES")国語だけを取り出す
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 <- na.omit(nrt.koku)
nrt.koku <- nrt.koku %>%
mutate(id = row_number())社会だけを取り出す
nrt.shak <- nrt.all %>%
dplyr::select(c("renban", "sho.sid",
"shak.ss.1415",
"shak.ss.1516",
"shak.ss.1617",
"shak.ss.1718" #6-7年生追加
))
colnames(nrt.shak) <- c("renban", "sid",
"g34.shak",
"g45.shak",
"g56.shak",
"g67.shak"
)
nrt.shak <- na.omit(nrt.shak)
nrt.shak <- nrt.shak %>%
mutate(id = row_number())NRTデータのある学校の番号を取り出す
nrt.koku.schl <- data.frame(unique(nrt.koku$sid)); colnames(nrt.koku.schl) <- c("sid.new")
nrt.shak.schl <- data.frame(unique(nrt.shak$sid)); colnames(nrt.shak.schl) <- c("sid.new")分析対象校のクラスサイズなどの表を作る
統廃合校を除外するなどの前処理
csnc.ses.taisho <- dplyr::filter(csnc.ses, taisho.g1 == 1 &
togo == 0 & nonrt == 0 & fuku == 0)
csnc.ses.taisho <- na.omit(csnc.ses.taisho)国語，社会の教科別の対象校に限定した分析対象校のクラスサイズなどの表
library(dplyr)
# 国語対象校
koku.csnc.ses_ <- inner_join(nrt.koku.schl, csnc.ses.taisho, by = "sid.new")
## 必要な列だけを取り出して列名を付け替える
koku.csnc.ses <- koku.csnc.ses_[,c(1, 9:28)]
colnames(koku.csnc.ses) <- c("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",
"setai.n", "nenshu.m", "nenshu.sd", "sotsu.n",
"tan.n", "dai.n", "tan.p", "dai.p")
# 社会対象校
shak.csnc.ses_ <- inner_join(nrt.shak.schl, csnc.ses.taisho, by = "sid.new")
## 必要な列だけを取り出して列名を付け替える
shak.csnc.ses <- shak.csnc.ses_[,c(1, 9:28)]
colnames(shak.csnc.ses) <- c("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",
"setai.n", "nenshu.m", "nenshu.sd", "sotsu.n",
"tan.n", "dai.n", "tan.p", "dai.p")クラスサイズなどの中心化
# 国語
## クラスサイズを中心化する
### 各学年での平均
koku.csnc.ses <- dplyr::mutate(koku.csnc.ses,
csm = (cs.g1 + cs.g2 + cs.g3 + cs.g4 + cs.g5 + cs.g6)/6)
### 中心化
koku.csm <- mean(koku.csnc.ses$csm)
koku.csnc.ses$cs.c.g1 <- koku.csnc.ses$cs.g1 - mean(koku.csm)
koku.csnc.ses$cs.c.g2 <- koku.csnc.ses$cs.g2 - mean(koku.csm)
koku.csnc.ses$cs.c.g3 <- koku.csnc.ses$cs.g3 - mean(koku.csm)
koku.csnc.ses$cs.c.g4 <- koku.csnc.ses$cs.g4 - mean(koku.csm)
koku.csnc.ses$cs.c.g5 <- koku.csnc.ses$cs.g5 - mean(koku.csm)
koku.csnc.ses$cs.c.g6 <- koku.csnc.ses$cs.g6 - mean(koku.csm)
## 世帯年収を中心化する
koku.nenshu.mean <- mean(koku.csnc.ses$nenshu.m)
koku.csnc.ses$nenshu.c <- koku.csnc.ses$nenshu.m - koku.nenshu.mean
# 短大高専卒業率を中心化する
koku.tan.p.mean <- mean(koku.csnc.ses$tan.p)
koku.csnc.ses$tan.c <- koku.csnc.ses$tan.p - koku.tan.p.mean
# 大学大学院卒業率を中心化する
koku.dai.p.mean <- mean(koku.csnc.ses$dai.p)
koku.csnc.ses$dai.c <- koku.csnc.ses$dai.p - koku.dai.p.mean
### おまけ：クラスサイズ変動差分データ列作成
koku.csnc.ses$cs.d12 <- koku.csnc.ses$cs.g2 - koku.csnc.ses$cs.g1
koku.csnc.ses$cs.d23 <- koku.csnc.ses$cs.g3 - koku.csnc.ses$cs.g2
koku.csnc.ses$cs.d34 <- koku.csnc.ses$cs.g4 - koku.csnc.ses$cs.g3
koku.csnc.ses$cs.d45 <- koku.csnc.ses$cs.g5 - koku.csnc.ses$cs.g4
koku.csnc.ses$cs.d56 <- koku.csnc.ses$cs.g6 - koku.csnc.ses$cs.g5
# 社会
## クラスサイズを中心化する
### 各学年での平均
shak.csnc.ses <- dplyr::mutate(shak.csnc.ses,
csm = (cs.g1 + cs.g2 + cs.g3 + cs.g4 + cs.g5 + cs.g6)/6)
### 中心化
shak.csm <- mean(shak.csnc.ses$csm)
shak.csnc.ses$cs.c.g1 <- shak.csnc.ses$cs.g1 - mean(shak.csm)
shak.csnc.ses$cs.c.g2 <- shak.csnc.ses$cs.g2 - mean(shak.csm)
shak.csnc.ses$cs.c.g3 <- shak.csnc.ses$cs.g3 - mean(shak.csm)
shak.csnc.ses$cs.c.g4 <- shak.csnc.ses$cs.g4 - mean(shak.csm)
shak.csnc.ses$cs.c.g5 <- shak.csnc.ses$cs.g5 - mean(shak.csm)
shak.csnc.ses$cs.c.g6 <- shak.csnc.ses$cs.g6 - mean(shak.csm)
## 世帯年収を中心化する
shak.nenshu.mean <- mean(shak.csnc.ses$nenshu.m)
shak.csnc.ses$nenshu.c <- shak.csnc.ses$nenshu.m - shak.nenshu.mean
# 短大高専卒業率を中心化する
shak.tan.p.mean <- mean(shak.csnc.ses$tan.p)
shak.csnc.ses$tan.c <- shak.csnc.ses$tan.p - shak.tan.p.mean
# 大学大学院卒業率を中心化する
shak.dai.p.mean <- mean(shak.csnc.ses$dai.p)
shak.csnc.ses$dai.c <- shak.csnc.ses$dai.p - shak.dai.p.mean
### おまけ：クラスサイズ変動差分データ列作成
shak.csnc.ses$cs.d34 <- shak.csnc.ses$cs.g4 - shak.csnc.ses$cs.g3
shak.csnc.ses$cs.d45 <- shak.csnc.ses$cs.g5 - shak.csnc.ses$cs.g4
shak.csnc.ses$cs.d56 <- shak.csnc.ses$cs.g6 - shak.csnc.ses$cs.g5 縦断データの作成
国語
# 1-2年生
koku.nrt.12_ <- nrt.koku[c("id", "sid", "g12.koku")]
koku.nrt.12_$year <- 0
koku.csnc.1 <- koku.csnc.ses[c("sid", "nc.g1", "cs.g1", "cs.c.g1", "nenshu.c", "tan.c", "dai.c")]
koku.csnc.1$cs.d01 <- 0 # ここは1年生時用の特殊処理
koku.nrt.12 <- dplyr::inner_join(koku.csnc.1, koku.nrt.12_, by = "sid")
koku.nrt.12 <- koku.nrt.12[c( "id", "sid", "nc.g1" , "cs.g1", "cs.c.g1", "cs.d01", "nenshu.c", "tan.c", "dai.c", "g12.koku", "year")]
colnames(koku.nrt.12) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "koku", "year")
#2-3年生
koku.nrt.23_ <- nrt.koku[c("id", "sid", "g23.koku")]
koku.nrt.23_$year <- 1
koku.csnc.2 <- koku.csnc.ses[c("sid", "nc.g2", "cs.g2", "cs.c.g2", "cs.d12", "nenshu.c", "tan.c", "dai.c")]
koku.nrt.23 <- dplyr::inner_join(koku.csnc.2, koku.nrt.23_, by = "sid")
koku.nrt.23 <- koku.nrt.23[c( "id", "sid", "nc.g2" , "cs.g2", "cs.c.g2", "cs.d12", "nenshu.c", "tan.c", "dai.c", "g23.koku", "year")]
colnames(koku.nrt.23) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "koku", "year")
#3-4年生
koku.nrt.34_ <- nrt.koku[c("id", "sid", "g34.koku")]
koku.nrt.34_$year <- 2
koku.csnc.3 <- koku.csnc.ses[c("sid", "nc.g3", "cs.g3", "cs.c.g3", "cs.d23", "nenshu.c", "tan.c", "dai.c")]
koku.nrt.34 <- dplyr::inner_join(koku.csnc.3, koku.nrt.34_, by = "sid")
koku.nrt.34 <- koku.nrt.34[c( "id", "sid", "nc.g3" , "cs.g3", "cs.c.g3", "cs.d23", "nenshu.c", "tan.c", "dai.c", "g34.koku", "year")]
colnames(koku.nrt.34) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "koku", "year")
#4-5年生
koku.nrt.45_ <- nrt.koku[c("id", "sid", "g45.koku")]
koku.nrt.45_$year <- 3
koku.csnc.4 <- koku.csnc.ses[c("sid", "nc.g4", "cs.g4", "cs.c.g4", "cs.d34", "nenshu.c", "tan.c", "dai.c")]
koku.nrt.45 <- dplyr::inner_join(koku.csnc.4, koku.nrt.45_, by = "sid")
koku.nrt.45 <- koku.nrt.45[c( "id", "sid", "nc.g4" , "cs.g4", "cs.c.g4", "cs.d34", "nenshu.c", "tan.c", "dai.c", "g45.koku", "year")]
colnames(koku.nrt.45) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "koku", "year")
#5-6年生
koku.nrt.56_ <- nrt.koku[c("id", "sid", "g56.koku")]
koku.nrt.56_$year <- 4
koku.csnc.5 <- koku.csnc.ses[c("sid", "nc.g5", "cs.g5", "cs.c.g5", "cs.d45", "nenshu.c", "tan.c", "dai.c")]
koku.nrt.56 <- dplyr::inner_join(koku.csnc.5, koku.nrt.56_, by = "sid")
koku.nrt.56 <- koku.nrt.56[c( "id", "sid", "nc.g5" , "cs.g5", "cs.c.g5", "cs.d45", "nenshu.c", "tan.c", "dai.c", "g56.koku", "year")]
colnames(koku.nrt.56) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "koku", "year")
#6-7年生
koku.nrt.67_ <- nrt.koku[c("id", "sid", "g67.koku")]
koku.nrt.67_$year <- 5
koku.csnc.6 <- koku.csnc.ses[c("sid", "nc.g6", "cs.g6", "cs.c.g6", "cs.d56", "nenshu.c", "tan.c", "dai.c")]
koku.nrt.67 <- dplyr::inner_join(koku.csnc.6, koku.nrt.67_, by = "sid")
koku.nrt.67 <- koku.nrt.67[c( "id", "sid", "nc.g6" , "cs.g6", "cs.c.g6", "cs.d56", "nenshu.c", "tan.c", "dai.c", "g67.koku", "year")]
colnames(koku.nrt.67) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "koku", "year")
# うぃーんがっしゃん
koku.nrt.lt <- dplyr::bind_rows(koku.nrt.12, koku.nrt.23, koku.nrt.34, koku.nrt.45, koku.nrt.56, koku.nrt.67)社会
#3-4年生
shak.nrt.34_ <- nrt.shak[c("id", "sid", "g34.shak")]
shak.nrt.34_$year <- 0
shak.csnc.3 <- shak.csnc.ses[c("sid", "nc.g3", "cs.g3", "cs.c.g3", "nenshu.c", "tan.c", "dai.c")]
shak.csnc.3$cs.d23 <- 0 # ここは3年生時用の特殊処理
shak.nrt.34 <- dplyr::inner_join(shak.csnc.3, shak.nrt.34_, by = "sid")
shak.nrt.34 <- shak.nrt.34[c( "id", "sid", "nc.g3" , "cs.g3", "cs.c.g3", "cs.d23", "nenshu.c", "tan.c", "dai.c", "g34.shak", "year")]
colnames(shak.nrt.34) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "shak", "year")
#4-5年生
shak.nrt.45_ <- nrt.shak[c("id", "sid", "g45.shak")]
shak.nrt.45_$year <- 1
shak.csnc.4 <- shak.csnc.ses[c("sid", "nc.g4", "cs.g4", "cs.c.g4", "cs.d34", "nenshu.c", "tan.c", "dai.c")]
shak.nrt.45 <- dplyr::inner_join(shak.csnc.4, shak.nrt.45_, by = "sid")
shak.nrt.45 <- shak.nrt.45[c( "id", "sid", "nc.g4" , "cs.g4", "cs.c.g4", "cs.d34", "nenshu.c", "tan.c", "dai.c", "g45.shak", "year")]
colnames(shak.nrt.45) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "shak", "year")
#5-6年生
shak.nrt.56_ <- nrt.shak[c("id", "sid", "g56.shak")]
shak.nrt.56_$year <- 2
shak.csnc.5 <- shak.csnc.ses[c("sid", "nc.g5", "cs.g5", "cs.c.g5", "cs.d45", "nenshu.c", "tan.c", "dai.c")]
shak.nrt.56 <- dplyr::inner_join(shak.csnc.5, shak.nrt.56_, by = "sid")
shak.nrt.56 <- shak.nrt.56[c( "id", "sid", "nc.g5" , "cs.g5", "cs.c.g5", "cs.d45", "nenshu.c", "tan.c", "dai.c", "g56.shak", "year")]
colnames(shak.nrt.56) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "shak", "year")
#6-7年生
shak.nrt.67_ <- nrt.shak[c("id", "sid", "g67.shak")]
shak.nrt.67_$year <- 3
shak.csnc.6 <- shak.csnc.ses[c("sid", "nc.g6", "cs.g6", "cs.c.g6", "cs.d56", "nenshu.c", "tan.c", "dai.c")]
shak.nrt.67 <- dplyr::inner_join(shak.csnc.6, shak.nrt.67_, by = "sid")
shak.nrt.67 <- shak.nrt.67[c( "id", "sid", "nc.g6" , "cs.g6", "cs.c.g6", "cs.d56", "nenshu.c", "tan.c", "dai.c", "g67.shak", "year")]
colnames(shak.nrt.67) <- c("id", "sid", "nc", "cs", "cs.c", "cs.d", "nenshu.c", "tan.c", "dai.c", "shak", "year")
# うぃーんがっしゃん
shak.nrt.lt <- dplyr::bind_rows(shak.nrt.34, shak.nrt.45, shak.nrt.56, shak.nrt.67)分布の図
par(oma = c(0, 0, 4, 0))
par(mfrow=c(2,3))
hist(koku.csnc.ses$cs.g1, xlim = c(0, 40), ylim = c(0,50))
hist(koku.csnc.ses$cs.g2, xlim = c(0, 40), ylim = c(0,50))
hist(koku.csnc.ses$cs.g3, xlim = c(0, 40), ylim = c(0,50))
hist(koku.csnc.ses$cs.g4, xlim = c(0, 40), ylim = c(0,50))
hist(koku.csnc.ses$cs.g5, xlim = c(0, 40), ylim = c(0,50))
hist(koku.csnc.ses$cs.g6, xlim = c(0, 40), ylim = c(0,50))
mtext(side = 3, line=1, outer=T, text = "Histgram of Class Size (Kokugo)", cex=1.5)
par(oma = c(0, 0, 4, 0))
par(mfrow=c(2,2))
hist(shak.csnc.ses$cs.g3, xlim = c(0, 40), ylim = c(0,20))
hist(shak.csnc.ses$cs.g4, xlim = c(0, 40), ylim = c(0,20))
hist(shak.csnc.ses$cs.g5, xlim = c(0, 40), ylim = c(0,20))
hist(shak.csnc.ses$cs.g6, xlim = c(0, 40), ylim = c(0,20))
mtext(side = 3, line=1, outer=T, text = "Histgram of Class Size (Shakai)", cex=1.5)
par(oma = c(0, 0, 4, 0))
par(mfrow=c(1,3))
hist(koku.csnc.ses$nenshu.m, xlim = c(300, 600), ylim = c(0,50), main = "Annual income")
hist(koku.csnc.ses$tan.p, xlim = c(0, 0.5), ylim = c(0,50), main = "Graduation rate (2-year higher edu.)")
hist(koku.csnc.ses$dai.p, xlim = c(0, 0.5), ylim = c(0,50), main = "Graduation rate (tertiary edu.)")
mtext(side = 3, line=1, outer=T, text = "Histgram of SES Alternative Indicators School Mean (Kokugo)", cex=1.5)
par(oma = c(0, 0, 4, 0))
par(mfrow=c(1,3))
hist(shak.csnc.ses$nenshu.m, xlim = c(300, 600), ylim = c(0,20), main = "Annual income")
hist(shak.csnc.ses$tan.p, xlim = c(0, 0.5), ylim = c(0,20), main = "Graduation rate (2-year higher edu.)")
hist(shak.csnc.ses$dai.p, xlim = c(0, 0.5), ylim = c(0,20), main = "Graduation rate (tertiary edu.)")
mtext(side = 3, line=1, outer=T, text = "Histgram of SES Alternative Indicators School Mean (Shakai)", cex=1.5)
分析する
library(lmerTest)## Loading required package: lme4## Loading required package: Matrix##
## Attaching package: 'lmerTest'## The following object is masked from 'package:lme4':
##
## lmer## The following object is masked from 'package:stats':
##
## step国語
# クラスサイズだけ投入
koku.res1 <- lmer(koku ~ year + cs.c + year:cs.c + (1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値にだけ年収が影響
koku.res2 <- lmer(koku ~ year + nenshu.c + cs.c + year:cs.c + (1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値と推移に年収が影響
koku.res3 <- lmer(koku ~ year + nenshu.c + cs.c + year:nenshu.c + year:cs.c +(1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値に年収とクラスサイズの交互作用が影響
koku.res4 <- lmer(koku ~ year + nenshu.c + cs.c +
nenshu.c:cs.c +
year:nenshu.c +
year:cs.c +
(1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値と推移に年収とクラスサイズの交互作用が影響
koku.res5 <- lmer(koku ~ year + nenshu.c + cs.c +
nenshu.c:cs.c +
year:nenshu.c +
year:cs.c +
year:cs.c:nenshu.c +
(1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値にだけ大卒率が影響
koku.res6 <- lmer(koku ~ year + dai.c + cs.c + year:cs.c + (1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値と推移に大卒率が影響
koku.res7 <- lmer(koku ~ year + dai.c + cs.c + year:dai.c + year:cs.c +(1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値に大卒率とクラスサイズの交互作用が影響
koku.res8 <- lmer(koku ~ year + dai.c + cs.c +
dai.c:cs.c +
year:dai.c +
year:cs.c +
(1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 初期値と推移に大卒率とクラスサイズの交互作用が影響
koku.res9 <- lmer(koku ~ year + dai.c + cs.c +
dai.c:cs.c +
year:dai.c +
year:cs.c +
year:cs.c:dai.c +
(1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)# 推移に大卒率とクラスサイズの交互作用が影響
koku.res10 <- lmer(koku ~ year + dai.c + cs.c +
year:dai.c +
year:cs.c +
year:cs.c:dai.c +
(1|id) + (1|sid),
data = koku.nrt.lt,
REML = FALSE
)print(koku.res1, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: koku ~ year + cs.c + year:cs.c + (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159427.4 159484.3 -79706.7 159413.4 25091
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.49
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year cs.c year:cs.c
## 53.6936 -0.2150 -0.0162 -0.0109print(koku.res2, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: koku ~ year + nenshu.c + cs.c + year:cs.c + (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159427.8 159492.9 -79705.9 159411.8 25090
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.46
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year nenshu.c cs.c year:cs.c
## 53.6585 -0.2148 -0.0104 -0.0177 -0.0109print(koku.res3, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: koku ~ year + nenshu.c + cs.c + year:nenshu.c + year:cs.c + (1 |
## id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159421.95 159495.13 -79701.98 159403.95 25089
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.46
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year nenshu.c cs.c year:nenshu.c
## 53.68585 -0.22498 -0.00518 -0.01356 -0.00210
## year:cs.c
## -0.01285print(koku.res4, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: koku ~ year + nenshu.c + cs.c + nenshu.c:cs.c + year:nenshu.c +
## year:cs.c + (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159415.78 159497.09 -79697.89 159395.78 25088
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.46
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year nenshu.c cs.c nenshu.c:cs.c
## 53.71352 -0.22676 -0.00750 0.00397 0.00172
## year:nenshu.c year:cs.c
## -0.00231 -0.01270print(koku.res5, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: koku ~ year + nenshu.c + cs.c + nenshu.c:cs.c + year:nenshu.c +
## year:cs.c + year:cs.c:nenshu.c + (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159416.94 159506.37 -79697.47 159394.94 25087
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.46
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year nenshu.c
## 53.723305 -0.231102 -0.008458
## cs.c nenshu.c:cs.c year:nenshu.c
## 0.002769 0.002056 -0.001879
## year:cs.c year:nenshu.c:cs.c
## -0.012402 -0.000147print(koku.res6, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: koku ~ year + dai.c + cs.c + year:cs.c + (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159429.33 159494.38 -79706.67 159413.33 25090
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.49
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year dai.c cs.c year:cs.c
## 53.6848 -0.2149 1.4750 -0.0167 -0.0109print(koku.res7, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: koku ~ year + dai.c + cs.c + year:dai.c + year:cs.c + (1 | id) +
## (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159416.6 159489.8 -79699.3 159398.6 25089
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.49
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year dai.c cs.c year:dai.c
## 53.72877 -0.23108 -3.75357 -0.00583 2.13671
## year:cs.c
## -0.01577print(koku.res8, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula:
## koku ~ year + dai.c + cs.c + dai.c:cs.c + year:dai.c + year:cs.c +
## (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159412.59 159493.90 -79696.29 159392.59 25088
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.50
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year dai.c cs.c dai.c:cs.c
## 53.77255 -0.23311 -3.04494 0.00515 -0.79625
## year:dai.c year:cs.c
## 2.43970 -0.01625print(koku.res9, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula:
## koku ~ year + dai.c + cs.c + dai.c:cs.c + year:dai.c + year:cs.c +
## year:cs.c:dai.c + (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159411.4 159500.8 -79694.7 159389.4 25087
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.50
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year dai.c cs.c
## 53.80209 -0.24486 -1.62631 0.00503
## dai.c:cs.c year:dai.c year:cs.c year:dai.c:cs.c
## -1.31385 1.78689 -0.01599 0.19891print(koku.res10, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula:
## koku ~ year + dai.c + cs.c + year:dai.c + year:cs.c + year:cs.c:dai.c +
## (1 | id) + (1 | sid)
## Data: koku.nrt.lt
## AIC BIC logLik deviance df.resid
## 159418.50 159499.81 -79699.25 159398.50 25088
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 7.67
## sid (Intercept) 1.49
## Residual 4.54
## Number of obs: 25098, groups: id, 4183; sid, 118
## Fixed Effects:
## (Intercept) year dai.c cs.c
## 53.72860 -0.22977 -3.87207 -0.00493
## year:dai.c year:cs.c year:dai.c:cs.c
## 2.24319 -0.01584 -0.02492koku.fit <- matrix(c(AIC(koku.res1), AIC(koku.res2), AIC(koku.res3),
AIC(koku.res4), AIC(koku.res5), AIC(koku.res6),
AIC(koku.res7), AIC(koku.res8), AIC(koku.res9),
AIC(koku.res10),
BIC(koku.res1), BIC(koku.res2), BIC(koku.res3),
BIC(koku.res4), BIC(koku.res5), BIC(koku.res6),
BIC(koku.res7), BIC(koku.res8), BIC(koku.res9),
BIC(koku.res10)),
nrow = 10, ncol = 2)
koku.fit ## [,1] [,2]
## [1,] 159427.4 159484.3
## [2,] 159427.8 159492.9
## [3,] 159422.0 159495.1
## [4,] 159415.8 159497.1
## [5,] 159416.9 159506.4
## [6,] 159429.3 159494.4
## [7,] 159416.6 159489.8
## [8,] 159412.6 159493.9
## [9,] 159411.4 159500.8
## [10,] 159418.5 159499.8社会
# クラスサイズだけ投入
shak.res1 <- lmer(shak ~ year + cs.c + year:cs.c + (1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値にだけ年収が影響
shak.res2 <- lmer(shak ~ year + nenshu.c + cs.c + year:cs.c + (1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値と推移に年収が影響
shak.res3 <- lmer(shak ~ year + nenshu.c + cs.c + year:nenshu.c + year:cs.c +(1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値に年収とクラスサイズの交互作用が影響
shak.res4 <- lmer(shak ~ year + nenshu.c + cs.c +
nenshu.c:cs.c +
year:nenshu.c +
year:cs.c +
(1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値と推移に年収とクラスサイズの交互作用が影響
shak.res5 <- lmer(shak ~ year + nenshu.c + cs.c +
nenshu.c:cs.c +
year:nenshu.c +
year:cs.c +
year:cs.c:nenshu.c +
(1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値にだけ大卒率が影響
shak.res6 <- lmer(shak ~ year + dai.c + cs.c + year:cs.c + (1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値と推移に大卒率が影響
shak.res7 <- lmer(shak ~ year + dai.c + cs.c + year:dai.c + year:cs.c +(1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値に大卒率とクラスサイズの交互作用が影響
shak.res8 <- lmer(shak ~ year + dai.c + cs.c +
dai.c:cs.c +
year:dai.c +
year:cs.c +
(1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 初期値と推移に大卒率とクラスサイズの交互作用が影響
shak.res9 <- lmer(shak ~ year + dai.c + cs.c +
dai.c:cs.c +
year:dai.c +
year:cs.c +
year:cs.c:dai.c +
(1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)# 推移に大卒率とクラスサイズの交互作用が影響
shak.res10 <- lmer(shak ~ year + dai.c + cs.c +
year:dai.c +
year:cs.c +
year:cs.c:dai.c +
(1|id) + (1|sid),
data = shak.nrt.lt,
REML = FALSE
)print(shak.res1, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: shak ~ year + cs.c + year:cs.c + (1 | id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45235.93 45283.51 -22610.96 45221.93 6609
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.57
## Residual 5.39
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year cs.c year:cs.c
## 52.3548 0.1004 0.0224 -0.0225print(shak.res2, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: shak ~ year + nenshu.c + cs.c + year:cs.c + (1 | id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45237.84 45292.22 -22610.92 45221.84 6608
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.57
## Residual 5.39
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year nenshu.c cs.c year:cs.c
## 52.36846 0.10067 0.00575 0.02423 -0.02246print(shak.res3, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: shak ~ year + nenshu.c + cs.c + year:nenshu.c + year:cs.c + (1 |
## id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45239.57 45300.74 -22610.78 45221.57 6607
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.57
## Residual 5.39
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year nenshu.c cs.c year:nenshu.c
## 52.36274 0.10470 0.00285 0.02052 0.00196
## year:cs.c
## -0.01973print(shak.res4, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: shak ~ year + nenshu.c + cs.c + nenshu.c:cs.c + year:nenshu.c +
## year:cs.c + (1 | id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45238.06 45306.03 -22609.03 45218.06 6606
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.75
## Residual 5.38
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year nenshu.c cs.c nenshu.c:cs.c
## 52.180026 0.101648 -0.000291 -0.011045 -0.004713
## year:nenshu.c year:cs.c
## 0.001500 -0.019294print(shak.res5, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: shak ~ year + nenshu.c + cs.c + nenshu.c:cs.c + year:nenshu.c +
## year:cs.c + year:cs.c:nenshu.c + (1 | id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45218.63 45293.40 -22598.32 45196.63 6605
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.71
## Residual 5.37
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year nenshu.c
## 52.02298 0.22272 0.00658
## cs.c nenshu.c:cs.c year:nenshu.c
## -0.01806 -0.00877 -0.00242
## year:cs.c year:nenshu.c:cs.c
## -0.01265 0.00313print(shak.res6, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: shak ~ year + dai.c + cs.c + year:cs.c + (1 | id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45237.91 45292.28 -22610.95 45221.91 6608
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.56
## Residual 5.39
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year dai.c cs.c year:cs.c
## 52.3452 0.1002 1.7616 0.0213 -0.0226print(shak.res7, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula: shak ~ year + dai.c + cs.c + year:dai.c + year:cs.c + (1 | id) +
## (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45233.15 45294.33 -22607.58 45215.15 6607
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.53
## Residual 5.38
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year dai.c cs.c year:dai.c
## 52.4035 0.0642 -6.3661 0.0339 6.0588
## year:cs.c
## -0.0368print(shak.res8, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula:
## shak ~ year + dai.c + cs.c + dai.c:cs.c + year:dai.c + year:cs.c +
## (1 | id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45235.15 45303.13 -22607.58 45215.15 6606
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.53
## Residual 5.38
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year dai.c cs.c dai.c:cs.c
## 52.40379 0.06425 -6.34899 0.03391 -0.00532
## year:dai.c year:cs.c
## 6.05733 -0.03678print(shak.res9, digits = 3)## Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
## Formula:
## shak ~ year + dai.c + cs.c + dai.c:cs.c + year:dai.c + year:cs.c +
## year:cs.c:dai.c + (1 | id) + (1 | sid)
## Data: shak.nrt.lt
## AIC BIC logLik deviance df.resid
## 45234.91 45309.68 -22606.45 45212.91 6605
## Random effects:
## Groups Name Std.Dev.
## id (Intercept) 9.00
## sid (Intercept) 1.53
## Residual 5.38
## Number of obs: 6616, groups: id, 1654; sid, 48
## Fixed Effects:
## (Intercept) year dai.c cs.c
## 52.3256 0.1052 -11.7356 0.0430
## dai.c:cs.c year:dai.c year:cs.c year:dai.c:cs.c
## 1.2280 8.9309 -0.0411 -0.6757shak.fit <- matrix(c(AIC(shak.res1), AIC(shak.res2), AIC(shak.res3),
AIC(shak.res4), AIC(shak.res5), AIC(shak.res6),
AIC(shak.res7), AIC(shak.res8), AIC(shak.res9),
AIC(shak.res10),
BIC(shak.res1), BIC(shak.res2), BIC(shak.res3),
BIC(shak.res4), BIC(shak.res5), BIC(shak.res6),
BIC(shak.res7), BIC(shak.res8), BIC(shak.res9),
BIC(shak.res10)),
nrow = 10, ncol = 2)
shak.fit## [,1] [,2]
## [1,] 45235.93 45283.51
## [2,] 45237.84 45292.22
## [3,] 45239.57 45300.74
## [4,] 45238.06 45306.03
## [5,] 45218.63 45293.40
## [6,] 45237.91 45292.28
## [7,] 45233.15 45294.33
## [8,] 45235.15 45303.13
## [9,] 45234.91 45309.68
## [10,] 45233.52 45301.49結果
国語はモデル9（初期値と推移に大卒率とクラスサイズの交互作用が影響）
社会はモデル7（初期値と推移に大卒率が影響）
summary(koku.res9, digits = 3)## Warning in summary.merMod(as(object, "lmerMod"), ...): additional arguments
## ignored## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula:
## koku ~ year + dai.c + cs.c + dai.c:cs.c + year:dai.c + year:cs.c +
## year:cs.c:dai.c + (1 | id) + (1 | sid)
## Data: koku.nrt.lt
##
## AIC BIC logLik deviance df.resid
## 159411.4 159500.8 -79694.7 159389.4 25087
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.3294 -0.5509 0.0419 0.5959 5.0165
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 58.848 7.671
## sid (Intercept) 2.257 1.502
## Residual 20.622 4.541
## Number of obs: 25098, groups: id, 4183; sid, 118
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.380e+01 2.062e-01 1.269e+02 260.892 < 2e-16 ***
## year -2.449e-01 2.079e-02 2.099e+04 -11.779 < 2e-16 ***
## dai.c -1.626e+00 6.324e+00 1.019e+02 -0.257 0.79757
## cs.c 5.035e-03 1.576e-02 9.685e+03 0.319 0.74937
## dai.c:cs.c -1.314e+00 4.353e-01 1.688e+04 -3.018 0.00255 **
## year:dai.c 1.787e+00 6.771e-01 2.102e+04 2.639 0.00832 **
## year:cs.c -1.599e-02 3.482e-03 2.098e+04 -4.591 4.44e-06 ***
## year:dai.c:cs.c 1.989e-01 1.114e-01 2.099e+04 1.785 0.07422 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) year dai.c cs.c d.c:c. yr:d.c yr:cs.
## year -0.247
## dai.c -0.167 0.014
## cs.c -0.069 0.120 -0.130
## dai.c:cs.c -0.117 0.241 -0.119 -0.208
## year:dai.c 0.018 -0.002 -0.236 0.201 0.223
## year:cs.c 0.076 -0.354 0.076 -0.546 0.014 -0.332
## yr:d.c:cs.c 0.080 -0.317 0.126 -0.004 -0.666 -0.540 0.043summary(shak.res7, digits = 3)## Warning in summary.merMod(as(object, "lmerMod"), ...): additional arguments
## ignored## Linear mixed model fit by maximum likelihood . t-tests use
## Satterthwaite's method [lmerModLmerTest]
## Formula: shak ~ year + dai.c + cs.c + year:dai.c + year:cs.c + (1 | id) +
## (1 | sid)
## Data: shak.nrt.lt
##
## AIC BIC logLik deviance df.resid
## 45233.2 45294.3 -22607.6 45215.2 6607
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.0469 -0.4938 0.0574 0.5393 4.8163
##
## Random effects:
## Groups Name Variance Std.Dev.
## id (Intercept) 81.073 9.004
## sid (Intercept) 2.343 1.531
## Residual 28.988 5.384
## Number of obs: 6616, groups: id, 1654; sid, 48
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 52.40348 0.36589 44.71912 143.220 < 2e-16 ***
## year 0.06424 0.06972 4980.98736 0.922 0.35683
## dai.c -6.36614 12.69402 31.80601 -0.502 0.61947
## cs.c 0.03390 0.03455 1559.39170 0.981 0.32661
## year:dai.c 6.05880 2.32973 4980.77741 2.601 0.00933 **
## year:cs.c -0.03678 0.01298 4991.94461 -2.833 0.00463 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) year dai.c cs.c yr:d.c
## year -0.300
## dai.c -0.190 0.038
## cs.c -0.179 0.273 -0.220
## year:dai.c 0.063 -0.198 -0.251 0.147
## year:cs.c 0.100 -0.351 0.086 -0.464 -0.421グラフを描いてみる
クラスサイズと大卒率の記述統計量を教科別に求める
library(psych)
# クラスサイズ
# 分析対象校の1-6学年までの平均クラスサイズ平均とSD
koku.cs.desc <- describe(koku.csnc.ses$csm)
koku.cs.desc## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 118 22.92 6.77 24.39 23.42 6.69 6.83 32.83 26 -0.59 -0.7
## se
## X1 0.62shak.cs.desc <- describe(shak.csnc.ses$csm)
shak.cs.desc## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 48 22.06 5.98 23.25 22.32 6.86 9 32.33 23.33 -0.4 -0.94
## se
## X1 0.86# 大卒率
koku.dai.desc <- describe(koku.csnc.ses$dai.p)
koku.dai.desc## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 118 0.08 0.03 0.07 0.07 0.03 0.03 0.17 0.14 0.91 0.36 0shak.dai.desc <- describe(shak.csnc.ses$dai.p)
shak.dai.desc## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 48 0.07 0.03 0.07 0.07 0.03 0.03 0.16 0.13 1.03 1.15 0グラフ用のクラスサイズと大卒率の表
# 国語
koku.gra.tab <- data.frame(
matrix(c(koku.cs.desc$mean - koku.cs.desc$sd, koku.cs.desc$mean, koku.cs.desc$mean + koku.cs.desc$sd,
koku.dai.desc$mean - koku.dai.desc$sd, koku.dai.desc$mean, koku.dai.desc$mean + koku.dai.desc$sd,
-1* koku.cs.desc$sd, 0, koku.cs.desc$sd,
-1* koku.dai.desc$sd, 0, koku.dai.desc$sd),
nrow = 3, ncol = 4)
)
colnames(koku.gra.tab) <- c("cs", "dai", "cs.c", "dai.c")
rownames(koku.gra.tab) <- c("-1SD", "M", "+1SD")
# 社会
shak.gra.tab <- data.frame(
matrix(c(shak.cs.desc$mean - shak.cs.desc$sd, shak.cs.desc$mean, shak.cs.desc$mean + shak.cs.desc$sd,
shak.dai.desc$mean - shak.dai.desc$sd, shak.dai.desc$mean, shak.dai.desc$mean + shak.dai.desc$sd,
-1* shak.cs.desc$sd, 0, shak.cs.desc$sd,
-1* shak.dai.desc$sd, 0, shak.dai.desc$sd),
nrow = 3, ncol = 4)
)
colnames(shak.gra.tab) <- c("cs", "dai", "cs.c", "dai.c")
rownames(shak.gra.tab) <- c("-1SD", "M", "+1SD")推定値からプロットするための数値を得る
国語のデータ
# 国語はモデル9（初期値と推移に大卒率とクラスサイズの交互作用が影響）
## koku.res9 <- lmer(koku ~ year + dai.c + cs.c +
## dai.c:cs.c +
## year:dai.c +
## year:cs.c +
## year:cs.c:dai.c
# 係数
koku.keisu <- data.frame(
matrix(c(koku.res9@pp$delb[1],
koku.res9@pp$delb[2],
koku.res9@pp$delb[3],
koku.res9@pp$delb[4],
koku.res9@pp$delb[5],
koku.res9@pp$delb[6],
koku.res9@pp$delb[7],
koku.res9@pp$delb[8]),
nrow = 1, ncol = 8))
colnames(koku.keisu) <- c("Intercept", "year",
"dai.c", "cs.c", "dai.c:cs.c",
"year:dai.c", "year:cs.c", "year:dai.c:cs.c")
# 大卒率高 クラスサイズ小
## t0, cs = small
year <- 0; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[1,3]
daih.css.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = small
year <- 1; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[1,3]
daih.css.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = small
year <- 2; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[1,3]
daih.css.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = small
year <- 3; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[1,3]
daih.css.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = small
year <- 4; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[1,3]
daih.css.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = small
year <- 5; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[1,3]
daih.css.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率高 クラスサイズ中
## t0, cs = medium
year <- 0; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[2,3]
daih.csm.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = medium
year <- 1; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[2,3]
daih.csm.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = medium
year <- 2; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[2,3]
daih.csm.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = medium
year <- 3; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[2,3]
daih.csm.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = medium
year <- 4; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[2,3]
daih.csm.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = medium
year <- 5; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[2,3]
daih.csm.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率高 クラスサイズ大
## t0, cs = large
year <- 0; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[3,3]
daih.csl.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = large
year <- 1; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[3,3]
daih.csl.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = large
year <- 2; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[3,3]
daih.csl.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = large
year <- 3; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[3,3]
daih.csl.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = large
year <- 4; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[3,3]
daih.csl.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = large
year <- 5; dai <- koku.gra.tab[3,4]; cs <- koku.gra.tab[3,3]
daih.csl.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率中 クラスサイズ小
## t0, cs = small
year <- 0; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[1,3]
daim.css.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = small
year <- 1; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[1,3]
daim.css.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = small
year <- 2; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[1,3]
daim.css.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = small
year <- 3; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[1,3]
daim.css.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = small
year <- 4; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[1,3]
daim.css.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = small
year <- 5; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[1,3]
daim.css.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率中 クラスサイズ中
## t0, cs = medium
year <- 0; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[2,3]
daim.csm.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = medium
year <- 1; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[2,3]
daim.csm.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = medium
year <- 2; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[2,3]
daim.csm.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = medium
year <- 3; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[2,3]
daim.csm.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = medium
year <- 4; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[2,3]
daim.csm.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = medium
year <- 5; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[2,3]
daim.csm.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率中 クラスサイズ大
## t0, cs = large
year <- 0; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[3,3]
daim.csl.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = large
year <- 1; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[3,3]
daim.csl.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = large
year <- 2; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[3,3]
daim.csl.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = large
year <- 3; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[3,3]
daim.csl.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = large
year <- 4; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[3,3]
daim.csl.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = large
year <- 5; dai <- koku.gra.tab[2,4]; cs <- koku.gra.tab[3,3]
daim.csl.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率低 クラスサイズ小
## t0, cs = small
year <- 0; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[1,3]
dail.css.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = small
year <- 1; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[1,3]
dail.css.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = small
year <- 2; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[1,3]
dail.css.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = small
year <- 3; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[1,3]
dail.css.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = small
year <- 4; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[1,3]
dail.css.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = small
year <- 5; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[1,3]
dail.css.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率低 クラスサイズ中
## t0, cs = medium
year <- 0; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[2,3]
dail.csm.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = medium
year <- 1; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[2,3]
dail.csm.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = medium
year <- 2; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[2,3]
dail.csm.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = medium
year <- 3; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[2,3]
dail.csm.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = medium
year <- 4; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[2,3]
dail.csm.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = medium
year <- 5; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[2,3]
dail.csm.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
# 大卒率低 クラスサイズ大
## t0, cs = large
year <- 0; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[3,3]
dail.csl.t0 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t1, cs = large
year <- 1; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[3,3]
dail.csl.t1 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t2, cs = large
year <- 2; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[3,3]
dail.csl.t2 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t3, cs = large
year <- 3; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[3,3]
dail.csl.t3 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t4, cs = large
year <- 4; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[3,3]
dail.csl.t4 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## t5, cs = large
year <- 5; dai <- koku.gra.tab[1,4]; cs <- koku.gra.tab[3,3]
dail.csl.t5 <- koku.keisu[,1] +
koku.keisu[,2] * year +
koku.keisu[,3] * dai +
koku.keisu[,4] * cs +
koku.keisu[,5] * (dai * cs) +
koku.keisu[,6] * (year * dai) +
koku.keisu[,7] * (year * cs) +
koku.keisu[,8] * (year * dai * cs)
## plotdata.koku
plotdata.koku <- matrix(
c(dail.css.t0, dail.css.t1, dail.css.t2, dail.css.t3, dail.css.t4, dail.css.t5,
dail.csm.t0, dail.csm.t1, dail.csm.t2, dail.csm.t3, dail.csm.t4, dail.csm.t5,
dail.csl.t0, dail.csl.t1, dail.csl.t2, dail.csl.t3, dail.csl.t4, dail.csl.t5,
daim.css.t0, daim.css.t1, daim.css.t2, daim.css.t3, daim.css.t4, daim.css.t5,
daim.csm.t0, daim.csm.t1, daim.csm.t2, daim.csm.t3, daim.csm.t4, daim.csm.t5,
daim.csl.t0, daim.csl.t1, daim.csl.t2, daim.csl.t3, daim.csl.t4, daim.csl.t5,
daih.css.t0, daih.css.t1, daih.css.t2, daih.css.t3, daih.css.t4, daih.css.t5,
daih.csm.t0, daih.csm.t1, daih.csm.t2, daih.csm.t3, daih.csm.t4, daih.csm.t5,
daih.csl.t0, daih.csl.t1, daih.csl.t2, daih.csl.t3, daih.csl.t4, daih.csl.t5),
nrow = 6, ncol =9)
rownames(plotdata.koku) <- c("Grade.1", "Grade 2", "Grade 3", "Grade 4", "Grade 5", "Grade 6")
colnames(plotdata.koku) <- c("Grad.Lo.CS.S", "Grad.Lo.CS.M", "Grad.Lo.CS.L",
"Grad.Md.CS.S", "Grad.Md.CS.M", "Grad.Md.CS.L",
"Grad.Hi.CS.S", "Grad.Hi.CS.M", "Grad.Hi.CS.L")社会のデータ
## 社会はモデル7（初期値と推移に大卒率が影響）
## shak.res7 <- lmer(shak ~ year +
# dai.c +
# cs.c +
# year:dai.c +
# year:cs.c +
# (1|id) + (1|sid),
# 係数
shak.keisu <- data.frame(
matrix(c(shak.res7@pp$delb[1],
shak.res7@pp$delb[2],
shak.res7@pp$delb[3],
shak.res7@pp$delb[4],
shak.res7@pp$delb[5],
shak.res7@pp$delb[6]),
nrow = 1, ncol = 6))
colnames(shak.keisu) <- c("Intercept", "year",
"dai.c", "cs.c",
"year:dai.c", "year:cs.c")
# 大卒率高 クラスサイズ小
## t0, cs = small
year <- 0; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[1,3]
daih.css.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = small
year <- 1; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[1,3]
daih.css.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = small
year <- 2; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[1,3]
daih.css.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = small
year <- 3; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[1,3]
daih.css.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率高 クラスサイズ中
## t0, cs = medium
year <- 0; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[2,3]
daih.csm.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = medium
year <- 1; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[2,3]
daih.csm.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = medium
year <- 2; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[2,3]
daih.csm.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = medium
year <- 3; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[2,3]
daih.csm.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率高 クラスサイズ大
## t0, cs = large
year <- 0; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[3,3]
daih.csl.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = large
year <- 1; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[3,3]
daih.csl.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = large
year <- 2; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[3,3]
daih.csl.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = large
year <- 3; dai <- shak.gra.tab[3,4]; cs <- shak.gra.tab[3,3]
daih.csl.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率中 クラスサイズ小
## t0, cs = small
year <- 0; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[1,3]
daim.css.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = small
year <- 1; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[1,3]
daim.css.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = small
year <- 2; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[1,3]
daim.css.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = small
year <- 3; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[1,3]
daim.css.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率中 クラスサイズ中
## t0, cs = medium
year <- 0; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[2,3]
daim.csm.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = medium
year <- 1; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[2,3]
daim.csm.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = medium
year <- 2; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[2,3]
daim.csm.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = medium
year <- 3; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[2,3]
daim.csm.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率中 クラスサイズ大
## t0, cs = large
year <- 0; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[3,3]
daim.csl.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = large
year <- 1; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[3,3]
daim.csl.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = large
year <- 2; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[3,3]
daim.csl.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = large
year <- 3; dai <- shak.gra.tab[2,4]; cs <- shak.gra.tab[3,3]
daim.csl.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率低 クラスサイズ小
## t0, cs = small
year <- 0; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[1,3]
dail.css.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = small
year <- 1; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[1,3]
dail.css.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = small
year <- 2; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[1,3]
dail.css.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = small
year <- 3; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[1,3]
dail.css.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t4, cs = small
year <- 4; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[1,3]
dail.css.t4 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t5, cs = small
year <- 5; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[1,3]
dail.css.t5 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率低 クラスサイズ中
## t0, cs = medium
year <- 0; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[2,3]
dail.csm.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = medium
year <- 1; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[2,3]
dail.csm.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = medium
year <- 2; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[2,3]
dail.csm.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = medium
year <- 3; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[2,3]
dail.csm.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
# 大卒率低 クラスサイズ大
## t0, cs = large
year <- 0; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[3,3]
dail.csl.t0 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t1, cs = large
year <- 1; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[3,3]
dail.csl.t1 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t2, cs = large
year <- 2; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[3,3]
dail.csl.t2 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## t3, cs = large
year <- 3; dai <- shak.gra.tab[1,4]; cs <- shak.gra.tab[3,3]
dail.csl.t3 <- shak.keisu[,1] +
shak.keisu[,2] * year +
shak.keisu[,3] * dai +
shak.keisu[,4] * cs +
shak.keisu[,5] * (year * dai) +
shak.keisu[,6] * (year * cs)
## plotdata.shak
plotdata.shak <- matrix(
c(dail.css.t0, dail.css.t1, dail.css.t2, dail.css.t3,
dail.csm.t0, dail.csm.t1, dail.csm.t2, dail.csm.t3,
dail.csl.t0, dail.csl.t1, dail.csl.t2, dail.csl.t3,
daim.css.t0, daim.css.t1, daim.css.t2, daim.css.t3,
daim.csm.t0, daim.csm.t1, daim.csm.t2, daim.csm.t3,
daim.csl.t0, daim.csl.t1, daim.csl.t2, daim.csl.t3,
daih.css.t0, daih.css.t1, daih.css.t2, daih.css.t3,
daih.csm.t0, daih.csm.t1, daih.csm.t2, daih.csm.t3,
daih.csl.t0, daih.csl.t1, daih.csl.t2, daih.csl.t3),
nrow = 4, ncol =9)
rownames(plotdata.shak) <- c("Grade 3", "Grade 4", "Grade 5", "Grade 6")
colnames(plotdata.shak) <- c("Grad.Lo.CS.S", "Grad.Lo.CS.M", "Grad.Lo.CS.L",
"Grad.Md.CS.S", "Grad.Md.CS.M", "Grad.Md.CS.L",
"Grad.Hi.CS.S", "Grad.Hi.CS.M", "Grad.Hi.CS.L")グラフ
col <- c("1年", "2年", "3年", "4年", "5年", "6年")
#大卒率 低
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.koku[,1:3], type="b", xlim=c(1, 6), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（国語）",
main="大学・大学院卒業者数の割合が平均(0.08)より1SD(0.03)低い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4, 5, 6), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.92) - 1SD(6.77)", "平均学級規模(22,92)", "平均学級規模(22.92) + 1SD(6.77)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 中
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.koku[,4:6], type="b", xlim=c(1, 6), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（国語）",
main="大学・大学院卒業者数の割合が平均(0.08)の学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4, 5, 6), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.92) - 1SD(6.77)", "平均学級規模(22,92)", "平均学級規模(22.92) + 1SD(6.77)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 高
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.koku[,7:9], type="b", xlim=c(1, 6), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（国語）",
main="大学・大学院卒業者数の割合が平均(0.08)より1SD(0.03)高い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4, 5, 6), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.92) - 1SD(6.77)", "平均学級規模(22,92)", "平均学級規模(22.92) + 1SD(6.77)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
社会
col <- c("3年", "4年", "5年", "6年")
#大卒率 低
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.shak[,1:3], type="b", xlim=c(1, 4), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（社会）",
main="大学・大学院卒業者数の割合が平均(0.07)より1SD(0.03)低い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (1.5, 51.5, #凡例左上の位置を座標で指定
c("平均学級規模(22.06) - 1SD(5.98)", "平均学級規模(22.06)", "平均学級規模(22.06) + 1SD(5.98)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 中
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.shak[,4:6], type="b", xlim=c(1, 4), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（社会）",
main="大学・大学院卒業者数の割合が平均(0.07)の学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (1.5, 51.5, #凡例左上の位置を座標で指定
c("平均学級規模(22.06) - 1SD(5.98)", "平均学級規模(22.06)", "平均学級規模(22.06) + 1SD(5.98)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 高
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.shak[,7:9], type="b", xlim=c(1, 4), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（社会）",
main="大学・大学院卒業者数の割合が平均(0.07)より1SD(0.03)高い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (1.5, 51.5, #凡例左上の位置を座標で指定
c("平均学級規模(22.06) - 1SD(5.98)", "平均学級規模(22.06)", "平均学級規模(22.06) + 1SD(5.98)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
グラフを3つ並べてみる
国語
col <- c("1年", "2年", "3年", "4年", "5年", "6年")
par(oma = c(0, 0, 4, 0))
par(mfrow=c(1,3))
#大卒率 低
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.koku[,1:3], type="b", xlim=c(1, 6), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（国語）",
main="大学・大学院卒業者数の割合が平均(0.08)より1SD(0.03)低い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4, 5, 6), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.92) - 1SD(6.77)", "平均学級規模(22,92)", "平均学級規模(22.92) + 1SD(6.77)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 中
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.koku[,4:6], type="b", xlim=c(1, 6), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（国語）",
main="大学・大学院卒業者数の割合が平均(0.08)の学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4, 5, 6), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.92) - 1SD(6.77)", "平均学級規模(22,92)", "平均学級規模(22.92) + 1SD(6.77)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 高
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.koku[,7:9], type="b", xlim=c(1, 6), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（国語）",
main="大学・大学院卒業者数の割合が平均(0.08)より1SD(0.03)高い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4, 5, 6), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.92) - 1SD(6.77)", "平均学級規模(22,92)", "平均学級規模(22.92) + 1SD(6.77)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
社会
col <- c("3年", "4年", "5年", "6年")
par(oma = c(0, 0, 4, 0))
par(mfrow=c(1,3))
#大卒率 低
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.shak[,1:3], type="b", xlim=c(1, 4), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（社会）",
main="大学・大学院卒業者数の割合が平均(0.07)より1SD(0.03)低い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.06) - 1SD(5.98)", "平均学級規模(22.06)", "平均学級規模(22.06) + 1SD(5.98)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 中
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.shak[,4:6], type="b", xlim=c(1, 4), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（社会）",
main="大学・大学院卒業者数の割合が平均(0.07)の学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.06) - 1SD(5.98)", "平均学級規模(22.06)", "平均学級規模(22.06) + 1SD(5.98)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)
#大卒率 高
mar = c(4,4,4,4) # 余白を小さく
# oma = c(0,0,0,0) # デバイス領域（インチで幅と高さ）
par(family = "HiraKakuProN-W3") #Macintoshの場合
matplot(plotdata.shak[,7:9], type="b", xlim=c(1, 4), ylim = c(50, 55), axes=F,
xlab="学年", ylab="学力偏差値（社会）",
main="大学・大学院卒業者数の割合が平均(0.07)より1SD(0.03)高い学区の学校の場合",
mgp = c(2, 0.7, 0),
lwd=1, #やや太く
lty = c(2, 1, 4), #線種は以下の通り
## lty="solid" or 1→実線
## lty="dashed" or 2→ダッシュ
## lty="dotted" or 3→ドット
## lty="dotdash" or 4→ドットとダッシュ
## lty="longdash" or 5→長いダッシュ
## lty="twodash" or 6→二つのダッシュ
col = c(1, 1, 1), #色は以下の通り
## 順に黒，赤，緑，青，水色，紫，黄，灰
pch = c(15, 16, 17),
cex = 1, # 記号の大きさ（標準は１）
cex.lab = 1.0, # 軸の説明の字の大きさ
cex.axis = 1.0, # 軸の数字等（ラベル）の大きさ
cex.main = 0.8 # メインタイトルの字の大きさ
)
axis(side=1, at=c(1, 2, 3, 4), labels = col)
axis(side=2, at=c(50, 51, 52, 53, 54, 55))
## 凡例をつける
legend (2.5, 51, #凡例左上の位置を座標で指定
c("平均学級規模(22.06) - 1SD(5.98)", "平均学級規模(22.06)", "平均学級規模(22.06) + 1SD(5.98)"),
lwd = 1, #線を太めに
lty = c(2, 1, 4), #線種
col = c(1, 1, 1), #線の
pch = c(15, 16, 17),
bty = "n", #枠なし
bg = "n", #背景色なし
cex = 1.0, #文字の大きさを基準の__%
)