library(tidyverse)
library(magrittr)
library(gnm)
library(broom)5 第5章
パッケージの呼び出し．
モデル適合度を表示するための関数を準備する．すべてgnmによって推定を行う．
# Specify the constrasts
# Here, we use sum to zero contrast.
options(width=90, contrasts = c(factor="contr.sum", ordered="contr.treatment"))
# Define a function which calculation BIC based on L2 statistics
model.summary <- function(obj, Model = NULL){
if (sum(class(obj) == "gnm") != 1)
stop("estimate with gnm")
aic <- obj$deviance - obj$df * 2 # AIC(L2)
bic <- obj$deviance - obj$df * log(sum(obj$y)) #BIC(L2)
delta <- 100 * sum(abs(obj$y - obj$fitted.values)) / (2 * sum(obj$y))
p <- pchisq(obj$deviance, obj$df, lower.tail = FALSE) #p<-ifelse(p<0.001,"<0.001",p)
result <- matrix(0, 1, 7)
if (is.null(Model)){
Model <- deparse(substitute(obj))
}
result <- tibble(
"Model Description" = Model,
"df" = obj$df,
"L2" = obj$deviance,
#"AIC(L2)" = aic,
"BIC" = bic,
"Delta" = delta,
"p" = p
)
return(result)
}5.1 表5.1
# Table 5.1
Freq <- c(118, 28, 32, 6, 7,
218, 28, 97, 12, 14,
11, 2, 4, 1, 1,
104, 22, 61, 8, 5,
117, 24, 70, 9, 7,
42, 6, 20, 2, 0,
48, 16, 104, 14, 9,
128, 52, 81, 14, 12)
worries <- gl(8,5)
situations <- gl(5,1,8*5)
tab5.1 <- tibble(Freq, worries, situations)
# Model 1 - Independence
m1 <- tab5.1 |>
gnm(Freq ~ worries + situations,
family = poisson,
trace = F,
tolerance = 1e-12,
data = _)
model.summary(m1)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m1 28 121. -84.3 9.84 1.31e-13# Model 2 - RC(1)
m2 <- tab5.1 |>
gnm(Freq ~ worries + situations + Mult(1, worries, situations),
family = poisson,
trace = F,
tolerance = 1e-12,
data = _)Initialising
Running start-up iterations..
Running main iterations...................................................................
.........
Donemodel.summary(m2)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m2 18 29.2 -103. 4.01 0.0461# Model 3 - RC(1) with equality constraints on
# MIL=ECO=MTO, ENR=SAB=OTH and ASAF=IFAA
worries.a <- worries
levels(worries.a) <- factor(c(1, 2, 2, 3, 3, 2, 4, 3))
worries.a [1] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 4 4 4 4 4 3 3 3 3 3
Levels: 1 2 3 4situations.a <- situations
levels(situations.a) <- factor(c(1, 2, 3, 3, 4))
situations.a [1] 1 2 3 3 4 1 2 3 3 4 1 2 3 3 4 1 2 3 3 4 1 2 3 3 4 1 2 3 3 4 1 2 3 3 4 1 2 3 3 4
Levels: 1 2 3 4m3.un <- tab5.1 |>
gnm(Freq ~ worries + situations +
Mult(1, worries.a, situations.a),
family = poisson,
trace = F,
tolerance = 1e-12,
data = _)Initialising
Running start-up iterations..
Running main iterations..........................................................
Donemodel.summary(m3.un)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m3.un 23 29.6 -139. 4.08 0.161mu <- getContrasts(m3.un,
pickCoef(m3.un, "[.]worries.a"),
ref = c(1, 3, 3, 1) / 8, scaleRef = c(1, 3, 3, 1) / 8,
scaleWeights = c(1, 3, 3, 1))
nu <- getContrasts(m3.un,
pickCoef(m3.un, "[.]situations.a"),
ref = c(1, 1, 2, 1) / 5, scaleRef = c(1, 1, 2, 1) / 5,
scaleWeights = c(1, 1, 2, 1))
con <- c(mu$qvframe[, 1][c(1, 4)], nu$qvframe[, 1][c(1, 4)])
m3 <- gnm(Freq ~ worries + situations + Mult(1, worries.a, situations.a),
family = poisson,
constrain = c(14, 17, 18, 21), constrainTo = con,
trace = F,
tolerance = 1e-12)Initialising
Running start-up iterations..
Running main iterations....................................................
Donesummary(m3);mu;nu;model.summary(m3.un)
Call:
gnm(formula = Freq ~ worries + situations + Mult(1, worries.a,
situations.a), constrain = c(14, 17, 18, 21), constrainTo = con,
family = poisson, tolerance = 1e-12, trace = F)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.55631 -0.35180 0.05564 0.41188 2.91020
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.85834 0.05067 56.406 < 2e-16 ***
worries1 0.10499 0.16439 0.639 0.52304
worries2 0.88097 0.08378 10.515 < 2e-16 ***
worries3 -2.08538 0.21086 -9.890 < 2e-16 ***
worries4 0.34128 0.07460 4.575 4.77e-06 ***
worries5 0.46792 0.07155 6.540 6.16e-11 ***
worries6 -0.78133 0.12531 -6.235 4.51e-10 ***
worries7 0.36910 0.28578 1.292 0.19651
situations1 1.43713 0.08740 16.443 < 2e-16 ***
situations2 -0.02940 0.07766 -0.379 0.70501
situations3 0.88374 0.07261 12.172 < 2e-16 ***
situations4 -1.07721 0.11438 -9.418 < 2e-16 ***
Mult(., worries.a, situations.a). 1.35718 0.50966 2.663 0.00775 **
Mult(1, ., situations.a).worries.a1 -0.42724 NA NA NA
Mult(1, ., situations.a).worries.a2 -0.17319 0.11548 -1.500 0.13367
Mult(1, ., situations.a).worries.a3 0.03189 0.09790 0.326 0.74459
Mult(1, ., situations.a).worries.a4 0.85113 NA NA NA
Mult(1, worries.a, .).situations.a1 -0.69874 NA NA NA
Mult(1, worries.a, .).situations.a2 -0.29537 0.22737 -1.299 0.19392
Mult(1, worries.a, .).situations.a3 0.45726 0.41619 1.099 0.27191
Mult(1, worries.a, .).situations.a4 0.07959 NA NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Std. Error is NA where coefficient has been constrained or is unidentified
Residual deviance: 29.612 on 23 degrees of freedom
AIC: 249.9
Number of iterations: 52 Estimate Std. Error
Mult(1, ., situations.a).worries.a1 -0.4272363 0.09380998
Mult(1, ., situations.a).worries.a2 -0.1731936 0.04675330
Mult(1, ., situations.a).worries.a3 0.0318943 0.04574940
Mult(1, ., situations.a).worries.a4 0.8511342 0.03998795 Estimate Std. Error
Mult(1, worries.a, .).situations.a1 -0.69874471 0.08798182
Mult(1, worries.a, .).situations.a2 -0.29537072 0.13782241
Mult(1, worries.a, .).situations.a3 0.45726207 0.06815138
Mult(1, worries.a, .).situations.a4 0.07959129 0.22251866# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m3.un 23 29.6 -139. 4.08 0.161# Model 4 - RC(1) with equality constraints on
# MIL=ECO=MTO=ENR=SAB=OTH and ASAF=IFAA
worries.b <- worries
levels(worries.b) <- factor(c(1, 2, 2, 2, 2, 2, 3, 2))
worries.b [1] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 2 2 2 2 2
Levels: 1 2 3m4.un <- tab5.1 |>
gnm(Freq ~ worries + situations + Mult(1, worries.b, situations.a),
family = poisson,
trace = F,
tolerance = 1e-12,
data = _)Initialising
Running start-up iterations..
Running main iterations..........................
Donemodel.summary(m4.un)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m4.un 24 34.5 -142. 4.80 0.0760# Model 5
m5.un <- tab5.1 |>
gnm(Freq ~ worries + situations +
Mult(1, worries, situations, inst = 1) +
Mult(1, worries, situations, inst = 2),
family = poisson,
trace = F, tolerance = 1e-12,
data = _)Initialising
Running start-up iterations..
Running main iterations...................................................................
Donemodel.summary(m5.un)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m5.un 10 6.81 -66.7 0.872 0.743# Model 6 - RC(2) with equality constraints on
# ECO=MTO=MIL=ENR=SAB and ASAF=IFAA in both dimensions
worries.c <- worries
levels(worries.c) <- factor(c(1,2,2,2,2,2,3,4))
worries.c [1] 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4
Levels: 1 2 3 4set.seed(12345)
m6.un <- tab5.1 |>
gnm(Freq ~ worries + situations +
Mult(1, worries.c, situations.a, inst = 1) +
Mult(1, worries.c, situations.a, inst = 2),
family = poisson,
trace = F,
tolerance = 1e-12,
data = _)Initialising
Running start-up iterations..
Running main iterations............................................
Donemodel.summary(m6.un)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m6.un 20 13.7 -133. 2.82 0.8475.2 表5.4
最適尺度法として連関モデルを利用する．
# Table 5.4
Freq<-c(1096, 1847, 1255, 925, 3321, 6123, 6830, 5524,
1541, 3134, 3145, 3300, 1915, 4429, 7035, 9421,
4183, 5139, 1857, 1272, 8080, 8586, 4788, 4294,
6033, 9211, 5046, 1058, 28130, 44589, 20074, 3408,
4354, 13430, 18670, 9821, 2250, 9075, 18286, 14358,
14587, 31470, 16390, 3751, 8242, 17532, 12825, 3956,
1517, 5820, 6197, 2372, 721, 2909, 4141, 2070,
3581, 9268, 5463, 1007, 1341, 3808, 3163, 815,
1454, 3109, 1055, 888, 563, 1909, 1018, 1051,
3237, 3851, 377, 102, 731, 858, 247, 84,
14882, 22182, 5363, 1136, 11650, 15818, 5524, 2122,
6033, 3475, 63, 18, 1603, 1005, 30, 16,
5968, 8783, 7701, 6483, 8733, 14329, 19386, 28143,
1011, 2162, 3536, 6649, 697, 1299, 2362, 10796,
3214, 3621, 2485, 3177, 793, 1134, 1292, 3597,
11532, 16837, 6975, 1839, 2563, 2995, 2060, 1600,
1009, 2719, 3521, 3409, 296, 503, 626, 1273,
1586, 3025, 1726, 668, 245, 415, 238, 218,
387, 941, 564, 316, 86, 138, 79, 48,
994, 1988, 542, 145, 158, 259, 101, 56,
171, 409, 223, 245, 65, 172, 99, 174,
293, 290, 67, 31, 32, 62, 18, 30,
4288, 4916, 1452, 766, 616, 794, 347, 300,
370, 186, 3, 4, 67, 37, 5, 2)
income <- gl(4, 1, 192) # 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 ...
occ <- gl(12, 8, 192) # 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 ...
edu1 <- rep(c(1,2), each = 4, length.out = 96)
edu2 <- rep(c(3,4), each = 4, length.out = 96)
educ <- c(edu1, edu2) |> factor()
# 確認
matrix(income, nrow = 24, ncol = 8, byrow = TRUE) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] "1" "2" "3" "4" "1" "2" "3" "4"
[2,] "1" "2" "3" "4" "1" "2" "3" "4"
[3,] "1" "2" "3" "4" "1" "2" "3" "4"
[4,] "1" "2" "3" "4" "1" "2" "3" "4"
[5,] "1" "2" "3" "4" "1" "2" "3" "4"
[6,] "1" "2" "3" "4" "1" "2" "3" "4"
[7,] "1" "2" "3" "4" "1" "2" "3" "4"
[8,] "1" "2" "3" "4" "1" "2" "3" "4"
[9,] "1" "2" "3" "4" "1" "2" "3" "4"
[10,] "1" "2" "3" "4" "1" "2" "3" "4"
[11,] "1" "2" "3" "4" "1" "2" "3" "4"
[12,] "1" "2" "3" "4" "1" "2" "3" "4"
[13,] "1" "2" "3" "4" "1" "2" "3" "4"
[14,] "1" "2" "3" "4" "1" "2" "3" "4"
[15,] "1" "2" "3" "4" "1" "2" "3" "4"
[16,] "1" "2" "3" "4" "1" "2" "3" "4"
[17,] "1" "2" "3" "4" "1" "2" "3" "4"
[18,] "1" "2" "3" "4" "1" "2" "3" "4"
[19,] "1" "2" "3" "4" "1" "2" "3" "4"
[20,] "1" "2" "3" "4" "1" "2" "3" "4"
[21,] "1" "2" "3" "4" "1" "2" "3" "4"
[22,] "1" "2" "3" "4" "1" "2" "3" "4"
[23,] "1" "2" "3" "4" "1" "2" "3" "4"
[24,] "1" "2" "3" "4" "1" "2" "3" "4" matrix(occ, nrow = 24, ncol = 8, byrow = TRUE) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] "1" "1" "1" "1" "1" "1" "1" "1"
[2,] "2" "2" "2" "2" "2" "2" "2" "2"
[3,] "3" "3" "3" "3" "3" "3" "3" "3"
[4,] "4" "4" "4" "4" "4" "4" "4" "4"
[5,] "5" "5" "5" "5" "5" "5" "5" "5"
[6,] "6" "6" "6" "6" "6" "6" "6" "6"
[7,] "7" "7" "7" "7" "7" "7" "7" "7"
[8,] "8" "8" "8" "8" "8" "8" "8" "8"
[9,] "9" "9" "9" "9" "9" "9" "9" "9"
[10,] "10" "10" "10" "10" "10" "10" "10" "10"
[11,] "11" "11" "11" "11" "11" "11" "11" "11"
[12,] "12" "12" "12" "12" "12" "12" "12" "12"
[13,] "1" "1" "1" "1" "1" "1" "1" "1"
[14,] "2" "2" "2" "2" "2" "2" "2" "2"
[15,] "3" "3" "3" "3" "3" "3" "3" "3"
[16,] "4" "4" "4" "4" "4" "4" "4" "4"
[17,] "5" "5" "5" "5" "5" "5" "5" "5"
[18,] "6" "6" "6" "6" "6" "6" "6" "6"
[19,] "7" "7" "7" "7" "7" "7" "7" "7"
[20,] "8" "8" "8" "8" "8" "8" "8" "8"
[21,] "9" "9" "9" "9" "9" "9" "9" "9"
[22,] "10" "10" "10" "10" "10" "10" "10" "10"
[23,] "11" "11" "11" "11" "11" "11" "11" "11"
[24,] "12" "12" "12" "12" "12" "12" "12" "12"matrix(educ, nrow = 24, ncol = 8, byrow = TRUE) [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,] "1" "1" "1" "1" "2" "2" "2" "2"
[2,] "1" "1" "1" "1" "2" "2" "2" "2"
[3,] "1" "1" "1" "1" "2" "2" "2" "2"
[4,] "1" "1" "1" "1" "2" "2" "2" "2"
[5,] "1" "1" "1" "1" "2" "2" "2" "2"
[6,] "1" "1" "1" "1" "2" "2" "2" "2"
[7,] "1" "1" "1" "1" "2" "2" "2" "2"
[8,] "1" "1" "1" "1" "2" "2" "2" "2"
[9,] "1" "1" "1" "1" "2" "2" "2" "2"
[10,] "1" "1" "1" "1" "2" "2" "2" "2"
[11,] "1" "1" "1" "1" "2" "2" "2" "2"
[12,] "1" "1" "1" "1" "2" "2" "2" "2"
[13,] "3" "3" "3" "3" "4" "4" "4" "4"
[14,] "3" "3" "3" "3" "4" "4" "4" "4"
[15,] "3" "3" "3" "3" "4" "4" "4" "4"
[16,] "3" "3" "3" "3" "4" "4" "4" "4"
[17,] "3" "3" "3" "3" "4" "4" "4" "4"
[18,] "3" "3" "3" "3" "4" "4" "4" "4"
[19,] "3" "3" "3" "3" "4" "4" "4" "4"
[20,] "3" "3" "3" "3" "4" "4" "4" "4"
[21,] "3" "3" "3" "3" "4" "4" "4" "4"
[22,] "3" "3" "3" "3" "4" "4" "4" "4"
[23,] "3" "3" "3" "3" "4" "4" "4" "4"
[24,] "3" "3" "3" "3" "4" "4" "4" "4" # 分析用データ
tab5.4 <- data.frame(Freq, income, occ, educ)
########################################################################################
# Table 5.5
# Model 1: Complete Independence
m1 <- tab5.4 |>
gnm(Freq ~ educ + occ + income,
data = _,
family = poisson,
tolerance = 1e-12)
model.summary(m1) # A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m1 174 586906. 584537. 33.8 0# Model 2: Conditional Independence
m2 <- tab5.4 |>
gnm(Freq ~ educ * occ + income * occ,
data = _,
family = poisson,
tolerance = 1e-12)
model.summary(m2)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m2 108 27957. 26487. 6.00 0# Model 3: All two-way interaction
m3 <- tab5.4 |>
gnm(Freq ~ educ * occ + income * occ + educ * income,
data = _,
family = poisson, tolerance = 1e-12)
model.summary(m3)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m3 99 6540. 5192. 2.64 0# Model 4: RC(1)+RL(1) partial association
m4 <- tab5.4 |>
gnm(Freq ~ educ + occ + income + Mult(1, occ, educ) + Mult(1, occ, income),
data = _,
family = poisson,
tolerance = 1e-12)Initialising
Running start-up iterations..
Running main iterations...................................................................
.....
Donemodel.summary(m4)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m4 148 70861. 68846. 11.2 0# Model 5: Model 4 with consistent row (occupation) scores
m5 <- tab5.4 |>
gnm(Freq ~ educ + occ + income + Mult(1, occ, educ + income),
data = _,
family = poisson,
tolerance = 1e-12)Initialising
Running start-up iterations..
Running main iterations...................................................................
..........................................................................................
......................................
Donemodel.summary(m5)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m5 158 185515. 183364. 18.3 0mu <- getContrasts(model = m5,
set = pickCoef(m5, "[.]occ")[1:12],
ref = "mean",
scaleRef = "mean",
scaleWeights = "unit")
mu Estimate Std. Error
Mult(1, ., educ + income).occ1 -0.54805987 0.001793134
Mult(1, ., educ + income).occ2 -0.38746682 0.001271727
Mult(1, ., educ + income).occ3 -0.21717857 0.001291161
Mult(1, ., educ + income).occ4 -0.14398575 0.001002368
Mult(1, ., educ + income).occ5 -0.08607017 0.001273939
Mult(1, ., educ + income).occ6 0.15776750 0.001820890
Mult(1, ., educ + income).occ7 0.05029005 0.002500252
Mult(1, ., educ + income).occ8 0.14092026 0.002598170
Mult(1, ., educ + income).occ9 0.02379483 0.003466324
Mult(1, ., educ + income).occ10 0.39930723 0.004697125
Mult(1, ., educ + income).occ11 0.10479535 0.001779509
Mult(1, ., educ + income).occ12 0.50588597 0.004258340# Model 6: RC(1)+RL(1)+CL(1) partial association
m6 <- tab5.4 |>
gnm(Freq ~ educ + occ + income +
Mult(1, occ, educ) +
Mult(1, occ, income) +
Mult(1, educ, income),
data = _,
family = poisson,
tolerance = 1e-12)Initialising
Running start-up iterations..
Running main iterations...................................................................
..
Donemodel.summary(m6)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m6 143 42101. 40154. 8.27 0# Model 7: Model 6 with consistent row (occupation) scores
m7 <- tab5.4 |>
gnm(Freq ~ educ + occ + income +
Mult(1, occ, educ + income) +
Mult(1, educ, income),
data = _,
family = poisson,
tolerance = 1e-12)Initialising
Running start-up iterations..
Running main iterations...................................................................
..........................................................................................
..........................................................................................
........................
Donemodel.summary(m7)# A tibble: 1 × 6
`Model Description` df L2 BIC Delta p
<chr> <int> <dbl> <dbl> <dbl> <dbl>
1 m7 153 174073. 171990. 17.8 0mu <- getContrasts(model = m7,
set = pickCoef(m7, "[.]occ"),
ref = "mean",
scaleRef = "mean",
scaleWeights = "unit")
mu Estimate Std. Error
Mult(1, ., educ + income).occ1 0.56931429 0.001883457
Mult(1, ., educ + income).occ2 0.37928161 0.001275097
Mult(1, ., educ + income).occ3 0.22247516 0.001381547
Mult(1, ., educ + income).occ4 0.17045561 0.001046926
Mult(1, ., educ + income).occ5 0.05464251 0.001423421
Mult(1, ., educ + income).occ6 -0.16380722 0.001871288
Mult(1, ., educ + income).occ7 -0.07890623 0.002667870
Mult(1, ., educ + income).occ8 -0.14504188 0.002680434
Mult(1, ., educ + income).occ9 -0.04282708 0.003676179
Mult(1, ., educ + income).occ10 -0.39013284 0.004810546
Mult(1, ., educ + income).occ11 -0.09178943 0.001794307
Mult(1, ., educ + income).occ12 -0.48366450 0.004397647# Model 8: model 6 with consistent row, column and layer scores
# currently, this might not be fitted with gnm!- 表5.5のモデル1については原著および訳書ともに\(\Delta\)の値が間違っているので注意すること（印刷後に修正漏れに気づきました）．実際は33.85である．
# 2つの引数についてのリストを作成
## 第1引数はモデル
models <- list(m1, m2, m3, m4, m5, m6, m7)
## 第2引数はモデルの内容
model_desctiption <-
list("完全独立",
"条件付き独立",
"すべての2元交互作用",
"RC(1)+RL(1) 部分連関",
"一貫した行(職業)スコアのあるRC(1)+RL(1) 部分連関",
"RC(1)+RL(1)+CL(1) 部分連関",
"一貫した行(職業)スコアのあるRC(1)+RL(1)+CL(1)部分連関")
# 結果をまとめる
map2_dfr(models, model_desctiption, model.summary, .id = "Model") |>
knitr::kable()| Model | Model Description | df | L2 | BIC | Delta | p |
|---|---|---|---|---|---|---|
| 1 | 完全独立 | 174 | 586906.225 | 584536.899 | 33.848968 | 0 |
| 2 | 条件付き独立 | 108 | 27957.399 | 26486.783 | 6.003195 | 0 |
| 3 | すべての2元交互作用 | 99 | 6540.396 | 5192.331 | 2.635334 | 0 |
| 4 | RC(1)+RL(1) 部分連関 | 148 | 70860.986 | 68845.698 | 11.169390 | 0 |
| 5 | 一貫した行(職業)スコアのあるRC(1)+RL(1) 部分連関 | 158 | 185515.252 | 183363.795 | 18.266858 | 0 |
| 6 | RC(1)+RL(1)+CL(1) 部分連関 | 143 | 42101.443 | 40154.238 | 8.274821 | 0 |
| 7 | 一貫した行(職業)スコアのあるRC(1)+RL(1)+CL(1)部分連関 | 153 | 174073.129 | 171989.756 | 17.803352 | 0 |