Multilevel models Probl
m0 <- lmer(probl~1+(1|v2.idmen/id), data=alldat)
print(icc(m0, by_group = TRUE))
## # ICC by Group
##
## Group | ICC
## -------------------
## id:v2.idmen | 0.041
## v2.idmen | 0.277
m1 <- lmer(probl~relevel(cluster, ref="compagnonnage")+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(probl~relevel(cluster, ref="compagnonnage")+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(probl~relevel(cluster,ref="compagnonnage")+relevel(cluster,ref="compagnonnage")*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(probl~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+enf+educ+activ+(1|v2.idmen/id), data=alldat)
fm1 <- plm(probl~relevel(cluster, ref="compagnonnage")+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(probl~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+educ+enf+activ, index="id", model="within", data=alldat)
htmlreg(list("Multilevel 0"=m0, "Multilevel 1"=m1, "Multilevel 1b"=m1b,"Multilevel 1c"=m1c, "Multilevel 2"=m2, "Fixed-Effect 1"=fm1, "Fixed-Effect 2"=fm2))
Statistical models
|
|
Multilevel 0
|
Multilevel 1
|
Multilevel 1b
|
Multilevel 1c
|
Multilevel 2
|
Fixed-Effect 1
|
Fixed-Effect 2
|
|
(Intercept)
|
1.60***
|
0.91***
|
0.67**
|
1.02***
|
0.57*
|
|
|
|
|
(0.07)
|
(0.19)
|
(0.21)
|
(0.20)
|
(0.27)
|
|
|
|
relevel(cluster, ref = “compagnonnage”)parallele
|
|
0.80***
|
0.78***
|
0.99***
|
0.77***
|
0.55***
|
0.51**
|
|
|
|
(0.14)
|
(0.14)
|
(0.19)
|
(0.15)
|
(0.16)
|
(0.16)
|
|
relevel(cluster, ref = “compagnonnage”)association
|
|
0.80***
|
0.76***
|
0.87***
|
0.75***
|
0.54**
|
0.46**
|
|
|
|
(0.16)
|
(0.16)
|
(0.21)
|
(0.16)
|
(0.18)
|
(0.18)
|
|
relevel(cluster, ref = “compagnonnage”)cocon
|
|
0.38**
|
0.35**
|
0.33*
|
0.33**
|
0.34*
|
0.28*
|
|
|
|
(0.12)
|
(0.12)
|
(0.16)
|
(0.13)
|
(0.13)
|
(0.14)
|
|
relevel(cluster, ref = “compagnonnage”)bastion
|
|
0.18
|
0.14
|
0.30
|
0.13
|
0.23
|
0.17
|
|
|
|
(0.13)
|
(0.13)
|
(0.17)
|
(0.13)
|
(0.15)
|
(0.15)
|
|
factor(wave)2
|
|
1.32***
|
1.73***
|
1.32***
|
1.31***
|
1.30***
|
1.32***
|
|
|
|
(0.09)
|
(0.19)
|
(0.09)
|
(0.09)
|
(0.09)
|
(0.10)
|
|
factor(wave)3
|
|
1.23***
|
1.61***
|
1.23***
|
1.24***
|
1.23***
|
1.28***
|
|
|
|
(0.09)
|
(0.19)
|
(0.09)
|
(0.10)
|
(0.09)
|
(0.10)
|
|
length
|
|
-0.03***
|
-0.01
|
-0.03***
|
-0.02***
|
|
|
|
|
|
(0.01)
|
(0.01)
|
(0.01)
|
(0.01)
|
|
|
|
factor(wave)2:length
|
|
|
-0.02*
|
|
|
|
|
|
|
|
|
(0.01)
|
|
|
|
|
|
factor(wave)3:length
|
|
|
-0.02*
|
|
|
|
|
|
|
|
|
(0.01)
|
|
|
|
|
|
sexh
|
|
|
|
-0.23
|
-0.42***
|
|
|
|
|
|
|
|
(0.15)
|
(0.10)
|
|
|
|
relevel(cluster, ref = “compagnonnage”)parallele:sexh
|
|
|
|
-0.38
|
|
|
|
|
|
|
|
|
(0.24)
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)association:sexh
|
|
|
|
-0.15
|
|
|
|
|
|
|
|
|
(0.27)
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)cocon:sexh
|
|
|
|
0.09
|
|
|
|
|
|
|
|
|
(0.21)
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)bastion:sexh
|
|
|
|
-0.24
|
|
|
|
|
|
|
|
|
(0.22)
|
|
|
|
|
enfenfants_oui
|
|
|
|
|
0.48*
|
|
0.14
|
|
|
|
|
|
|
(0.20)
|
|
(0.30)
|
|
educprof_school
|
|
|
|
|
0.02
|
|
-0.12
|
|
|
|
|
|
|
(0.11)
|
|
(0.15)
|
|
educuniversity
|
|
|
|
|
0.11
|
|
-0.37
|
|
|
|
|
|
|
(0.14)
|
|
(0.26)
|
|
activ25-75%
|
|
|
|
|
0.07
|
|
0.03
|
|
|
|
|
|
|
(0.12)
|
|
(0.14)
|
|
activ>=80%
|
|
|
|
|
0.21
|
|
0.39**
|
|
|
|
|
|
|
(0.12)
|
|
(0.14)
|
|
AIC
|
8713.47
|
8459.58
|
8471.88
|
8457.61
|
8360.45
|
|
|
|
BIC
|
8735.99
|
8521.51
|
8545.06
|
8547.68
|
8455.92
|
|
|
|
Log Likelihood
|
-4352.74
|
-4218.79
|
-4222.94
|
-4212.80
|
-4163.22
|
|
|
|
Num. obs.
|
2058
|
2058
|
2058
|
2058
|
2030
|
2058
|
2030
|
|
Num. groups: id:v2.idmen
|
686
|
686
|
686
|
686
|
686
|
|
|
|
Num. groups: v2.idmen
|
343
|
343
|
343
|
343
|
343
|
|
|
|
Var: id:v2.idmen (Intercept)
|
0.19
|
0.36
|
0.36
|
0.30
|
0.33
|
|
|
|
Var: v2.idmen (Intercept)
|
1.28
|
1.03
|
1.04
|
1.06
|
1.06
|
|
|
|
Var: Residual
|
3.15
|
2.65
|
2.64
|
2.66
|
2.65
|
|
|
|
R2
|
|
|
|
|
|
0.16
|
0.16
|
|
Adj. R2
|
|
|
|
|
|
-0.26
|
-0.27
|
|
***p < 0.001; **p < 0.01; *p < 0.05
|
Multilevel models satisf
m0 <- lmer(satisf~1+(1|v2.idmen/id), data=alldat)
print(icc(m0, by_group = TRUE))
## # ICC by Group
##
## Group | ICC
## -------------------
## id:v2.idmen | 0.088
## v2.idmen | 0.310
m0g <- glmer(satisf~1+(1|v2.idmen/id), data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa"))
print(icc(m0g, by_group = TRUE))
## # ICC by Group
##
## Group | ICC
## -------------------
## id:v2.idmen | 0.061
## v2.idmen | 0.438
m1 <- lmer(satisf~relevel(cluster, ref="compagnonnage")+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(satisf~relevel(cluster, ref="compagnonnage")+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(satisf~relevel(cluster,ref="compagnonnage")+relevel(cluster,ref="compagnonnage")*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(satisf~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id), data=alldat)
m1g <- glmer(satisf~relevel(cluster, ref="compagnonnage")+factor(wave)+length+(1|v2.idmen/id), data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun=100000)))
m2g <- glmer(satisf~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id),
data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun=100000)))
fm1 <- plm(satisf~relevel(cluster, ref="compagnonnage")+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(satisf~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+educ+enf+activ, index="id", model="within", data=alldat)
htmlreg(list("Multilevel 0"=m0, "Multilevel 1"=m1, "Multilevel 1b"=m1b,"Multilevel 1c"=m1c, "Multilevel 2"=m2, "Fixed-Effect 1"=fm1, "Fixed-Effect 2"=fm2, "Logistic 0"=m0g, "Logistic 1"=m1g, "Logistic 2"=m2g))
Statistical models
|
|
Multilevel 0
|
Multilevel 1
|
Multilevel 1b
|
Multilevel 1c
|
Multilevel 2
|
Fixed-Effect 1
|
Fixed-Effect 2
|
Logistic 0
|
Logistic 1
|
Logistic 2
|
|
(Intercept)
|
0.52***
|
0.43***
|
0.40***
|
0.45***
|
0.37***
|
|
|
0.10
|
-0.48
|
-0.87*
|
|
|
(0.02)
|
(0.05)
|
(0.05)
|
(0.05)
|
(0.07)
|
|
|
(0.11)
|
(0.29)
|
(0.41)
|
|
relevel(cluster, ref = “compagnonnage”)parallele
|
|
0.10**
|
0.10**
|
0.13**
|
0.11**
|
0.05
|
0.05
|
|
0.67**
|
0.69**
|
|
|
|
(0.04)
|
(0.04)
|
(0.05)
|
(0.04)
|
(0.04)
|
(0.04)
|
|
(0.22)
|
(0.22)
|
|
relevel(cluster, ref = “compagnonnage”)association
|
|
0.13***
|
0.13***
|
0.09
|
0.13**
|
0.06
|
0.05
|
|
0.88***
|
0.83***
|
|
|
|
(0.04)
|
(0.04)
|
(0.05)
|
(0.04)
|
(0.04)
|
(0.04)
|
|
(0.24)
|
(0.24)
|
|
relevel(cluster, ref = “compagnonnage”)cocon
|
|
0.09**
|
0.09**
|
0.07
|
0.08**
|
0.08*
|
0.07*
|
|
0.59**
|
0.52**
|
|
|
|
(0.03)
|
(0.03)
|
(0.04)
|
(0.03)
|
(0.03)
|
(0.03)
|
|
(0.19)
|
(0.19)
|
|
relevel(cluster, ref = “compagnonnage”)bastion
|
|
0.07*
|
0.07*
|
0.10*
|
0.07*
|
0.10**
|
0.09*
|
|
0.44*
|
0.40*
|
|
|
|
(0.03)
|
(0.03)
|
(0.04)
|
(0.03)
|
(0.03)
|
(0.04)
|
|
(0.20)
|
(0.20)
|
|
factor(wave)2
|
|
0.05*
|
0.13**
|
0.05*
|
0.04
|
0.04
|
0.04
|
|
0.30*
|
0.28*
|
|
|
|
(0.02)
|
(0.05)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
(0.14)
|
(0.14)
|
|
factor(wave)3
|
|
0.06**
|
0.09
|
0.06**
|
0.06**
|
0.06**
|
0.06*
|
|
0.39**
|
0.39**
|
|
|
|
(0.02)
|
(0.05)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
(0.14)
|
(0.15)
|
|
length
|
|
-0.00
|
0.00
|
-0.00
|
-0.00
|
|
|
|
-0.00
|
-0.00
|
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
|
|
|
(0.01)
|
(0.01)
|
|
factor(wave)2:length
|
|
|
-0.00*
|
|
|
|
|
|
|
|
|
|
|
|
(0.00)
|
|
|
|
|
|
|
|
|
factor(wave)3:length
|
|
|
-0.00
|
|
|
|
|
|
|
|
|
|
|
|
(0.00)
|
|
|
|
|
|
|
|
|
sexh
|
|
|
|
-0.04
|
-0.05*
|
|
|
|
|
-0.34*
|
|
|
|
|
|
(0.04)
|
(0.02)
|
|
|
|
|
(0.15)
|
|
relevel(cluster, ref = “compagnonnage”)parallele:sexh
|
|
|
|
-0.05
|
|
|
|
|
|
|
|
|
|
|
|
(0.06)
|
|
|
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)association:sexh
|
|
|
|
0.08
|
|
|
|
|
|
|
|
|
|
|
|
(0.06)
|
|
|
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)cocon:sexh
|
|
|
|
0.05
|
|
|
|
|
|
|
|
|
|
|
|
(0.05)
|
|
|
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)bastion:sexh
|
|
|
|
-0.06
|
|
|
|
|
|
|
|
|
|
|
|
(0.05)
|
|
|
|
|
|
|
|
educprof_school
|
|
|
|
|
-0.03
|
|
-0.04
|
|
|
-0.20
|
|
|
|
|
|
|
(0.03)
|
|
(0.04)
|
|
|
(0.16)
|
|
educuniversity
|
|
|
|
|
-0.01
|
|
-0.09
|
|
|
-0.08
|
|
|
|
|
|
|
(0.04)
|
|
(0.06)
|
|
|
(0.21)
|
|
enfenfants_oui
|
|
|
|
|
0.09
|
|
0.09
|
|
|
0.54
|
|
|
|
|
|
|
(0.05)
|
|
(0.07)
|
|
|
(0.31)
|
|
activ25-75%
|
|
|
|
|
0.02
|
|
0.00
|
|
|
0.16
|
|
|
|
|
|
|
(0.03)
|
|
(0.03)
|
|
|
(0.18)
|
|
activ>=80%
|
|
|
|
|
0.04
|
|
0.05
|
|
|
0.25
|
|
|
|
|
|
|
(0.03)
|
|
(0.03)
|
|
|
(0.18)
|
|
AIC
|
2598.46
|
2632.86
|
2653.81
|
2652.24
|
2632.87
|
|
|
2504.63
|
2495.16
|
2465.31
|
|
BIC
|
2620.97
|
2694.78
|
2726.99
|
2742.31
|
2728.33
|
|
|
2521.52
|
2551.45
|
2555.17
|
|
Log Likelihood
|
-1295.23
|
-1305.43
|
-1313.90
|
-1310.12
|
-1299.43
|
|
|
-1249.32
|
-1237.58
|
-1216.66
|
|
Num. obs.
|
2057
|
2057
|
2057
|
2057
|
2030
|
2057
|
2030
|
2057
|
2057
|
2030
|
|
Num. groups: id:v2.idmen
|
686
|
686
|
686
|
686
|
686
|
|
|
686
|
686
|
686
|
|
Num. groups: v2.idmen
|
343
|
343
|
343
|
343
|
343
|
|
|
343
|
343
|
343
|
|
Var: id:v2.idmen (Intercept)
|
0.02
|
0.02
|
0.02
|
0.02
|
0.02
|
|
|
0.40
|
0.43
|
0.39
|
|
Var: v2.idmen (Intercept)
|
0.08
|
0.07
|
0.07
|
0.07
|
0.07
|
|
|
2.88
|
2.70
|
2.68
|
|
Var: Residual
|
0.15
|
0.15
|
0.15
|
0.15
|
0.15
|
|
|
|
|
|
|
R2
|
|
|
|
|
|
0.01
|
0.01
|
|
|
|
|
Adj. R2
|
|
|
|
|
|
-0.49
|
-0.50
|
|
|
|
|
***p < 0.001; **p < 0.01; *p < 0.05
|
Multilevel models sep
m0 <- lmer(sep~1+(1|v2.idmen/id), data=alldat)
print(icc(m0, by_group = TRUE))
## # ICC by Group
##
## Group | ICC
## -------------------
## id:v2.idmen | 0.163
## v2.idmen | 0.309
m0g <- glmer(sep~1+(1|v2.idmen/id), data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa"))
print(icc(m0g, by_group = TRUE))
## # ICC by Group
##
## Group | ICC
## -------------------
## id:v2.idmen | 0.169
## v2.idmen | 0.483
m1 <- lmer(sep~relevel(cluster, ref="compagnonnage")+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(sep~relevel(cluster, ref="compagnonnage")+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(sep~relevel(cluster, ref="compagnonnage")+relevel(cluster, ref="compagnonnage")*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(sep~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id),data=alldat)
m1g <- glmer(sep~relevel(cluster, ref="compagnonnage")+factor(wave)+length+(1|v2.idmen/id), data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun=100000)))
m2g <- glmer(sep~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id),
data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa",
optCtrl = list(maxfun=100000)))
fm1 <- plm(sep~relevel(cluster, ref="compagnonnage")+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(sep~relevel(cluster, ref="compagnonnage")+sex+factor(wave)+length+educ+enf+activ, index="id", model="within", data=alldat)
htmlreg(list("Multilevel 0"=m0, "Multilevel 1"=m1, "Multilevel 1b"=m1b,"Multilevel 1c"=m1c, "Multilevel 2"=m2, "Fixed-Effect 1"=fm1, "Fixed-Effect 2"=fm2, "Logistic 0"=m0g, "Logistic 1"=m1g, "Logistic 2"=m2g))
Statistical models
|
|
Multilevel 0
|
Multilevel 1
|
Multilevel 1b
|
Multilevel 1c
|
Multilevel 2
|
Fixed-Effect 1
|
Fixed-Effect 2
|
Logistic 0
|
Logistic 1
|
Logistic 2
|
|
(Intercept)
|
0.26***
|
0.26***
|
0.17***
|
0.29***
|
0.16**
|
|
|
-2.05***
|
-2.02***
|
-3.05***
|
|
|
(0.02)
|
(0.04)
|
(0.05)
|
(0.04)
|
(0.06)
|
|
|
(0.19)
|
(0.40)
|
(0.58)
|
|
relevel(cluster, ref = “compagnonnage”)parallele
|
|
0.05
|
0.04
|
0.10**
|
0.05
|
0.01
|
0.01
|
|
0.46
|
0.51
|
|
|
|
(0.03)
|
(0.03)
|
(0.04)
|
(0.03)
|
(0.03)
|
(0.03)
|
|
(0.28)
|
(0.28)
|
|
relevel(cluster, ref = “compagnonnage”)association
|
|
0.05
|
0.04
|
0.13**
|
0.04
|
-0.00
|
-0.02
|
|
0.54
|
0.44
|
|
|
|
(0.03)
|
(0.03)
|
(0.04)
|
(0.03)
|
(0.03)
|
(0.03)
|
|
(0.29)
|
(0.30)
|
|
relevel(cluster, ref = “compagnonnage”)cocon
|
|
0.05
|
0.04
|
0.04
|
0.04
|
0.05
|
0.03
|
|
0.45
|
0.40
|
|
|
|
(0.02)
|
(0.02)
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.03)
|
|
(0.24)
|
(0.25)
|
|
relevel(cluster, ref = “compagnonnage”)bastion
|
|
-0.02
|
-0.03
|
0.02
|
-0.03
|
-0.02
|
-0.04
|
|
-0.31
|
-0.37
|
|
|
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.03)
|
(0.03)
|
|
(0.27)
|
(0.28)
|
|
factor(wave)2
|
|
0.04*
|
0.17***
|
0.04*
|
0.05*
|
0.03
|
0.04*
|
|
0.35*
|
0.47*
|
|
|
|
(0.02)
|
(0.04)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
(0.17)
|
(0.19)
|
|
factor(wave)3
|
|
0.04*
|
0.20***
|
0.04*
|
0.07***
|
0.04*
|
0.06**
|
|
0.42*
|
0.63**
|
|
|
|
(0.02)
|
(0.04)
|
(0.02)
|
(0.02)
|
(0.02)
|
(0.02)
|
|
(0.17)
|
(0.19)
|
|
length
|
|
-0.00
|
0.00
|
-0.00
|
-0.00
|
|
|
|
-0.03
|
-0.02
|
|
|
|
(0.00)
|
(0.00)
|
(0.00)
|
(0.00)
|
|
|
|
(0.02)
|
(0.02)
|
|
factor(wave)2:length
|
|
|
-0.01***
|
|
|
|
|
|
|
|
|
|
|
|
(0.00)
|
|
|
|
|
|
|
|
|
factor(wave)3:length
|
|
|
-0.01***
|
|
|
|
|
|
|
|
|
|
|
|
(0.00)
|
|
|
|
|
|
|
|
|
sexh
|
|
|
|
-0.05
|
-0.12***
|
|
|
|
|
-1.09***
|
|
|
|
|
|
(0.03)
|
(0.02)
|
|
|
|
|
(0.21)
|
|
relevel(cluster, ref = “compagnonnage”)parallele:sexh
|
|
|
|
-0.11*
|
|
|
|
|
|
|
|
|
|
|
|
(0.05)
|
|
|
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)association:sexh
|
|
|
|
-0.15**
|
|
|
|
|
|
|
|
|
|
|
|
(0.05)
|
|
|
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)cocon:sexh
|
|
|
|
0.01
|
|
|
|
|
|
|
|
|
|
|
|
(0.04)
|
|
|
|
|
|
|
|
relevel(cluster, ref = “compagnonnage”)bastion:sexh
|
|
|
|
-0.08
|
|
|
|
|
|
|
|
|
|
|
|
(0.05)
|
|
|
|
|
|
|
|
educprof_school
|
|
|
|
|
0.01
|
|
-0.02
|
|
|
0.09
|
|
|
|
|
|
|
(0.02)
|
|
(0.03)
|
|
|
(0.22)
|
|
educuniversity
|
|
|
|
|
0.05
|
|
0.01
|
|
|
0.47
|
|
|
|
|
|
|
(0.03)
|
|
(0.05)
|
|
|
(0.29)
|
|
enfenfants_oui
|
|
|
|
|
0.08
|
|
0.12*
|
|
|
0.77
|
|
|
|
|
|
|
(0.04)
|
|
(0.06)
|
|
|
(0.43)
|
|
activ25-75%
|
|
|
|
|
0.08***
|
|
0.08**
|
|
|
0.80***
|
|
|
|
|
|
|
(0.02)
|
|
(0.03)
|
|
|
(0.24)
|
|
activ>=80%
|
|
|
|
|
0.08**
|
|
0.09**
|
|
|
0.74**
|
|
|
|
|
|
|
(0.02)
|
|
(0.03)
|
|
|
(0.24)
|
|
AIC
|
1968.98
|
2009.98
|
2009.83
|
2001.46
|
1978.25
|
|
|
1978.34
|
1971.08
|
1904.93
|
|
BIC
|
1991.50
|
2071.89
|
2083.00
|
2091.51
|
2073.71
|
|
|
1995.23
|
2027.37
|
1994.78
|
|
Log Likelihood
|
-980.49
|
-993.99
|
-991.92
|
-984.73
|
-972.12
|
|
|
-986.17
|
-975.54
|
-936.47
|
|
Num. obs.
|
2056
|
2056
|
2056
|
2056
|
2029
|
2056
|
2029
|
2056
|
2056
|
2029
|
|
Num. groups: id:v2.idmen
|
686
|
686
|
686
|
686
|
686
|
|
|
686
|
686
|
686
|
|
Num. groups: v2.idmen
|
343
|
343
|
343
|
343
|
343
|
|
|
343
|
343
|
343
|
|
Var: id:v2.idmen (Intercept)
|
0.03
|
0.03
|
0.03
|
0.03
|
0.03
|
|
|
1.60
|
1.67
|
1.25
|
|
Var: v2.idmen (Intercept)
|
0.06
|
0.06
|
0.06
|
0.06
|
0.06
|
|
|
4.58
|
4.36
|
4.72
|
|
Var: Residual
|
0.10
|
0.10
|
0.10
|
0.10
|
0.10
|
|
|
|
|
|
|
R2
|
|
|
|
|
|
0.01
|
0.02
|
|
|
|
|
Adj. R2
|
|
|
|
|
|
-0.49
|
-0.49
|
|
|
|
|
***p < 0.001; **p < 0.01; *p < 0.05
|
Taux de transition entre vagues 1 et 2
library(DescTools)
homme <- subset(alldat, sex=="h"&wave==1)
homme2 <- subset(alldat, sex=="h"&wave==2)
Desc(table(homme$cluster, homme2$cluster))
## ------------------------------------------------------------------------------
## table(homme$cluster, homme2$cluster) (table)
##
## Summary:
## n: 343, rows: 5, columns: 5
##
## Pearson's Chi-squared test:
## X-squared = 57.47, df = 16, p-value = 1.388e-06
## Log likelihood ratio (G-test) test of independence:
## G = 54.771, X-squared df = 16, p-value = 3.878e-06
## Mantel-Haenszel Chi-squared:
## X-squared = 16.831, df = 1, p-value = 4.087e-05
## [31m
## Warning message:
## Exp. counts < 5: Chi-squared approx. may be incorrect!!
##
## [39m
## Contingency Coeff. 0.379
## Cramer's V 0.205
## Kendall Tau-b 0.184
##
##
## parallele association compagnonnage cocon
##
## parallele freq 14 7 8 9
## perc 4.1% 2.0% 2.3% 2.6%
## p.row 30.4% 15.2% 17.4% 19.6%
## p.col 27.5% 25.0% 7.1% 12.3%
##
## association freq 9 10 16 13
## perc 2.6% 2.9% 4.7% 3.8%
## p.row 17.0% 18.9% 30.2% 24.5%
## p.col 17.6% 35.7% 14.2% 17.8%
##
## compagnonnage freq 4 2 35 13
## perc 1.2% 0.6% 10.2% 3.8%
## p.row 6.2% 3.1% 54.7% 20.3%
## p.col 7.8% 7.1% 31.0% 17.8%
##
## cocon freq 17 4 34 28
## perc 5.0% 1.2% 9.9% 8.2%
## p.row 15.3% 3.6% 30.6% 25.2%
## p.col 33.3% 14.3% 30.1% 38.4%
##
## bastion freq 7 5 20 10
## perc 2.0% 1.5% 5.8% 2.9%
## p.row 10.1% 7.2% 29.0% 14.5%
## p.col 13.7% 17.9% 17.7% 13.7%
##
## Sum freq 51 28 113 73
## perc 14.9% 8.2% 32.9% 21.3%
## p.row . . . .
## p.col . . . .
##
##
## bastion Sum
##
## parallele freq 8 46
## perc 2.3% 13.4%
## p.row 17.4% .
## p.col 10.3% .
##
## association freq 5 53
## perc 1.5% 15.5%
## p.row 9.4% .
## p.col 6.4% .
##
## compagnonnage freq 10 64
## perc 2.9% 18.7%
## p.row 15.6% .
## p.col 12.8% .
##
## cocon freq 28 111
## perc 8.2% 32.4%
## p.row 25.2% .
## p.col 35.9% .
##
## bastion freq 27 69
## perc 7.9% 20.1%
## p.row 39.1% .
## p.col 34.6% .
##
## Sum freq 78 343
## perc 22.7% 100.0%
## p.row . .
## p.col . .
##
Taux de transition entre vagues 2 et 3
homme3 <- subset(alldat, sex=="h"&wave==3)
Desc(table(homme2$cluster, homme3$cluster))
## ------------------------------------------------------------------------------
## table(homme2$cluster, homme3$cluster) (table)
##
## Summary:
## n: 343, rows: 5, columns: 5
##
## Pearson's Chi-squared test:
## X-squared = 110.18, df = 16, p-value = 4.142e-16
## Log likelihood ratio (G-test) test of independence:
## G = 97.503, X-squared df = 16, p-value = 1.016e-13
## Mantel-Haenszel Chi-squared:
## X-squared = 44.36, df = 1, p-value = 2.732e-11
## [31m
## Warning message:
## Exp. counts < 5: Chi-squared approx. may be incorrect!!
##
## [39m
## Contingency Coeff. 0.493
## Cramer's V 0.283
## Kendall Tau-b 0.312
##
##
## parallele association compagnonnage cocon
##
## parallele freq 22 8 8 5
## perc 6.4% 2.3% 2.3% 1.5%
## p.row 43.1% 15.7% 15.7% 9.8%
## p.col 34.9% 20.0% 7.1% 8.5%
##
## association freq 8 10 6 2
## perc 2.3% 2.9% 1.7% 0.6%
## p.row 28.6% 35.7% 21.4% 7.1%
## p.col 12.7% 25.0% 5.3% 3.4%
##
## compagnonnage freq 14 9 62 15
## perc 4.1% 2.6% 18.1% 4.4%
## p.row 12.4% 8.0% 54.9% 13.3%
## p.col 22.2% 22.5% 54.9% 25.4%
##
## cocon freq 14 7 20 21
## perc 4.1% 2.0% 5.8% 6.1%
## p.row 19.2% 9.6% 27.4% 28.8%
## p.col 22.2% 17.5% 17.7% 35.6%
##
## bastion freq 5 6 17 16
## perc 1.5% 1.7% 5.0% 4.7%
## p.row 6.4% 7.7% 21.8% 20.5%
## p.col 7.9% 15.0% 15.0% 27.1%
##
## Sum freq 63 40 113 59
## perc 18.4% 11.7% 32.9% 17.2%
## p.row . . . .
## p.col . . . .
##
##
## bastion Sum
##
## parallele freq 8 51
## perc 2.3% 14.9%
## p.row 15.7% .
## p.col 11.8% .
##
## association freq 2 28
## perc 0.6% 8.2%
## p.row 7.1% .
## p.col 2.9% .
##
## compagnonnage freq 13 113
## perc 3.8% 32.9%
## p.row 11.5% .
## p.col 19.1% .
##
## cocon freq 11 73
## perc 3.2% 21.3%
## p.row 15.1% .
## p.col 16.2% .
##
## bastion freq 34 78
## perc 9.9% 22.7%
## p.row 43.6% .
## p.col 50.0% .
##
## Sum freq 68 343
## perc 19.8% 100.0%
## p.row . .
## p.col . .
##
