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~cluster+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(probl~cluster+sex+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(probl~cluster+cluster*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(probl~cluster+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id), data=alldat)
fm1 <- plm(probl~cluster+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(probl~cluster+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*** 1.88*** 1.61*** 2.01*** 1.35***    
  (0.07) (0.20) (0.22) (0.21) (0.27)    
clusterassociation   -0.01 -0.01 -0.12 -0.02 -0.01 -0.05
    (0.17) (0.17) (0.22) (0.17) (0.18) (0.19)
clustercompagnonnage   -0.80*** -0.78*** -0.99*** -0.77*** -0.55*** -0.51**
    (0.14) (0.14) (0.19) (0.15) (0.16) (0.16)
clustercocon   -0.43** -0.43** -0.66*** -0.44** -0.21 -0.23
    (0.15) (0.15) (0.19) (0.15) (0.16) (0.16)
clusterbastion   -0.63*** -0.63*** -0.69*** -0.65*** -0.32 -0.34*
    (0.15) (0.15) (0.20) (0.15) (0.17) (0.17)
sexh   -0.33*** -0.33*** -0.60** -0.42***    
    (0.08) (0.08) (0.20) (0.10)    
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)        
clusterassociation:sexh       0.23      
        (0.29)      
clustercompagnonnage:sexh       0.38      
        (0.24)      
clustercocon:sexh       0.47      
        (0.25)      
clusterbastion:sexh       0.14      
        (0.26)      
educprof_school         0.02   -0.12
          (0.11)   (0.15)
educuniversity         0.11   -0.37
          (0.14)   (0.26)
enfenfants_oui         0.48*   0.14
          (0.20)   (0.30)
activ25-75%         0.07   0.03
          (0.12)   (0.14)
activ>=80%         0.21   0.39**
          (0.12)   (0.14)
AIC 8713.47 8449.40 8461.70 8457.61 8360.45    
BIC 8735.99 8516.96 8540.51 8547.68 8455.92    
Log Likelihood -4352.74 -4212.70 -4216.85 -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 0.31 0.31 0.30 0.33    
Var: v2.idmen (Intercept) 1.28 1.06 1.06 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~cluster+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(satisf~cluster+sex+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(satisf~cluster+cluster*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(satisf~cluster+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id), data=alldat)

m1g <- glmer(satisf~cluster+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa", 
                                                                  optCtrl = list(maxfun=100000)))
m2g <- glmer(satisf~cluster+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~cluster+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(satisf~cluster+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*** 0.56*** 0.52*** 0.58*** 0.47***     0.10 0.34 -0.18
  (0.02) (0.05) (0.05) (0.05) (0.07)     (0.11) (0.30) (0.42)
clusterassociation   0.03 0.03 -0.04 0.02 0.02 0.00   0.20 0.14
    (0.04) (0.04) (0.05) (0.04) (0.04) (0.04)   (0.26) (0.26)
clustercompagnonnage   -0.10** -0.10** -0.13** -0.11** -0.05 -0.05   -0.68** -0.69**
    (0.04) (0.04) (0.05) (0.04) (0.04) (0.04)   (0.22) (0.22)
clustercocon   -0.01 -0.01 -0.06 -0.02 0.03 0.01   -0.08 -0.17
    (0.04) (0.04) (0.05) (0.04) (0.04) (0.04)   (0.22) (0.22)
clusterbastion   -0.03 -0.03 -0.03 -0.04 0.05 0.04   -0.24 -0.29
    (0.04) (0.04) (0.05) (0.04) (0.04) (0.04)   (0.23) (0.23)
sexh   -0.05* -0.05* -0.10* -0.05*       -0.29* -0.34*
    (0.02) (0.02) (0.05) (0.02)       (0.12) (0.15)
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)              
clusterassociation:sexh       0.13            
        (0.07)            
clustercompagnonnage:sexh       0.05            
        (0.06)            
clustercocon:sexh       0.11            
        (0.06)            
clusterbastion:sexh       -0.01            
        (0.06)            
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 2635.90 2656.85 2652.24 2632.87     2504.63 2491.54 2465.31
BIC 2620.97 2703.45 2735.66 2742.31 2728.33     2521.52 2553.45 2555.17
Log Likelihood -1295.23 -1305.95 -1314.43 -1310.12 -1299.43     -1249.32 -1234.77 -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 0.39 0.39
Var: v2.idmen (Intercept) 0.08 0.07 0.07 0.07 0.07     2.88 2.72 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~cluster+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(sep~cluster+sex+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(sep~cluster+cluster*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(sep~cluster+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id), data=alldat)


m1g <- glmer(sep~cluster+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat, family=binomial, control = glmerControl(optimizer = "bobyqa", 
                                                                  optCtrl = list(maxfun=100000)))
m2g <- glmer(sep~cluster+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~cluster+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(sep~cluster+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*** 0.36*** 0.26*** 0.39*** 0.21***     -2.05*** -1.05** -2.54***
  (0.02) (0.04) (0.05) (0.05) (0.06)     (0.19) (0.40) (0.58)
clusterassociation   0.01 0.00 0.02 -0.01 -0.01 -0.03   0.08 -0.06
    (0.03) (0.03) (0.05) (0.03) (0.04) (0.04)   (0.31) (0.31)
clustercompagnonnage   -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)
clustercocon   0.00 0.00 -0.06 -0.01 0.04 0.02   -0.01 -0.11
    (0.03) (0.03) (0.04) (0.03) (0.03) (0.03)   (0.27) (0.28)
clusterbastion   -0.07* -0.07* -0.08* -0.08** -0.03 -0.05   -0.76* -0.88**
    (0.03) (0.03) (0.04) (0.03) (0.03) (0.03)   (0.30) (0.31)
sexh   -0.10*** -0.10*** -0.16*** -0.12***       -0.96*** -1.09***
    (0.02) (0.02) (0.04) (0.02)       (0.18) (0.21)
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* 0.21*** 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)              
clusterassociation:sexh       -0.03            
        (0.06)            
clustercompagnonnage:sexh       0.11*            
        (0.05)            
clustercocon:sexh       0.12*            
        (0.05)            
clusterbastion:sexh       0.03            
        (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 1989.33 1989.16 2001.46 1978.25     1978.34 1942.69 1904.93
BIC 1991.50 2056.87 2067.96 2091.51 2073.71     1995.23 2004.60 1994.78
Log Likelihood -980.49 -982.67 -980.58 -984.73 -972.12     -986.17 -960.34 -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 1.17 1.25
Var: v2.idmen (Intercept) 0.06 0.06 0.06 0.06 0.06     4.58 4.56 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

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