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="companionship")+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(probl~relevel(cluster, ref="companionship")+sex+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(probl~relevel(cluster, ref="companionship")+relevel(cluster, ref="companionship")*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(probl~relevel(cluster, ref="companionship")+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id), data=alldat)
fm1 <- plm(probl~relevel(cluster, ref="companionship")+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(probl~relevel(cluster, ref="companionship")+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*** 1.16** 0.85* 1.20** 0.58    
  (0.07) (0.38) (0.40) (0.39) (0.44)    
relevel(cluster, ref = “companionship”)cocoon   0.31 0.36 0.18 0.37 0.40 0.39
    (0.35) (0.35) (0.36) (0.36) (0.38) (0.39)
relevel(cluster, ref = “companionship”)bastion   0.49 0.55 0.37 0.55 0.89* 0.82*
    (0.38) (0.38) (0.41) (0.39) (0.41) (0.42)
relevel(cluster, ref = “companionship”)association   0.43*** 0.40** 0.43* 0.39** 0.31* 0.25
    (0.13) (0.13) (0.17) (0.13) (0.14) (0.14)
relevel(cluster, ref = “companionship”)parallel   0.58*** 0.53*** 0.60** 0.52*** 0.39* 0.33*
    (0.14) (0.14) (0.19) (0.15) (0.16) (0.16)
sexh   -0.33*** -0.33*** -0.41** -0.43***    
    (0.08) (0.08) (0.13) (0.11)    
factor(wave)2   1.36*** 1.83*** 1.36*** 1.43*** 1.56*** 1.59***
    (0.34) (0.39) (0.34) (0.35) (0.37) (0.37)
factor(wave)3   1.31*** 1.75*** 1.31*** 1.40*** 1.51*** 1.59***
    (0.34) (0.39) (0.34) (0.35) (0.37) (0.38)
length   -0.03*** -0.01 -0.03*** -0.03***    
    (0.01) (0.01) (0.01) (0.01)    
factor(wave)2:length     -0.02*        
      (0.01)        
factor(wave)3:length     -0.02*        
      (0.01)        
relevel(cluster, ref = “companionship”)cocoon:sexh       0.27      
        (0.19)      
relevel(cluster, ref = “companionship”)bastion:sexh       0.23      
        (0.32)      
relevel(cluster, ref = “companionship”)association:sexh       0.01      
        (0.22)      
relevel(cluster, ref = “companionship”)parallel:sexh       -0.05      
        (0.25)      
educprof_school         0.03   -0.12
          (0.11)   (0.15)
educuniversity         0.11   -0.37
          (0.15)   (0.26)
enfenfants_oui         0.45*   0.14
          (0.21)   (0.30)
activ25-75%         0.08   0.04
          (0.12)   (0.15)
activ>=80%         0.20   0.38**
          (0.12)   (0.14)
activretired         -0.05   -0.13
          (0.19)   (0.20)
AIC 8713.47 8466.00 8478.51 8475.28 8381.13    
BIC 8735.99 8533.55 8557.33 8565.36 8482.21    
Log Likelihood -4352.74 -4221.00 -4225.26 -4221.64 -4172.56    
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 0.31 0.33    
Var: v2.idmen (Intercept) 1.28 1.17 1.18 1.17 1.18    
Var: Residual 3.15 2.65 2.64 2.65 2.65    
R2           0.17 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="companionship")+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(satisf~relevel(cluster, ref="companionship")+sex+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(satisf~relevel(cluster, ref="companionship")+relevel(cluster, ref="companionship")*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(satisf~relevel(cluster, ref="companionship")+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id), data=alldat)

m1g <- glmer(satisf~relevel(cluster, ref="companionship")+sex+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="companionship")+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="companionship")+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(satisf~relevel(cluster, ref="companionship")+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*** 0.59*** 0.54*** 0.60*** 0.48***     0.10 0.51 -0.13
  (0.02) (0.09) (0.10) (0.09) (0.11)     (0.11) (0.58) (0.65)
relevel(cluster, ref = “companionship”)cocoon   -0.04 -0.03 -0.06 -0.04 -0.05 -0.05   -0.23 -0.22
    (0.08) (0.08) (0.09) (0.09) (0.09) (0.09)   (0.53) (0.54)
relevel(cluster, ref = “companionship”)bastion   -0.09 -0.09 -0.14 -0.07 -0.05 -0.03   -0.58 -0.48
    (0.09) (0.09) (0.10) (0.09) (0.10) (0.10)   (0.57) (0.58)
relevel(cluster, ref = “companionship”)association   0.06* 0.05 0.01 0.05 0.02 0.00   0.40* 0.32
    (0.03) (0.03) (0.04) (0.03) (0.03) (0.03)   (0.19) (0.19)
relevel(cluster, ref = “companionship”)parallel   0.06 0.05 0.07 0.05 0.01 -0.01   0.39 0.34
    (0.03) (0.03) (0.05) (0.04) (0.04) (0.04)   (0.22) (0.22)
sexh   -0.05* -0.05* -0.07* -0.04       -0.29* -0.26
    (0.02) (0.02) (0.03) (0.03)       (0.12) (0.15)
factor(wave)2   -0.04 0.05 -0.04 -0.04 -0.02 -0.01   -0.26 -0.21
    (0.08) (0.09) (0.08) (0.08) (0.09) (0.09)   (0.52) (0.52)
factor(wave)3   -0.03 0.01 -0.03 -0.03 -0.00 -0.00   -0.15 -0.17
    (0.08) (0.09) (0.08) (0.08) (0.09) (0.09)   (0.51) (0.53)
length   -0.00 0.00 -0.00 -0.00       -0.01 -0.01
    (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)              
relevel(cluster, ref = “companionship”)cocoon:sexh       0.04            
        (0.04)            
relevel(cluster, ref = “companionship”)bastion:sexh       0.09            
        (0.08)            
relevel(cluster, ref = “companionship”)association:sexh       0.10            
        (0.05)            
relevel(cluster, ref = “companionship”)parallel:sexh       -0.02            
        (0.06)            
educprof_school         -0.03   -0.04     -0.19
          (0.03)   (0.04)     (0.16)
educuniversity         -0.02   -0.09     -0.08
          (0.04)   (0.06)     (0.21)
enfenfants_oui         0.10   0.11     0.57
          (0.05)   (0.07)     (0.31)
activ25-75%         0.04   0.02     0.25
          (0.03)   (0.04)     (0.18)
activ>=80%         0.04   0.06     0.26
          (0.03)   (0.03)     (0.18)
activretired         0.08   0.08     0.52
          (0.05)   (0.05)     (0.28)
AIC 2598.46 2643.31 2664.08 2662.09 2643.66     2504.63 2501.99 2474.99
BIC 2620.97 2710.86 2742.88 2752.16 2744.74     2521.52 2563.91 2570.46
Log Likelihood -1295.23 -1309.66 -1318.04 -1315.05 -1303.83     -1249.32 -1240.00 -1220.49
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 0.38 0.38
Var: v2.idmen (Intercept) 0.08 0.08 0.08 0.07 0.07     2.88 2.76 2.77
Var: Residual 0.15 0.15 0.15 0.15 0.15          
R2           0.00 0.01      
Adj. R2           -0.50 -0.51      
***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="companionship")+sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m1b <- lmer(sep~relevel(cluster, ref="companionship")+sex+factor(wave)+factor(wave)*length+(1|v2.idmen/id), data=alldat)
m1c <- lmer(sep~relevel(cluster, ref="companionship")+relevel(cluster, ref="companionship")*sex+factor(wave)+length+(1|v2.idmen/id), data=alldat)
m2 <- lmer(sep~relevel(cluster, ref="companionship")+sex+factor(wave)+length+educ+enf+activ+(1|v2.idmen/id), data=alldat)


m1g <- glmer(sep~relevel(cluster, ref="companionship")+sex+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="companionship")+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="companionship")+sex+factor(wave), index="id", model="within", data=alldat)
fm2 <- plm(sep~relevel(cluster, ref="companionship")+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*** 0.24** 0.36*** 0.22*     -2.05*** -1.04 -2.41**
  (0.02) (0.08) (0.08) (0.08) (0.09)     (0.19) (0.69) (0.82)
relevel(cluster, ref = “companionship”)cocoon   -0.01 0.01 -0.03 -0.02 0.04 0.02   -0.11 -0.23
    (0.07) (0.07) (0.07) (0.07) (0.08) (0.08)   (0.60) (0.62)
relevel(cluster, ref = “companionship”)bastion   -0.05 -0.02 -0.09 -0.05 0.04 0.03   -0.70 -0.74
    (0.08) (0.08) (0.08) (0.08) (0.08) (0.08)   (0.70) (0.73)
relevel(cluster, ref = “companionship”)association   0.06* 0.05* 0.07* 0.05* 0.05 0.03   0.56* 0.49*
    (0.03) (0.03) (0.03) (0.03) (0.03) (0.03)   (0.24) (0.24)
relevel(cluster, ref = “companionship”)parallel   0.05 0.03 0.04 0.04 0.02 0.01   0.42 0.39
    (0.03) (0.03) (0.04) (0.03) (0.03) (0.03)   (0.27) (0.27)
sexh   -0.10*** -0.10*** -0.12*** -0.11***       -0.96*** -1.06***
    (0.02) (0.02) (0.03) (0.02)       (0.17) (0.22)
factor(wave)2   -0.01 0.14 -0.01 -0.01 0.05 0.05   -0.14 -0.10
    (0.07) (0.08) (0.07) (0.07) (0.07) (0.07)   (0.58) (0.60)
factor(wave)3   0.00 0.18* 0.00 0.01 0.06 0.07   -0.05 0.06
    (0.07) (0.08) (0.07) (0.07) (0.07) (0.07)   (0.58) (0.60)
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)              
relevel(cluster, ref = “companionship”)cocoon:sexh       0.03            
        (0.04)            
relevel(cluster, ref = “companionship”)bastion:sexh       0.09            
        (0.07)            
relevel(cluster, ref = “companionship”)association:sexh       -0.02            
        (0.05)            
relevel(cluster, ref = “companionship”)parallel:sexh       0.01            
        (0.05)            
educprof_school         0.01   -0.02     0.09
          (0.02)   (0.03)     (0.21)
educuniversity         0.05   0.01     0.49
          (0.03)   (0.05)     (0.29)
enfenfants_oui         0.07   0.12*     0.67
          (0.04)   (0.06)     (0.43)
activ25-75%         0.08***   0.08**     0.80***
          (0.02)   (0.03)     (0.24)
activ>=80%         0.07**   0.08**     0.72**
          (0.02)   (0.03)     (0.24)
activretired         0.02   0.01     0.20
          (0.04)   (0.04)     (0.36)
AIC 1968.98 1989.99 1991.93 2011.94 1987.15     1978.34 1946.87 1912.96
BIC 1991.50 2057.53 2070.73 2102.00 2088.22     1995.23 2008.78 2008.42
Log Likelihood -980.49 -982.99 -981.97 -989.97 -975.57     -986.17 -962.43 -939.48
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 1.15 1.22
Var: v2.idmen (Intercept) 0.06 0.06 0.06 0.06 0.06     4.58 4.57 4.69
Var: Residual 0.10 0.10 0.10 0.10 0.10          
R2           0.01 0.02      
Adj. R2           -0.50 -0.50      
***p < 0.001; **p < 0.01; *p < 0.05 


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