batch

note  Modelling total income, ML models

retr recode.sav
logo chip2.out

response "logearn" 

maxi 500
tole 3

calc c105 = c5*c5 
name c105 'exp2'

calc c101 = 'gross_d'*'edu'*'sex'
name c101 'g_edusex'

calc c24 = 'gross_d'*'edu'
calc c25 = 'gross_d'*'exp'
calc c26 = 'gross_d'*'cpc'
calc c27 = 'gross_d'*'sex'
name c24 'g_edu' c25 'G_exp' c26 'G_cpc' c27 'G_sex'

summary

calc c55='edu'*'sex'
name c55 'eduL_sex'



note      Variance components model
explanatory "cons" "edu" "exp" "exp2" "cpc" "sex" 'eduL_sex' 
setv 1 'cons'
setv 2 'cons'
sett
start
fixed
random
like 

note      Random Coefficients model with certain covariance
clrv 2 
sete 2 "cons" "cons" 
sete 2 "edu" "edu" 
sete 2 "exp" "exp" 
sete 2 "cpc" "cpc" 
sete 2 "sex" "sex" 
note sete 2 "cons" "edu" 
note sete 2 "cons" "exp" 
note sete 2 "cons" "cpc" 
note sete 2 "cons" "sex" 
sett
start
fixed
random
like 

note      Multi-level effects: no sex-edu interactions
explanatory 'gross_d' 'g_edu' 'G_exp' 'G_cpc' 'G_sex'
sett
clrv 2 
sete 2 "cons" "cons" 
sete 2 "edu" "edu" 
sete 2 "exp" "exp" 
sete 2 "cpc" "cpc" 
sete 2 "sex" "sex" 
start
fixed
random
like 

note   trimmed Multi-level effects: no sex-edu interactions
explanatory 'G_cpc' 'G_sex'
sett
clrv 2 
sete 2 "cons" "cons" 
sete 2 "edu" "edu" 
sete 2 "exp" "exp" 
sete 2 "cpc" "cpc" 
sete 2 "sex" "sex" 
start
fixed
random
like 


note      Multi-level effects: adding three-way interaction
explanatory 'G_sex' 'g_edusex' 
sett
clrv 2 
sete 2 "cons" "cons" 
sete 2 "edu" "edu" 
sete 2 "exp" "exp" 
sete 2 "cpc" "cpc" 
sete 2 "sex" "sex" 
start
fixed
random
like 

note   Full Multi-level effects: no sex-edu interactions
explanatory 'G_sex'
sett
clrv 2 
sete 2 "cons" "cons" 
sete 2 "edu" "edu" 
sete 2 "exp" "exp" 
sete 2 "cpc" "cpc" 
sete 2 "sex" "sex" 
start
fixed
random
like 



endobey