diff --git a/Census2022-CER-mixed_model_0910.R b/Census2022-CER-mixed_model_0910.R
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+++ b/Census2022-CER-mixed_model_0910.R
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+####################################################################################
+## Data Analysis for ESRC Transformative project
+## - Using the Commission for Energy Regulation (CER)'s Irish Smart Meter Trial data
+## - http://www.ucd.ie/issda/data/commissionforenergyregulationcer/
+
+## This file is used to produce midweek (Tues-Thurs) and weekend (Sat-Sun) mixed models which test the ability of
+## various power (electricity consumption) variables to predict the household attributes of interest
+
+## The energy data is for 28 days (4 weeks) of Oct 2009 & 2010
+## October 2009 was before the smart meter trials started and
+## was also close to the pre-trial survey date
+
+## Comparativ emodels also run for 2010
+
+## For the latest version of this code go to: https://github.com/dataknut/Census2022
+
+## This work was funded by RCUK through the ESRC's Transformative Social Science Programme via the
+## "Census 2022: Transforming Small Area Socio-Economic Indicators through 'Big Data'" Project
+## - http://gtr.rcuk.ac.uk/project/2D2CD798-4F04-4399-B1AF-D810A233DD21
+## - http://www.energy.soton.ac.uk/tag/census2022/
+
+## Copyright (C) 2014 University of Southampton
+
+## Author: Sharon Lin (X.Lin@soton.ac.uk)
+## [Energy & Climate Change, Faculty of Engineering & Environment, University of Southampton]
+
+## This program is free software; you can redistribute it and/or modify
+## it under the terms of the GNU General Public License as published by
+## the Free Software Foundation; either version 2 of the License
+## (http://choosealicense.com/licenses/gpl-2.0/), or (at your option) any later version.
+
+## This program is distributed in the hope that it will be useful,
+## but WITHOUT ANY WARRANTY; without even the implied warranty of
+## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+## GNU General Public License for more details.
+
+## #YMMV - http://en.wiktionary.org/wiki/YMMV
+
+
+rm(list=ls())
+library(lme4)
+
+
+
+## load in energy data
+setwd("//soton.ac.uk/ude/PersonalFiles/Users/xl25g12/mydocuments/census2022/CER_analysis/data") ## the file is in working order
+## egdata = read.table("OctHH_midwk_long", header=T, stringsAsFactors=FALSE) ## read in R exported txt file, no need sep="t"; long form of mid week data for all Oct 2009 and 2010 data
+egdata = read.table("CER_OctHH_wkend_long", header=T, stringsAsFactors=FALSE) ## read in R exported txt file, no need sep="t"; long form of mid week data for all Oct 2009 and 2010 data
+eg09 = egdata[egdata$DateOct<365,]
+eg10 = egdata[egdata$DateOct>365,]
+
+## load in HH survey data, note survey content for pre-trial and post-trial are different
+data1 = read.table("HH09_pretrial.txt",header=T,stringsAsFactors=FALSE,sep="\t") ## read in txt file, need sep="t", HHpresurvey data
+HH09FA = read.table("HH_floor_pretrial_survey.txt",header=T,stringsAsFactors=FALSE,sep="\t") ## read in txt file, need sep="t"; pretrial floor area data
+## HH10 = read.table("HH10_posttrial.txt",header=T,stringsAsFactors=FALSE,sep="\t") ## read in txt file, need sep="t", HHpresurvey data
+HH09 = merge(data1,HH09FA)
+
+## merge energy and HH data together
+## data for 09 and 10 separately
+mydata09=merge(eg09,HH09)
+##mydata10=merge(eg10,HH10)
+
+
+
+
+
+########## mixed effects models are run on these separate files ###################
+## check on mixed model with income variables using 09 data
+## make a comparison with 0910 data results using 0910
+
+mydata09$dummy_HHchild = NA
+mydata09 = within(mydata09,dummy_HHchild[HHtype_y09==1]<-0)
+mydata09 = within(mydata09,dummy_HHchild[HHtype_y09==2]<-0)
+mydata09 = within(mydata09,dummy_HHchild[HHtype_y09==3]<-1)
+ddply(mydata09, .(dummy_HHchild),nrow)
+ddply(mydata09, .(HHtype_y09),nrow)
+
+## make a new dummy vriable column D_unemployed
+mydata09$dummy_unemp_R = NA
+mydata09 = within(mydata09,dummy_unemp_R[employment_y09==1]<-0)
+mydata09 = within(mydata09,dummy_unemp_R[employment_y09==4]<-1)
+mydata09 = within(mydata09,dummy_unemp_R[employment_y09==5]<-1)
+mydata09 = within(mydata09,dummy_unemp_R[employment_y09==2]<-NA)
+mydata09 = within(mydata09,dummy_unemp_R[employment_y09==3]<-NA)
+mydata09 = within(mydata09,dummy_unemp_R[employment_y09==6]<-NA)
+
+ddply(mydata09, .(dummy_unemp_R),nrow)
+
+## make a second new dummy vriable column dum_em_mixed, (1-3)
+
+
+
+## prepare dummy variable for "number of residents"
+mydata09["dum_noResident"] = NA
+mydata09 = within(mydata09,dum_noResident[HHtype_y09==1]<-0)
+mydata09 = within(mydata09,dum_noResident[HHtype_y09==3&HHchild_y09==1]<-0)
+mydata09 = within(mydata09,dum_noResident[HHtype_y09==2&HHadult_y09==1]<-0)
+mydata09 = within(mydata09,dum_noResident[HHadult_y09>1]<-1)
+mydata09 = within(mydata09,dum_noResident[HHchild_y09>1]<-1)
+ddply(mydata09, .(dum_noResident),nrow)
+names(mydata09)
+
+
+## change income to approximated mid ranged numbers
+mydata09$income_num = mydata09$income ## add a new column
+mydata09 = within(mydata09,income_num[income_num==1]<-7500)
+mydata09 = within(mydata09,income_num[income_num==2]<-22500)
+mydata09 = within(mydata09,income_num[income_num==3]<-40000)
+mydata09 = within(mydata09,income_num[income_num==4]<-62500)
+mydata09 = within(mydata09,income_num[income_num==5]<-92500)
+mydata09 = within(mydata09,income_num[income_num==6]<-NA)
+
+## change dataframe structure
+mydata09 = within(mydata09,HHtype_y09<-as.factor(HHtype_y09))
+mydata09 = within(mydata09,dummy_unemp_R<-as.factor(dummy_unemp_R))
+mydata09 = within(mydata09,dummy_HHchild<-as.factor(dummy_HHchild))
+mydata = mydata09
+
+
+## the following code are used for weekend data
+final =0
+for (i in c(11,9,12, 3, 5, 7,16,17) )
+{
+ energy = mydata[,i]
+
+ ## C1: test = lmer(energy ~ 1 + (1|HHID) + num_resident + log(income_num) + emp_dummy, data=mydata, na.action="na.omit") ## RE on intercept only
+ ## C2: test = lmer(energy ~ 1 + (1|HHID) + num_resident + log(income_num), data=mydata, na.action="na.omit") ## RE on intercept only
+ ## C3: test = lmer( energy ~ 1 + (1|HHID) + num_resident + log(income_num) + HHtype , data=mydata, na.action="na.omit") ## RE on intercept only
+ ## C4: test = lmer( energy ~ 1 + (1|HHID) + num_resident + log(income_num) + dummy_HHchild , data=mydata, na.action="na.omit") ## RE on intercept only
+ ## C5: test = lmer(energy ~ 1 + (1|HHID) + num_resident + log(income_num) + dummy_HHchild + dummy_unemp_R , data=mydata, na.action="na.omit") ## RE on intercept only
+ ## C4: test = lmer(energy ~ 1 + (1|ID) + dum_noResident + log(income_num) + dummy_HHchild, data=mydata, na.action="na.omit") ## RE on intercept only
+ ## C3: test = lmer(energy ~ 1 + (1|ID) + dum_noResident + log(income_num), data=mydata, na.action="na.omit") ## RE on intercept only
+ ## C1:
+ test = lmer(energy ~ 1 + (1|ID) + dum_noResident + log(income_num)+dummy_unemp_R, data=mydata, na.action="na.omit") ## RE on intercept only
+
+ ## Extract fixed effect coefficients
+ out_FE = as.data.frame(cbind(as.data.frame(coef(summary(test)))[,1],as.data.frame(coef(summary(test)))[,3]))
+ colnames(out_FE)[1] = c(get("i"))
+ colnames(out_FE)[2] = c("zsta")
+
+ ## for 1 RE only: Extract random effect varaince and percentage of ICC (intercorrelation between RE in intercept and residuals)
+ RE1 = as.data.frame(VarCorr(test))$vcov[1] ## variance of HHID ## as.data.frame(VarCorr(test))$sdcor[1] for std. dev.
+ RE2 = as.data.frame(VarCorr(test))$vcov[2] ## varaince of residual ##
+ # alternative code
+ ## as.numeric(summary(test)@REmat[1,3]) ## variance of HHID;
+ ## as.numeric(summary(test)@REmat[1,4]) ## std error of HHID
+ ## as.numeric(summary(test)@REmat[2,4]) ## std error of residuals
+ ## k = 2
+ ## tmp11 = RE1 / (RE1 + RE2) ## % of RE effects in HH
+ ## RE3 = as.numeric(format(round(tmp11,k),nsmall=k)) ## % of RE effects in HH
+ ## RE = NA ## REtmp and out are of different length
+ ## out = cbind(out_FE,RE)
+ ## out$RE[1]=RE1
+ ## out$RE[2]=RE2
+ ## out$RE[3]=RE3
+
+ testlmer = as.vector(test@pp$X %*% fixef(test)) ## fitted values ## length of fitted values is 4099, no adjustment is made
+ VarF=var(testlmer)
+ ## VarV = variance (intercept) + varaince(Slope) + 2 cov.correlation * stdev(intercept)stdev(slope) ## variation generated from RE
+ ## RE_sdcor = as.data.frame(VarCorr(test))$sdcor ## stdev and correlation of two RE effects, and stdev of residuals
+ ## VarV = RE_sdcor[1]^2
+ ## VarR = RE_sdcor[2]^2 ## variation from residuals
+ VarV=RE1
+ VarR = RE2
+ R2.m = VarF/(VarF + VarV + VarR) ## Var(FE)/[Var(FE)+Var(RE) + Var(Res)]
+ R2.c = (VarF+VarV)/(VarF + VarV + VarR)
+ R2.r = VarR / (VarF + VarV + VarR)
+ R2 = NA ## REtmp and out are of different length
+ out = cbind(out_FE,R2)
+ out$R2[1]=R2.m
+ out$R2[2]=R2.c
+ out$R2[3]=R2.r
+
+
+
+# ## for 2 REs in the model: extract RE effects from the model
+# RE_sdcor = as.data.frame(VarCorr(test))$sdcor ## stdev and correlation of two RE effects, and stdev of residuals
+# ## final_FE = cbind(final_FE,out_FE)
+# ## ## final_RE = cbind(final_RE,RE_sdcor)
+# ## R squared for D1-D5 models, for mixed with RE in intercept and slope
+# testlmer = as.vector(test@pp$X %*% fixef(test)) ## fitted values ## length of fitted values is 4099, no adjustment is made
+# VarF=var(testlmer)
+# ## VarV = variance (intercept) + varaince(Slope) + 2 cov.correlation * stdev(intercept)stdev(slope) ## variation generated from RE
+# ## or VarV = RE_sdcor[1]^2 + 2*RE_sdcor[1]*RE_sdcor[2]*RE_sdcor[3] + RE_sdcor[2]^2 ## variation generated from RE
+# VarR = RE_sdcor[4]^2 ## variation from residuals
+# R2.m = VarF/(VarF + VarV + VarR) ## Var(FE)/[Var(FE)+Var(RE) + Var(Res)]
+# R2.c = (VarF+VarV)/(VarF + VarV + VarR)
+# R2.r = VarR / (VarF + VarV + VarR)
+# R2 = NA ## REtmp and out are of different length
+# out = cbind(out_FE,R2)
+# out$R2[1]=R2.m
+# out$R2[2]=R2.c
+# out$R2[3]=R2.r
+#
+ final = cbind(final,out)
+}
+
+
+## RE1 working codes are as above.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+###### models with T1 - T2 (without income variables)
+
+final=0
+for (i in c(3,7,9,11, 12, 16, 17) ) ## mean, q975, ammean, pktime, base, LF (ECF has been consistent not converging, taken out)
+ ## i = 16, ECF does not converge
+{
+ energy = (mydata09[,i])
+ ## D1: test = lmer(energy ~ num_resident + income_num.c + dummy_unemp_R + (1+income_num.c|HHID) , data=mydata, na.action="na.omit") ## RE on intercept and slope
+ ## D2 test = lmer(energy ~ num_resident + income_num.c + (1+income_num.c|HHID), data=mydata, na.action="na.omit") ## RE on intercept and slope
+ ## D3: test = lmer(energy ~ num_resident + income_num.c + HHtype + (1+income_num.c|HHID) , data=mydata, na.action="na.omit") ## RE on intercept and slope
+ ## D4: test = lmer(energy ~ num_resident + income_num.c + dummy_HHchild + (1+income_num.c|HHID) , data=mydata, na.action="na.omit") ## RE on intercept and slope
+
+ ## T1 = lmer(energy ~ 1 + dum_noResident + dum_em13_46 + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+
+ T2 = lmer(energy ~ 1 + dum_noResident + (1|ID), data=mydata09, na.action="na.omit") ## RE on intercept only
+ ## T2a = lmer( energy ~ 1 + dum_noResident + dum_em + (1|ID), data=mydata09, na.action="na.omit") ## RE on intercept only
+ ## T3 = lmer(energy ~ 1 + dum_noResident + dum_HHtype2 + dum_HHtype3 + (1|ID), data=mydata09, na.action="na.omit")
+
+ ## T4 = lmer( energy ~ 1 + dum_noResident + dum_child + (1|ID), data=mydata09, na.action="na.omit") ## RE on intercept only
+ ## T4a = lmer( energy ~ 1 + dum_child + (1|ID), data=mydata09, na.action="na.omit") ## RE on intercept only
+ ## T5 = lmer( energy ~ 1 + dum_noResident + dum_child + dum_em + (1|ID), data=mydata09, na.action="na.omit") ## RE on intercept only
+ ## T6 = lmer( energy ~ 1 + dum_child + dum_noResident + dum_child*dum_noResident + (1|ID), data=mydata09, na.action="na.omit") ## RE on intercept only
+
+ ## T7 = lmer( energy ~ 1 + dum_child + dum_noResident + dum_child*dum_noResident + dum_em + (1|ID), data=mydata09, na.action="na.omit") ## RE on intercept only
+
+ T2 = T2
+
+ ## Extract fixed effect coefficients
+ out = as.data.frame(cbind(as.data.frame(coef(summary(T2)))[,1],as.data.frame(coef(summary(T2)))[,3]))
+ colnames(out)[1] = c(get("i"))
+ colnames(out)[2] = c("zsta")
+
+ ## for 1 RE only: Extract random effect varaince and percentage of ICC (intercorrelation between RE in intercept and residuals)
+ ## command: as.data.frame(VarCorr(test)) gives variance-covariance of the model: as.data.frame(VarCorr(test))$vcov; std.error of the model: as.data.frame(VarCorr(test))$sdcor
+ RE1 = as.data.frame(VarCorr(T2))$vcov[1] ## variance of RE in the intercept "ID"
+ RE2 = as.data.frame(VarCorr(T2))$vcov[2] ## varaince of residual ##
+
+ testlmer = as.vector(T2@pp$X %*% fixef(T2)) ## fitted values ## length of fitted values is 4099, no adjustment is made
+ VarF=var(testlmer)
+ ## VarV = variance (intercept) + varaince(Slope) + 2 cov.correlation * stdev(intercept)stdev(slope) ## variation generated from RE
+ VarV =RE1
+ VarR = RE2 ## variation from residuals
+ R2.m = VarF/(VarF + VarV + VarR) ## Var(FE)/[Var(FE)+Var(RE) + Var(Res)]
+ R2.c = (VarF+VarV)/(VarF + VarV + VarR)
+ R2.r = VarR / (VarF + VarV + VarR)
+ out["R2"]=NA
+ out$R2[1] = R2.m
+ out$R2[2] = R2.c
+ out$R2[3] = R2.r
+ final= cbind(final,out)
+}
+
+
+
+
+
+
+
+
+######## end of mixed effects models on sepearte 09 and 10 periods ###################
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+################# beginning of mixed models on both Oct 09 and Oct 10 data #######################
+
+######## data for 09 and 10 together #############
+HH0910 = read.table("HH_both_survey", header=T, stringsAsFactors=FALSE) ## HH info.
+tmpHH09=HH0910[HH0910$index==0,] ## HH info in Oct09
+tmpHH10=HH0910[HH0910$index==1,] ## HH info in Oct10
+
+tdata09=merge(eg09,tmpHH09) ## HH and enregy infoin Oct 09 where HH has both info (HH participated in both pre and post-survey)
+tdata10=merge(eg10,tmpHH10) ## HH and enregy infoin Oct 10 where HH has both info (HH participated in both pre and post-survey)
+mydata0910 = rbind(tdata09,tdata10)
+
+mydata0910["dum_em"]=NA ## in survey data, 1 is employed; 4 is unemployed
+mydata0910 = within(mydata0910,dum_em[employment==1]<-0)
+mydata0910 = within(mydata0910,dum_em[employment==4]<-1)
+ddply(mydata0910, .(dum_em),nrow)
+names(mydata0910)
+
+mydata0910["dum_child"] = NA
+mydata0910 = within(mydata0910,dum_child[HHtype==1]<-0)
+mydata0910 = within(mydata0910,dum_child[HHtype==2]<-0)
+mydata0910 = within(mydata0910,dum_child[HHtype==3]<-1)
+ddply(mydata0910, .(dum_child),nrow)
+ddply(mydata0910, .(HHtype),nrow)
+names(mydata0910)
+
+## prepare the new columne, count for the number of resdients (0/1),
+mydata0910["dum_noResident"] = NA
+mydata0910 = within(mydata0910,dum_noResident[HHtype==1]<-0)
+mydata0910 = within(mydata0910,dum_noResident[HHtype==3&HHchild==1]<-0)
+mydata0910 = within(mydata0910,dum_noResident[HHtype==2&HHadult==1]<-0)
+mydata0910 = within(mydata0910,dum_noResident[HHadult>1]<-1)
+mydata0910 = within(mydata0910,dum_noResident[HHchild>1]<-1)
+ddply(mydata0910, .(dum_noResident),nrow)
+names(mydata0910)
+
+
+##### prepare for dummy_em, with 1-3 value of employment variable as 0, 4-6 as 1 ######
+mydata0910["dum_em13_46"] = NA
+mydata0910 = within(mydata0910,dum_em13_46[employment<4]<-0) ## 0 for employed people: 1: employee, 2:sel-employed with employees, 3:self-employed without employees;
+mydata0910 = within(mydata0910,dum_em13_46[employment>3]<-1) ## 1 for unemployed people: , 4: unemployed actively seeking work; 5 unemployed not seeking work, 6: retired, 7: carer: looking after relative family)
+ddply(mydata0910, .(dum_em13_46),nrow)
+
+
+## prepare dummy for HHtype, HHtype2 = 1 if HHtype = 2, otherwise 0; HHtype3 = 1 if HHtype = 3, otherwise 0;
+mydata0910["dum_HHtype2"] = NA
+mydata0910 = within(mydata0910,dum_HHtype2[HHtype==2]<-1) ## 0 for employed people: 1: employee, 2:sel-employed with employees, 3:self-employed without employees;
+mydata0910 = within(mydata0910,dum_HHtype2[HHtype==1]<-0) ## 0 for employed people: 1: employee, 2:sel-employed with employees, 3:self-employed without employees;
+mydata0910 = within(mydata0910,dum_HHtype2[HHtype==3]<-0) ## 0 for employed people: 1: employee, 2:sel-employed with employees, 3:self-employed without employees;
+ddply(mydata0910, .(dum_HHtype2),nrow)
+
+
+mydata0910["dum_HHtype3"] = NA
+mydata0910 = within(mydata0910,dum_HHtype3[HHtype==3]<-1) ## 0 for employed people: 1: employee, 2:sel-employed with employees, 3:self-employed without employees;
+mydata0910 = within(mydata0910,dum_HHtype3[HHtype==1]<-0) ## 0 for employed people: 1: employee, 2:sel-employed with employees, 3:self-employed without employees;
+mydata0910 = within(mydata0910,dum_HHtype3[HHtype==2]<-0) ## 0 for employed people: 1: employee, 2:sel-employed with employees, 3:self-employed without employees;
+ddply(mydata0910, .(dum_HHtype3),nrow)
+
+
+## change dataframe structure
+mydata0910 = within(mydata0910,MF<-as.factor(MF))
+mydata0910 = within(mydata0910,HHtype<-as.factor(HHtype))
+mydata0910 = within(mydata0910,age<-as.factor(age))
+mydata0910 = within(mydata0910,employment<-as.factor(employment))
+mydata0910 = within(mydata0910,socialclass<-as.factor(socialclass))
+mydata0910 = within(mydata0910,HHadult<-as.factor(HHadult))
+mydata0910 = within(mydata0910,dayault<-as.factor(dayault))
+mydata0910 = within(mydata0910,dum_child<-as.factor(dum_child))
+mydata0910 = within(mydata0910,daychild<-as.factor(daychild))
+## mydata0910 = within(mydata0910,bedroom<-as.factor(bedroom))
+## mydata0910 = within(mydata0910,heating<-as.factor(heating))
+## mydata0910 = within(mydata0910,income<-as.factor(income))
+mydata0910 = within(mydata0910,dum_em<-as.factor(dum_em))
+mydata0910 = within(mydata0910,dum_HHtype2<-as.factor(dum_HHtype2))
+mydata0910 = within(mydata0910,dum_HHtype3<-as.factor(dum_HHtype3))
+mydata0910 = within(mydata0910,ID<-as.factor(ID))
+mydata0910 = within(mydata0910,dum_child<-as.factor(dum_child))
+mydata0910 = within(mydata0910,dum_noResident<-as.factor(dum_noResident))
+mydata0910 = within(mydata0910,dum_em13_46<-as.factor(dum_em13_46))
+
+
+########## mixed models for RE effect in the intercept ############################
+mydata = mydata0910[order(mydata0910$ID),] ## sort data accoring to HH ID.
+A1 = lmer( dailyammean ~ 1 + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+A2 = lmer( dailyammean ~ 1 + dum_noResident + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+A3 = lmer( dailyammean ~ 1 + dum_noResident + dum_em + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+A4 = lmer( dailyammean ~ 1 + dum_child + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+A5 = lmer( dailyammean ~ 1 + dum_child + dum_em + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+A6 = lmer( dailyammean ~ 1 + dum_child + dum_noResident + dum_child*dum_noResident + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+A7 = lmer( dailyammean ~ 1 + dum_child + dum_noResident + dum_child*dum_noResident + dum_em + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+
+
+############### automation to extract tables from RE ##########################
+########## loop through one energy variable at a time ##########
+final=0
+for (i in c(3,7,9,11, 12, 17) ) ## mean, q975, ammean, pktime, base, LF (ECF has been consistent not converging, taken out)
+ ## i = 16, ECF does not converge
+ {
+ energy = (mydata0910[,i])
+ ## D1: test = lmer(energy ~ num_resident + income_num.c + dummy_unemp_R + (1+income_num.c|HHID) , data=mydata, na.action="na.omit") ## RE on intercept and slope
+ ## D2 test = lmer(energy ~ num_resident + income_num.c + (1+income_num.c|HHID), data=mydata, na.action="na.omit") ## RE on intercept and slope
+ ## D3: test = lmer(energy ~ num_resident + income_num.c + HHtype + (1+income_num.c|HHID) , data=mydata, na.action="na.omit") ## RE on intercept and slope
+ ## D4: test = lmer(energy ~ num_resident + income_num.c + dummy_HHchild + (1+income_num.c|HHID) , data=mydata, na.action="na.omit") ## RE on intercept and slope
+
+ ## T1 = lmer(energy ~ 1 + dum_noResident + dum_em13_46 + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+
+ ## T2 = lmer(energy ~ 1 + dum_noResident + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+ ## T2a = lmer( energy ~ 1 + dum_noResident + dum_em + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+ ## T3 = lmer(energy ~ 1 + dum_noResident + dum_HHtype2 + dum_HHtype3 + (1|ID), data=mydata0910, na.action="na.omit")
+
+ T4 = lmer( energy ~ 1 + dum_noResident + dum_child + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+ ## T4a = lmer( energy ~ 1 + dum_child + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+ T5 = lmer( energy ~ 1 + dum_noResident + dum_child + dum_em + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+ ## T6 = lmer( energy ~ 1 + dum_child + dum_noResident + dum_child*dum_noResident + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+
+ ## T7 = lmer( energy ~ 1 + dum_child + dum_noResident + dum_child*dum_noResident + dum_em + (1|ID), data=mydata0910, na.action="na.omit") ## RE on intercept only
+
+ T2 = T5
+
+ ## Extract fixed effect coefficients
+ out = as.data.frame(cbind(as.data.frame(coef(summary(T2)))[,1],as.data.frame(coef(summary(T2)))[,3]))
+ colnames(out)[1] = c(get("i"))
+ colnames(out)[2] = c("zsta")
+
+ ## for 1 RE only: Extract random effect varaince and percentage of ICC (intercorrelation between RE in intercept and residuals)
+ ## command: as.data.frame(VarCorr(test)) gives variance-covariance of the model: as.data.frame(VarCorr(test))$vcov; std.error of the model: as.data.frame(VarCorr(test))$sdcor
+ RE1 = as.data.frame(VarCorr(T2))$vcov[1] ## variance of RE in the intercept "ID"
+ RE2 = as.data.frame(VarCorr(T2))$vcov[2] ## varaince of residual ##
+
+ testlmer = as.vector(T2@pp$X %*% fixef(T2)) ## fitted values ## length of fitted values is 4099, no adjustment is made
+ VarF=var(testlmer)
+ ## VarV = variance (intercept) + varaince(Slope) + 2 cov.correlation * stdev(intercept)stdev(slope) ## variation generated from RE
+ VarV =RE1
+ VarR = RE2 ## variation from residuals
+ R2.m = VarF/(VarF + VarV + VarR) ## Var(FE)/[Var(FE)+Var(RE) + Var(Res)]
+ R2.c = (VarF+VarV)/(VarF + VarV + VarR)
+ R2.r = VarR / (VarF + VarV + VarR)
+ out["R2"]=NA
+ out$R2[1] = R2.m
+ out$R2[2] = R2.c
+ out$R2[3] = R2.r
+ final= cbind(final,out)
+}
+
+