###################################################
### chunk number 1: Options
###################################################
options(width=90,scipen=3,digits=4)


###################################################
### chunk number 2: Reading data
###################################################
library(foreign)
FNSEM <- data.frame(read.dta("3685.dta",
     convert.underscore=TRUE))


###################################################
### chunk number 3: Selecting variables
###################################################
FNSEM<-subset(FNSEM,select=c(
     a1an,a1e,a3,a4n,area,ethnic,
     f14a,f14b,f15a,f15bn:f15dn,f16a,f16b1n:f16b3n,
     hh2a.s,hh2c.b,hh2c.c,hh2c.d,hh2c.e,hh2c.f,
          hh2c.g,hh2c.h,hh2c.i,hh2c.j,hh2c.k,
          hh2c.l,hh2c.m,hh5d.s,hh40,
     j1b,j2,j3occ,j55a,j63a,
     q1,q3,question,
     s6,s7,s9,s12a,s12b,s12c,s24a,s34a,s34b,s39,
     v1a,v1b,v1c,v1d,v9a,
     weightis,wown,wunemp,
     year,
     ..q2a1,..q2a2,..q2a3,..q2a4,..q2a5,..q2a6,
          ..q2a7,..q2a8,..q2a9,..q2a10,..q2a11,
          ..q2a12,..q2a13,..q2a14,..q2a15,
          ..q2a16,..q2a17,..q2a18,..q2a19,
          ..q2a20,..q2a21,..q2a22,..q2a23,
          ..q2a24,..q2a25,..q2a26,..q2a27,
          ..q2a28,..q2a29,..q2a30,..q2a31,
          ..q2a32,..q2a33,
     ..s12f1,..s12f2, ..s12f3, ..s12f4, ..s12f5,
             ..s12f6, ..s12f7, ..s12f8, ..s12f9,
             ..s12f10,..s12f11,..s12f12,..s12f13,
             ..s12f14,..s12f15,..s12f16,..s12f17,
             ..s12f18,
     ..s12g1,..s12g2, ..s12g3, ..s12g4, ..s12g5,
             ..s12g6, ..s12g7, ..s12g8, ..s12g9,
             ..s12g10,..s12g11,..s12g12,..s12g13,
             ..s12g14,..s12g15,..s12g16,..s12g17,
             ..s12g18,
     ..s12h1,..s12h2, ..s12h3, ..s12h4, ..s12h5,
             ..s12h6, ..s12h7, ..s12h8, ..s12h9,
             ..s12h10,..s12h11,..s12h12,..s12h13,
             ..s12h14,..s12h15,..s12h16,..s12h17,
             ..s12h18,
     ..s12i1,..s12i2, ..s12i3, ..s12i4, ..s12i5,
             ..s12i6, ..s12i7, ..s12i8, ..s12i9,
             ..s12i10,..s12i11,..s12i12,..s12i13,
             ..s12i14,..s12i15,..s12i16,..s12i17,
             ..s12i18
))


###################################################
### chunk number 4: Ethnic
###################################################
t(table(FNSEM$ethnic,exclude=c()))


###################################################
### chunk number 5: Religious
###################################################
table(FNSEM$s6,exclude=c())


###################################################
### chunk number 6: Singles
###################################################
table(FNSEM$a1e,exclude=c())


###################################################
### chunk number 7: Questionnaire
###################################################
table(FNSEM$question,exclude=c())


###################################################
### chunk number 8: The ethnic minority sample 1
###################################################
# table(U$ethnic,exclude=c())
# table(U$s6,exclude=c())
# table(U$a1e,exclude=c())


###################################################
### chunk number 9: The ethnic minority sample 2
###################################################
FNSEM <- FNSEM[FNSEM$ethnic!="white" &
     !is.na(FNSEM$ethnic),]


###################################################
### chunk number 10: The ethnic minority sample 3
###################################################
write(nrow(FNSEM),file="crossreference1.tex")


###################################################
### chunk number 11: The ethnic minority sample 4
###################################################
FNSEM <- FNSEM[FNSEM$s6!=2,]
FNSEM <- FNSEM[FNSEM$a1e!=3,]
U <- FNSEM <- FNSEM[FNSEM$question==1,]


###################################################
### chunk number 12: The ethnic minority sample 5
###################################################
# table(U$ethnic,exclude=c())
# table(U$s6,exclude=c())
# table(U$a1e,exclude=c())


###################################################
### chunk number 13: Recoding of missing values
###################################################
is.na(U) <- U==-1


###################################################
### chunk number 14: Religious Affiliation 1
###################################################
# table(U$s7,exclude=c())


###################################################
### chunk number 15: Religious Affiliation 2
###################################################
is.na(U$s7) <- U$s7=="na"
U$Religion  <- ifelse(U$s7=="muslim","muslim","non-muslim")


###################################################
### chunk number 16: Religious Affiliation 3
###################################################
# table(U$Religion,exclude=c())


###################################################
### chunk number 17: Importance of religion 1
###################################################
# table(U$s9,exclude=c())


###################################################
### chunk number 18: Importance of religion 2
###################################################
is.na(U$s9) <-  U$s9==8 | U$s9==9
U$Importance.of.Religion <- U$s9 == 4


###################################################
### chunk number 19: Importance of religion 3
###################################################
# table(U$Importance.of.Religion,exclude=c())


###################################################
### chunk number 20: Rowtable function
###################################################
RowTable <- function(x1,x2)  round(cbind(table(x1,x2,exclude=c()),table(x1,exclude=c()))/c(table(x1, exclude=c())),2)


###################################################
### chunk number 21: Religion vs Importance of Religion
###################################################
RowTable(U$s7,U$s9)


###################################################
### chunk number 22: Attitude towards inter-marriage 1
###################################################
# table(U$s34a,exclude=c())
# table(U$s34b,exclude=c())


###################################################
### chunk number 23: Attitude towards inter-marriage 2
###################################################
is.na(U$s34a) <- U$s34a==8 | U$s34a==9
is.na(U$s34b) <- U$s34b==8
U$Attitude.Towards.Inter.Marriage <-
     U$s34a==1 & U$s34b==1


###################################################
### chunk number 24: Attitude towards inter-marriage 3
###################################################
# table(U$Attitude.Towards.Inter.Marriage,exclude=c())


###################################################
### chunk number 25: Attitudes
###################################################
RowTable(U$s7,U$Attitude.Towards.Inter.Marriage)


###################################################
### chunk number 26: Importance of racial composition in schools 1
###################################################
# table(U$s24a,exclude=c())


###################################################
### chunk number 27: Importance of racial composition in schools 2
###################################################
is.na(U$s24a) <- U$s24a==8 | U$s24a==9
U$Importance.of.Racial.Composition.in.Schools <- U$s24a==3


###################################################
### chunk number 28: Importance of racial composition in schools 3
###################################################
# table(U$Importance.of.Racial.Composition.in.Schools,
#      exclude=c())


###################################################
### chunk number 29: Importance
###################################################
RowTable(U$Religion,
     U$Importance.of.Racial.Composition.in.Schools)


###################################################
### chunk number 30: Born in the UK 1
###################################################
#table(U$a3,exclude=c())


###################################################
### chunk number 31: Born in the UK 2
###################################################
is.na(U$a3)<- U$a3==99
U$Born.in.the.UK <- U$a3==16 | U$a3==17 | U$a3==18


###################################################
### chunk number 32: Born in the UK 3
###################################################
#table(U$Born.in.the.UK,exclude=c())


###################################################
### chunk number 33: Age at and years since arrival 1
###################################################
# table(U$a4n,exclude=c())
# table(U$a1an,exclude=c())
# table(U$year,exclude=c())


###################################################
### chunk number 34: Age at and years since arrival 2
###################################################
is.na(U$a4n) <- U$a4n==98 | U$a4n==99
is.na(U$year)<- U$year==99
U$Age <- U$a1an
U$Years.Since.Arrival <- ifelse(U$Born.in.the.UK==TRUE,
     0, U$year-U$a4n)
U$Age.at.Arrival <- U$Age - U$Years.Since.Arrival
U$Age.at.Arrival <- replace(U$Age.at.Arrival,
     U$Born.in.the.UK==TRUE, 0)


###################################################
### chunk number 35: Age at and years since arrival 3
###################################################
# table(U$Years.Since.Arrival,exclude=c())
# table(U$Age.at.Arrival,exclude=c())


###################################################
### chunk number 36: Female 1
###################################################
# table(U$hh2a.s,exclude=c())


###################################################
### chunk number 37: Female 2
###################################################
is.na(U$hh2a.s) <- U$hh2a.s =="na"
U$Female <- U$hh2a=="female"


###################################################
### chunk number 38: Female 3
###################################################
# table(U$Female,exclude=c())


###################################################
### chunk number 39: Arranged marriage 1
###################################################
# table(U$s39,exclude=c())


###################################################
### chunk number 40: Arranged marriage 2
###################################################
is.na(U$s39) <- U$s39==8 | U$s39==9
U$Arranged.Marriage <- ifelse(
     U$s39==3 | U$s39==4 | U$s39==5 |
     U$ethnic=="caribbean", FALSE , TRUE)


###################################################
### chunk number 41: Arranged marriage 3
###################################################
# table(U$Arranged.Marriage,exclude=c())


###################################################
### chunk number 42: Discrimination 1
###################################################
vjlist <- c(paste("v1",letters[1:4],sep=""),
     "v9a","j55a","j63a")


###################################################
### chunk number 43: Discrimination 3
###################################################
is.na(U[vjlist]) <- U[vjlist]==8 |
     U[vjlist]==9
is.na(U$v1b)  <- U$v1b==7 |  U$v1b==8 |
     U$v1b==9
U$Discrimination   <-
     (U$v1a==1 &  U$v1b==1 &  U$v1c==1) |
     (U$v1a==1 &
     (U$v1b >= 2 & U$v1b <= 6)  &
     U$v1d==1) | U$v9a==1 |  U$j55a==1 |
     U$j63a==1


###################################################
### chunk number 44: Discrimination 4
###################################################
# table(U$Discrimination,exclude=c())


###################################################
### chunk number 45: Children 1
###################################################
# table(U$f16a,exclude=c())
# table(U$f16b1n,exclude=c())
# table(U$f16b2n,exclude=c())
# table(U$f16b3n,exclude=c())


###################################################
### chunk number 46: Children 2
###################################################
is.na(U$f16a)    <- U$f16a==8 | U$f16a==9
is.na(U$f16b1n)  <- U$f16b1n==99
is.na(U$f16b2n)  <- U$f16b2n==99
is.na(U$f16b3n)  <- U$f16b3n==98 |
     U$f16b3n==99
U$Child.not.at.Home <- ifelse(U$f16a==1,
     U$f16b1n+U$f16b2n+U$f16b3n,0)


###################################################
### chunk number 47: Children 3
###################################################
hhlist <- c(paste("hh2c.", letters[2:13], sep=""))


###################################################
### chunk number 48: Children 4
###################################################
is.na(U[hhlist]) <- U[hhlist]==98 | U[hhlist]==99
U$Children <-
     U$Child.not.at.Home + apply(apply(subset(U,
     select=c(hh2c.b:hh2c.m)), 2,
     function(x) x==5),1, function(x) sum(x,
     na.rm=TRUE))


###################################################
### chunk number 49: Children 5
###################################################
# table(U$Children,exclude=c())


###################################################
### chunk number 50: No British Education 1
###################################################
# table(U$q1,exclude=c())


###################################################
### chunk number 51: No British Education 2
###################################################
is.na(U$q1) <- U$q1==9
U$No.British.Education <- U$q1==2


###################################################
### chunk number 52: No British Education 3
###################################################
# table(U$No.British.Education,exclude=c())


###################################################
### chunk number 53: British basic education 1
###################################################
q2alist <- c(paste("..q2a", c(1:8,12:18), sep=""))


###################################################
### chunk number 54: British basic education 2
###################################################
# apply(U[q2alist], 2, function(x) table(x,
#      exclude=c()))


###################################################
### chunk number 55: British basic education 3
###################################################
U$British.Basic.Education <-
     apply(apply(U[q2alist] ,2,function(x)
     x==1),1, function(x) sum(x, na.rm=TRUE))!=0
U$British.Basic.Education <- ifelse(
     is.na(U$No.British.Education),
     NA,U$British.Basic.Education)


###################################################
### chunk number 56: British basic education 4
###################################################
# table(U$British.Basic.Education,exclude=c())


###################################################
### chunk number 57: British higher education 1
###################################################
# table(U$..q2a9,exclude=c())
# table(U$..q2a10,exclude=c())
# table(U$..q2a11,exclude=c())


###################################################
### chunk number 58: British higher education 2
###################################################
U$British.Higher.Education <- apply(apply(subset(
     U,select=c(..q2a9:..q2a11,..q2a19,..q2a20))
     ,2,function(x) x==1),1,
     function(x) sum(x, na.rm=TRUE))!=0
U$British.Higher.Education <- ifelse(
     is.na(U$No.British.Education),
     NA,U$British.Higher.Education)


###################################################
### chunk number 59: British higher education 3
###################################################
# table(U$British.Higher.Education,exclude=c())


###################################################
### chunk number 60: Foreign education 1
###################################################
# table(U$q3,exclude=c())


###################################################
### chunk number 61: Foreign education 2
###################################################
is.na(U$q3) <- U$q3==8 | U$q3==9
U$Foreign.Education <- U$q3==1


###################################################
### chunk number 62: Foreign education 3
###################################################
# table(U$Foreign.Education,exclude=c())


###################################################
### chunk number 63: Labour market status 1
###################################################
# table(U$j1b,exclude=c())
# table(U$j3occ,exclude=c())


###################################################
### chunk number 64: Labour market status 2
###################################################
is.na(U$j3occ) <- U$j3occ==10 | U$j3occ==11
U$Labour.Market.Status <- ifelse(U$j1b==1 &
     U$j3occ==9 & !is.na(U$j1b==1 &
     U$j3occ==9), "Employee","NotAssigned")


###################################################
### chunk number 65: Labour market status 3
###################################################
U$Labour.Market.Status <- replace(
     U$Labour.Market.Status,
     (U$j3occ==1 | U$j3occ==2 |
     U$j3occ==3  | U$j3occ==4) &
     U$j1b==1,"SelfEmployed")


###################################################
### chunk number 66: Labour market status 4
###################################################
U$Labour.Market.Status <-
     replace(U$Labour.Market.Status,
     U$j1b==1 & U$j3occ>4 & U$j3occ<9,
     "Manager")


###################################################
### chunk number 67: Labour market status 5
###################################################
U$Labour.Market.Status <-
     replace(U$Labour.Market.Status,
     (U$hh5d.s==6 | U$hh5d.s == 7) &
     U$Labour.Market.Status!="NotAssigned",
     "Unclear")
U$Labour.Market.Status <-
     replace(U$Labour.Market.Status,
     (U$hh5d.s==6 | U$hh5d.s == 7) &
     U$Labour.Market.Status=="NotAssigned",
     "Unemployed")


###################################################
### chunk number 68: Labour market status 6
###################################################
U$Labour.Market.Status <-
     replace(U$Labour.Market.Status,
     (U$hh5d.s==1 | U$hh5d.s == 2 |
     U$hh5d.s==5  | U$hh5d.s == 8 |
     U$hh5d.s==9  | U$hh5d.s == 10|
     U$hh5d.s==11 | U$j1b    == 2 |
     U$j2    == 2 ) &
     U$Labour.Market.Status=="NotAssigned",
     "OutOfLabourForce")


###################################################
### chunk number 69: Labour market status 8
###################################################
is.na(U$Labour.Market.Status) <-
     U$Labour.Market.Status == "NotAssigned"
is.na(U$Labour.Market.Status) <-
     U$Labour.Market.Status == "Unclear"


###################################################
### chunk number 70: Labour market status 9
###################################################
# table(U$Labour.Market.Status,exclude=c())


###################################################
### chunk number 71: No parents 1
###################################################
# table(U$f14a,exclude=c())
# table(U$f14b,exclude=c())


###################################################
### chunk number 72: No parents 2
###################################################
is.na(U$f14a) <- U$f14a==9
is.na(U$f14b) <- U$f14b==9
U$No.Parents   <- U$f14a==8 | U$f14b==2


###################################################
### chunk number 73: No parents 3
###################################################
# table(U$No.Parents,exclude=c())


###################################################
### chunk number 74: Contacts with parents 1
###################################################
# table(U$f15bn,exclude=c())
# table(U$f15cn,exclude=c())
# table(U$f15dn,exclude=c())


###################################################
### chunk number 75: Contacts with parents 2
###################################################
is.na(U$f15bn) <- U$f15bn==997 | U$f15bn==999
is.na(U$f15cn) <- U$f15cn==999
is.na(U$f15dn) <- U$f15dn==999
U$Parents.Physical.Contacts <- ifelse(
     U$f14a!=8,  U$f15bn,0)
U$Parents.Telephone.Calls <- ifelse(
     U$f14a!=8, U$f15cn,0)
U$Parents.Letters <- ifelse(
     U$f14a!=8, U$f15dn,0)


###################################################
### chunk number 76: Contacts with parents 3
###################################################
# table(U$Parents.Physical.Contacts,exclude=c())
# table(U$Parents.Telephone.Calls,exclude=c())
# table(U$Parents.Letters,exclude=c())


###################################################
### chunk number 77: English language 1
###################################################
# table(U$s12a,exclude=c())
# table(U$..s12f1,exclude=c())
# table(U$..s12g1,exclude=c())
# table(U$..s12h1,exclude=c())
# table(U$..s12i1,exclude=c())


###################################################
### chunk number 78: English language 2
###################################################
is.na(U$s12a) <- U$s12a== 8 | U$s12a == 9
s12flist <- c(paste("..s12f", 2:16, sep=""))
U$oOLD <- apply(U[s12flist]=="yes", 1, sum)
U$English.Spoken.at.Home.with.Older <-
     ((U$s12a==1 | is.na(U$s12a)) &
     U$..s12f1=="yes") | U$s12a==2
is.na(U$English.Spoken.at.Home.with.Older) <-
     U$oOLD==0 &
     U$English.Spoken.at.Home.with.Older==FALSE
U$English.Spoken.at.Home.with.Older <-
    replace(U$English.Spoken.at.Home.with.Older,
    U$oOLD>0 &
    is.na(U$English.Spoken.at.Home.with.Older),FALSE)
U$DO.NOT.SPEAK.WITH.OLDER <- ifelse(U$..s12f16=="no",0,1)


###################################################
### chunk number 79: English language 3
###################################################
s12glist <- c(paste("..s12g", 2:16, sep=""))
U$oYOUNG <- apply(U[s12glist]=="yes", 1, sum)
U$English.Spoken.at.Home.with.Younger <-
     ((U$s12a==1 | is.na(U$s12a)) &
     U$..s12g1=="yes") | U$s12a==2
is.na(U$English.Spoken.at.Home.with.Younger) <-
     U$oYOUNG==0 &
     U$English.Spoken.at.Home.with.Younger==FALSE
U$English.Spoken.at.Home.with.Younger <-
     replace(U$English.Spoken.at.Home.with.Younger,
     U$oYOUNG>0 &
     is.na(U$English.Spoken.at.Home.with.Younger),
     FALSE)
U$DO.NOT.SPEAK.WITH.YOUNGER <- ifelse(U$..s12g16=="no",0,1)


###################################################
### chunk number 80: English language 4
###################################################
s12hlist <- c(paste("..s12h", 2:16, sep=""))
U$oWORK <- apply(U[s12hlist]=="yes", 1, sum)
U$English.Spoken.at.Work <-
     ((U$s12a==1 | is.na(U$s12a)) &
     U$..s12h1=="yes") | U$s12a==2
is.na(U$English.Spoken.at.Work) <-
     U$oWORK==0 & U$English.Spoken.at.Work==FALSE
U$English.Spoken.at.Work <-
     replace(U$English.Spoken.at.Work,
     U$oWORK>0 & is.na(U$English.Spoken.at.Work),
     FALSE)
U$DO.NOT.SPEAK.AT.WORK <- ifelse(U$..s12h16=="no",0,1)


###################################################
### chunk number 81: English language 5
###################################################
s12ilist <- c(paste("..s12i", 2:16, sep=""))
U$oFRIENDS <- apply(U[s12ilist]=="yes",1,sum)
U$English.Spoken.With.Friends <-
     ((U$s12a==1 | is.na(U$s12a)) &
     U$..s12i1=="yes") | U$s12a==2
is.na(U$English.Spoken.With.Friends) <-
     U$oFRIENDS==0 &
     U$English.Spoken.With.Friends==FALSE
U$English.Spoken.With.Friends <-
     replace(U$English.Spoken.With.Friends,
     U$oFRIENDS>0 &
     is.na(U$English.Spoken.With.Friends),  FALSE)
U$DO.NOT.SPEAK.WITH.FRIENDS <- ifelse(U$..s12i16=="no",0,1)


###################################################
### chunk number 82: English language 6
###################################################
# table(U$English.Spoken.at.Home.with.Older,
#     exclude=c())
# table(U$English.Spoken.at.Home.with.Younger,
#     exclude=c())
# table(U$English.Spoken.at.Work,exclude=c())
# table(U$English.Spoken.With.Friends,exclude=c())


###################################################
### chunk number 83: Household income 1
###################################################
# table(U$hh40,exclude=c())


###################################################
### chunk number 84: Household income 2
###################################################
is.na(U$hh40) <- {U$hh40=="refused" |
     U$hh40=="can't say"  | U$hh40=="na"}
U$Household.Income <- c(38.5,96.5,135.0,
     173.5,230.5,260.0,318.0,366.0,414.0,
     471.5,529.5,587.0,649.5,702.0,759.5,
     817.5)[U$hh40]


###################################################
### chunk number 85: Household income 3
###################################################
# table(U$Household.Income,exclude=c())


###################################################
### chunk number 86: Ward variables 1
###################################################
# table(U$wown,exclude=c())


###################################################
### chunk number 87: Ward variables 2
###################################################
U$Ward.Density.Own.Ethnicity <- (c(1.0,3.5,
     7.5,12.5,20.0,29.0,37.0)[U$wown])/100
U$Ward.Unemployment.Rate <- c(1.0,3.5,7.5,12.5,
     17.5,22.5)[U$wunemp]


###################################################
### chunk number 88: Ward variables 3
###################################################
# table(U$Ward.Density.Own.Ethnicity,exclude=c())
# table(U$Ward.Unemployment.Rate,exclude=c())


###################################################
### chunk number 89: Another variable definition
###################################################
GroupDiscrimination <-
     tapply(U$Discrimination,U$ethnic,
              function(x) mean(x, na.rm=TRUE))
U$Discrimination.Own.Ethnicity <-
     GroupDiscrimination[U$ethnic]


###################################################
### chunk number 90: Defining dummies for Labour Market Status
###################################################
U$Employee <- as.numeric(U$Labour.Market.Status=="Employee")
U$Manager <- as.numeric(U$Labour.Market.Status=="Manager")
U$Self.Employed <-
      as.numeric(U$Labour.Market.Status=="SelfEmployed")
U$OUT.OF.LABOUR.FORCE <-
      as.numeric(U$Labour.Market.Status=="OutOfLabourForce")
U$Unemployed <-
      as.numeric(U$Labour.Market.Status=="Unemployed")


###################################################
### chunk number 91: Defining the subset 1
###################################################
U.Original <- U


###################################################
### chunk number 92: Defining the subset 2
###################################################
U <- subset(U, select=c(
    Religion,
    s7,
    ethnic,
    Importance.of.Religion,
    Attitude.Towards.Inter.Marriage,
    Importance.of.Racial.Composition.in.Schools,
    Age.at.Arrival,
    Age,
    Female,
    Born.in.the.UK,
    Arranged.Marriage,
    Discrimination,
    Children,
    Years.Since.Arrival,
    No.British.Education,
    British.Basic.Education,
    British.Higher.Education,
    Foreign.Education,
    Employee,
    Manager,
    Self.Employed,
    OUT.OF.LABOUR.FORCE,
    Unemployed,
    No.Parents,
    Parents.Physical.Contacts,
    Parents.Telephone.Calls,
    Parents.Letters,
    English.Spoken.at.Home.with.Older,
    DO.NOT.SPEAK.WITH.OLDER,
    English.Spoken.at.Home.with.Younger,
    DO.NOT.SPEAK.WITH.YOUNGER,
    English.Spoken.at.Work,
    DO.NOT.SPEAK.AT.WORK,
    English.Spoken.With.Friends,
    DO.NOT.SPEAK.WITH.FRIENDS,
    Household.Income,
    Ward.Density.Own.Ethnicity,
    Ward.Unemployment.Rate,
    Discrimination.Own.Ethnicity,
    area,
    weightis))


###################################################
### chunk number 93: Defining the subset 3
###################################################
U <- na.omit(U)


###################################################
### chunk number 94: Defining models for regressions
###################################################
# Defining the regression model for
# Importance of Religion
# DO.NOT.SPEAK.WITH.YOUNGER, removed,
# no observations for Muslims.
# only two observations for non-Muslims
# table(U$DO.NOT.SPEAK.WITH.YOUNGER, U$Religion)

model1 <- {formula(Importance.of.Religion ~
    Age.at.Arrival + Female + Born.in.the.UK +
    Arranged.Marriage + Discrimination +
    Children + Years.Since.Arrival +
    No.British.Education + British.Basic.Education +
    British.Higher.Education + Foreign.Education +
    Employee+ Manager + Self.Employed + Unemployed+
    No.Parents + Parents.Physical.Contacts +
    Parents.Telephone.Calls + Parents.Letters +
    English.Spoken.at.Home.with.Older +
    English.Spoken.at.Home.with.Younger +
    English.Spoken.at.Work +
    English.Spoken.With.Friends +
    Household.Income +
    Discrimination.Own.Ethnicity +
    Ward.Density.Own.Ethnicity +
    Ward.Unemployment.Rate + area +
    factor(DO.NOT.SPEAK.WITH.OLDER)+
    factor(DO.NOT.SPEAK.AT.WORK) +
    factor(DO.NOT.SPEAK.WITH.FRIENDS))}


# Defining the model using the same regressors as
# in Model 1 and Attitude.Towards.Inter.Marriage
# as dependent variable
model2 <- {update(model1,
     Attitude.Towards.Inter.Marriage ~ .)}

# Defining the model using the same regressors as
# in Model 1 and Importance.of.Racial.Composition.in.Schools
# as dependent variable
model3 <- {update(model2,
     Importance.of.Racial.Composition.in.Schools ~ .)}


###################################################
### chunk number 95: Regressions
###################################################
library(sandwich,warn.conflicts=FALSE)
library(lmtest,warn.conflicts=FALSE)
Umuslim  <- U[U$Religion=="muslim",]
UNmuslim <- U[U$Religion!="muslim",]

ESTIMATE <- function(x,df)
       {fm <- lm(x, weights=weightis, data=df)
        rbind(coeftest(fm,
               vcov = vcovHC(fm, type ="HC1")),
                       summary(fm)$adj.r.squared)}

result1.muslim <- ESTIMATE(model1, Umuslim)
result2.muslim <- ESTIMATE(model2, Umuslim)
result3.muslim <- ESTIMATE(model3, Umuslim)


result1.non.muslim <- ESTIMATE(model1, UNmuslim)
result2.non.muslim <- ESTIMATE(model2, UNmuslim)
result3.non.muslim <- ESTIMATE(model3, UNmuslim)



###################################################
### chunk number 96: Loading xtable
###################################################
library(xtable)


###################################################
### chunk number 97: Table 1
###################################################
# Defining variables to use in the table
OnonMuslim        <- table(FNSEM$s7!="muslim")[2]
names(OnonMuslim) <- "nonMuslim"
SnonMuslim        <- table(U$Religion!="muslim")[2]
names(SnonMuslim) <- "nonMuslim"
# Defining the table header
Header <- paste("Religious affiliation",
"&\\multicolumn{1}{c}{(1)}",
     "&\\multicolumn{1}{c}{(2)}",
     "&\\multicolumn{1}{c}{(3)}",
     "&\\multicolumn{1}{c}{(4)}",
     "&\\multicolumn{1}{c}{(5)}",
     "&\\multicolumn{1}{c}{(6)}",
     "\\\\\\hline",
     "&\\multicolumn{2}{c}{Before}",
     "&\\multicolumn{2}{c}{After}",
     "&\\multicolumn{2}{c}{Bisin et al.}",
     "\\\\",
     "&\\multicolumn{2}{c}{$n=",length(U.Original$s7),"$}",
     "&\\multicolumn{2}{c}{$n=",length(U$s7),"$}",
     "&\\multicolumn{2}{c}{$n=5963$}",
     "\\\\\\cline{2-7}",
     "&\\multicolumn{1}{r}{\\#}&\\multicolumn{1}{r}{\\%}",
     "&\\multicolumn{1}{r}{\\#}&\\multicolumn{1}{r}{\\%}",
     "&\\multicolumn{1}{r}{\\#}&\\multicolumn{1}{r}{\\%}\\\\",
     sep="")
# Defining table footer
Footer <- paste("\\hline All non-Muslims",
      "&",OnonMuslim,"&",
      round(100*OnonMuslim/table(FNSEM$s7=="na")[[1]],2),
      "&",SnonMuslim,"&",
      round(100*SnonMuslim/length(U$Religion),2),
      "&3594&",round(100*3594/(3594+2369),2),
      "\\\\\\hline",
      "\\multicolumn{7}{l}{\\multirow{2}{10.5cm}",
      "{\\footnotesize NOTE: The row names shows exactly ",
      "how the original data is coded, so that e.g., ",
      "`NA's' are true missing values whereas `na' is ",
      "coded as religious affiliation `na'. On the last ",
      "line non--Muslims are calculated excluding na and ",
      "NA.}}\\\\",
       sep="")
addtorow <- list()
addtorow$pos <- list()
addtorow$pos[[1]] <- 0
addtorow$pos[[2]] <- 13
addtorow$command <- c(Header,Footer)
# Defining the data to use in the table
DiffSample <- cbind(
     summary(FNSEM$s7),
     100*summary(U.Original$s7)/length(U.Original$s7),
     c(summary(U$s7),
     length(U$s7)-table(is.na(U$s7))[[1]]),
     c(100*summary(U$s7)/length(U$s7),
     100*(length(U$s7)-table(is.na(U$s7))[[1]])),
     matrix(list(c(),c(),round(2369,0),c(),c(),c(),
     c(),c(),c(),c(),c(),c(),c())),
     matrix(list(c(),c(),39.73,c(),c(),c(),c(),c(),
     c(),c(),c(),c(),c()))
     )
# Taking dimnames from original data
dimnames(DiffSample)[[1]] <- names(summary(U.Original$s7))
# Generate the table
TabDiffSample <- print(xtable(DiffSample,align=c("lrrrrrr"),
     caption=c("Religious affiliation (absolute (\\#) and
     relative (\\%) numbers), before (columns 1 and 2) and
     after (columns 3 and 4) removal of \\code{NA} compared
     with \\citet{Bisin08} (columns 5 and 6)."),
     label="ta:diffsample",
     digits=c(0,0,2,0,2,0,2)),
     table.placement = "h",
     caption.placement="top",
     include.colnames=FALSE,
     add.to.row=addtorow,
     hline.after=c(-1,0,11))


###################################################
### chunk number 98: Table 2-3
###################################################
# Generate sample with all NA removed
SampleU <- subset(U,select=c(
     Importance.of.Religion,
     Attitude.Towards.Inter.Marriage,
     Importance.of.Racial.Composition.in.Schools,
     Age.at.Arrival,
     Age,
     Female,
     Born.in.the.UK,
     Arranged.Marriage,
     Discrimination,
     Children,
     Years.Since.Arrival,
     No.British.Education,
     British.Basic.Education,
     British.Higher.Education,
     Foreign.Education,
     Employee,
     Manager,
     Self.Employed,
     OUT.OF.LABOUR.FORCE,
     Unemployed,
     No.Parents,
     Parents.Physical.Contacts,
     Parents.Telephone.Calls,
     Parents.Letters,
     English.Spoken.at.Home.with.Older,
     DO.NOT.SPEAK.WITH.OLDER,
     English.Spoken.at.Home.with.Younger,
     DO.NOT.SPEAK.WITH.YOUNGER,
     English.Spoken.at.Work,
     DO.NOT.SPEAK.AT.WORK,
     English.Spoken.With.Friends,
     DO.NOT.SPEAK.WITH.FRIENDS,
     Household.Income,
     Ward.Density.Own.Ethnicity,
     Ward.Unemployment.Rate,
     Discrimination.Own.Ethnicity
))
# Generate sample with no NA removed
SampleU.Original <- subset(U.Original,select=c(
     Importance.of.Religion,
     Attitude.Towards.Inter.Marriage,
     Importance.of.Racial.Composition.in.Schools,
     Age.at.Arrival,
     Age,
     Female,
     Born.in.the.UK,
     Arranged.Marriage,
     Discrimination,
     Children,
     Years.Since.Arrival,
     No.British.Education,
     British.Basic.Education,
     British.Higher.Education,
     Foreign.Education,
     Employee,
     Manager,
     Self.Employed,
     OUT.OF.LABOUR.FORCE,
     Unemployed,
     No.Parents,
     Parents.Physical.Contacts,
     Parents.Telephone.Calls,
     Parents.Letters,
     English.Spoken.at.Home.with.Older,
     DO.NOT.SPEAK.WITH.OLDER,
     English.Spoken.at.Home.with.Younger,
     DO.NOT.SPEAK.WITH.YOUNGER,
     English.Spoken.at.Work,
     DO.NOT.SPEAK.AT.WORK,
     English.Spoken.With.Friends,
     DO.NOT.SPEAK.WITH.FRIENDS,
     Household.Income,
     Ward.Density.Own.Ethnicity,
     Ward.Unemployment.Rate,
     Discrimination.Own.Ethnicity
))

# Set Age.at.Arrival and Years.Since.Arrival
# for those Born.in.the.UK to NA. This is only for the
# sample statistics.


is.na(SampleU.Original$Age.at.Arrival)      <-
                       SampleU.Original$Born.in.the.UK==1
is.na(SampleU.Original$Years.Since.Arrival) <-
                       SampleU.Original$Born.in.the.UK==1


# A function to compute weighted mean
# for a variable in a data set (x), with
# weights (w) and column (n).
WMEAN <- function (x,W,n) {
     weighted.mean(x[!is.na(x[,n]),n], W[!is.na(x[, n])])}


# Weights to use in computing weighted means for Muslims
Muslim.weight <-
     U$weightis[U$Religion=="muslim"]
Orig.Muslim.weight <-
     U.Original$weightis[U.Original$Religion=="muslim"]
Non.Muslim.weight <-
     U$weightis[U$Religion=="non-muslim"]
Orig.Non.Muslim.weight <-
     U.Original$weightis[U.Original$Religion=="non-muslim"]


# Create a matrix to gather means
# Columns 1:5 for Muslims:
# 1:2 weighted means, 3:4 unweighted, 5 Bisin et al.
# Columns 6:10 for non-Muslims:
# 6:7 weighted means, 8:9 unweighted, 10 Bisin et al.

Means.Table <- matrix(,ncol(SampleU),10)
rownames(Means.Table) <- colnames(SampleU)

# Compute weighted means in columns 1:36
for (i in 1:ncol(SampleU.Original)) {Means.Table[i,1]<-
     WMEAN(SampleU.Original[U.Original$Religion=="muslim",],
                                      Orig.Muslim.weight, i)}

# Compute weighted means in columns 1:36
for (i in 1:ncol(SampleU)) {Means.Table[i,2]<-
     WMEAN(SampleU[U$Religion=="muslim",] , Muslim.weight, i)}



# Unweighted Means for Muslims
Means.Table[,3] <-
     mean(SampleU.Original[U.Original$Religion=="muslim",],
     na.rm=TRUE)
Means.Table[,4] <-
     mean(SampleU[U$Religion=="muslim",], na.rm=TRUE)


# Bisin et al. Muslims
Means.Table[,5]  <-
c(0.79,0.70,0.65,39.18, NA, 0.47,0.21,0.22,0.17,2.17,
     26.43,0.81,0.06,0.08,0.25,0.38,0.02,0.09,NA,0.19,
     0.34,3.05,3.38,0.67,0.03,NA,0.20,NA,0.19,NA,0.22,
     NA,200.74,0.15,16.57,0.21)


# Compute weighted means SampleU.Original
for (i in 1:ncol(SampleU.Original)) {Means.Table[i,6]<-
     WMEAN(SampleU.Original[U.Original$Religion=="non-muslim",],
     Orig.Non.Muslim.weight, i)}

# Compute weighted means SampleU
for (i in 1:ncol(SampleU)) {Means.Table[i,7]<-
     WMEAN(SampleU[U$Religion=="non-muslim",],
     Non.Muslim.weight, i)}


# Unweighted Means for non-Muslims
Means.Table[,8] <-
     mean(SampleU.Original[U.Original$Religion=="non-muslim",],
         na.rm=TRUE)
Means.Table[,9] <-
     mean(SampleU[U$Religion=="non-muslim",],na.rm=TRUE)



# Bisin et al. non-Muslims
Means.Table[,10]  <-
c(0.42,0.37,0.33,42.57,NA,0.48,0.28,0.12,0.19,1.68,
     27.08,0.52,0.13,0.16,0.29,0.59,0.04,0.14,NA,0.08,
     0.32,3.87,4.74,0.37,0.08,NA,0.25,NA,0.27,NA,0.27,
     NA,330.26,0.11,12.60,0.18)



# Replace "." with " " in variable names to appear in the table
rownames(Means.Table) <- gsub("\\." , " ", rownames(Means.Table))



# Creating the table i LaTeX code
print(xtable(Means.Table[1:17,],align=c("lrrrrrrrrrr"),
     caption="Weighted and Unweighted Means for Muslims and
     non--Muslims before and after removal of \\code{NA}
     compared with \\citet{Bisin08}.",
     label="tab:descriptive"),
     table.placement = "p",
     floating.environment = "sidewaystable",
     caption.placement="top",
     add.to.row=list(pos=list(0),
     command=paste(
     "&\\multicolumn{5}{c}{Muslim}",
     "&\\multicolumn{5}{c}{Non-Muslim}\\\\\\hline",
     "&\\multicolumn{2}{c}{W e i g h t e d}",
     "&\\multicolumn{3}{c}{U n w e i g h t e d}",
     "&\\multicolumn{2}{c}{W e i g h t e d}",
     "&\\multicolumn{3}{c}{U n w e i g h t e d}\\\\",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Bisin}",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Bisin}\\\\",
     "&&&&&et al.&&&&&et al.\\\\\\hline",
     sep="")),
     include.colnames=FALSE,
     hline.after=c(-1,0,NULL))
# Table 3: Continuation of Table 2
print(xtable(Means.Table[18:nrow(Means.Table),],
     align=c("lrrrrrrrrrr"),
     caption="Table 2 continued. Weighted and Unweighted
     Means for Muslims and non--Muslims before and after
     removal of \\code{NA} compared with \\citet{Bisin08}.",
     label="tab:descriptive2"),
     table.placement = "p",
     floating.environment = "sidewaystable",
     caption.placement="top",
     add.to.row=list(pos=list(0),
     command=paste(
     "&\\multicolumn{5}{c}{Muslim}",
     "&\\multicolumn{5}{c}{Non-Muslim}\\\\\\hline",
     "&\\multicolumn{2}{c}{W e i g h t e d}",
     "&\\multicolumn{3}{c}{U n w e i g h t e d}",
     "&\\multicolumn{2}{c}{W e i g h t e d}",
     "&\\multicolumn{3}{c}{U n w e i g h t e d}\\\\",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Bisin}",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Before}",
     "&\\multicolumn{1}{c}{After}",
     "&\\multicolumn{1}{c}{Bisin}\\\\",
     "&&&&&et al.&&&&&et al.\\\\\\hline",
     sep="")),
     include.colnames=FALSE,
     hline.after=c(-1,0,NULL))


###################################################
### chunk number 99: Table 4 prepare
###################################################
MakeTable <- function(x, n=2) {
    # take coef() objects and makes table
    Stars <- ifelse(x[,4]<0.05, paste("*"), paste(""))
    names(Stars) <- NULL
    Estimates  <- format(round(x[,1],n))
    Std.errors <- format(round(x[,2],n))
    x <- cbind(Estimates,Std.errors, Stars)
    Table <- matrix(,2*nrow(x),1)
    Table[seq(1,2*nrow(x)-1,2),1] <-
    paste(x[,1],x[,3], sep="")
    Table[seq(2,2*nrow(x),2  ),1] <-
    paste("(",x[,2],")",sep="")
    Xname  <- unlist(strsplit(unlist(strsplit(rownames(x),
    "TRUE")), "yes") )
    Xname  <- gsub("\\.", " ", Xname)
    Xnames <- matrix(,2*nrow(x),1)
    Xnames[seq(1,2*nrow(x)-1,2),1] <- Xname
    Xnames[seq(2,2*nrow(x)  ,2),1] <- ""
    strsplit(Xnames, "TRUE")
    strsplit(Xnames, "yes")
    rownames(Table) <- Xnames
    Table
    }


Table.Reg <-
    cbind(
    rbind(MakeTable(result1.muslim    [1:28,]),
          format(round(result1.muslim[39,1],2), nsmall=2)),
    rbind(MakeTable(result1.non.muslim[1:28,]),
          round(result1.non.muslim[40,1],2)),
    rbind(MakeTable(result2.muslim    [1:28,]),
          round(result2.muslim[39,1],2)),
    rbind(MakeTable(result2.non.muslim[1:28,]),
          round(result2.non.muslim[40,1],2)),
    rbind(MakeTable(result3.muslim    [1:28,]),
          round(result3.muslim[39,1],2)),
    rbind(MakeTable(result3.non.muslim[1:28,]),
          round(result3.non.muslim[40,1],2))
    )



###################################################
### chunk number 100: Print Table 4
###################################################


rownames(Table.Reg)[nrow(Table.Reg)] <- "Adjusted R-square"
varnames <- rownames(Table.Reg)
rownames(Table.Reg) <- NULL
Table.Reg.dat <- data.frame(varnames, Table.Reg)
print(xtable(Table.Reg.dat[1:28,],align=c("llcccccc"),
     caption=paste("Regression Results for Muslims",
     nrow(Umuslim),"and non-Muslims", nrow(UNmuslim),
     "to be compared with Table 2 in \\citet{Bisin08}. ",
     "Heteroskedasticity corrected (HC1) Standard Errors ",
     "are in parentheses. P-values $< 0.05$ are ",
     "marked with *.",sep=" "),
     label="ta:regressions"),
     table.placement = "p",
     floating.environment = "sidewaystable",
     caption.placement="top",
     include.rownames=FALSE,
     include.colnames=FALSE,
     add.to.row=list(pos=list(0),
     command=paste(
     "&\\multicolumn{2}{c}{Importance of}",
     "&\\multicolumn{2}{c}{Inter Ethnic }",
     "&\\multicolumn{2}{c}{Ethnic Composition}\\\\",
     "&\\multicolumn{2}{c}{Religion}",
     "&\\multicolumn{2}{c}{Marriage}",
     "&\\multicolumn{2}{c}{of Schools}",
     "\\\\\\cline{2-7}\\\\",
     "&\\multicolumn{1}{c}{Muslims}",
     "&\\multicolumn{1}{c}{non--Muslims}",
     "&\\multicolumn{1}{c}{Muslims}",
     "&\\multicolumn{1}{c}{non--Muslims}",
     "&\\multicolumn{1}{c}{Muslims}",
     "&\\multicolumn{1}{c}{non--Muslims}\\\\\\hline",
     sep=""))
     ,hline.after=c(-1,0,NULL))
# Table 5: Continuation of Table 4
Footer <- paste("\\hline",
      "\\multicolumn{7}{l}{\\multirow{3}{20cm}",
      "{\\footnotesize NOTE: All estimated models include ",
      "7 UK-region dummies, and ",
      "the variables \\code{DO.NOT.SPEAK.WITH.OLDER}, ",
      "\\code{DO.NOT.SPEAK.AT.WORK}, and ",
      "\\code{DO.NOT.SPEAK.WITH.FRIENDS}.  It turned out ",
      "that the variable \\code{DO.NOT.SPEAK.WITH.YOUNGER} ",
      "is TRUE for few observations and cannot be ",
      "included in the model.}}\\\\",
      sep="")


Header <- paste(
      "&\\multicolumn{2}{c}{Importance of}",
      "&\\multicolumn{2}{c}{Inter Ethnic }",
      "&\\multicolumn{2}{c}{Ethnic Composition}\\\\",
      "&\\multicolumn{2}{c}{Religion}",
      "&\\multicolumn{2}{c}{Marriage}",
      "&\\multicolumn{2}{c}{of Schools}",
      "\\\\\\cline{2-7}\\\\",
      "&\\multicolumn{1}{c}{Muslims}",
      "&\\multicolumn{1}{c}{non--Muslims}",
      "&\\multicolumn{1}{c}{Muslims}",
      "&\\multicolumn{1}{c}{non--Muslims}",
      "&\\multicolumn{1}{c}{Muslims}",
      "&\\multicolumn{1}{c}{non--Muslims}\\\\\\hline",
      sep="")


print(xtable(Table.Reg.dat[29:57,],align=c("llcccccc"),
     caption=paste("Table 4 continued. Regression Results ",
     "for Muslims", nrow(Umuslim)," and non-Muslims",
     nrow(UNmuslim), "to be compared with Table 2 in ",
     "\\citet{Bisin08}. Heteroskedasticity corrected ",
     "(HC1) Standard Errors are in parentheses. P-values ",
     "$< 0.05$ are marked with *.", sep=" "),
      label="ta:regressions2"),
      table.placement = "p",
      floating.environment = "sidewaystable",
      caption.placement="top",
      include.rownames=FALSE,
      include.colnames=FALSE,
      add.to.row=list(pos=list(0,29),
      command=c(Header,Footer))
      ,hline.after=c(-1,0,NULL))


###################################################
### chunk number 101: Cross-references
###################################################
RelativeSampleSize <-round(100*nrow(U)/nrow(FNSEM),
     digits=0)
BisinRelativeSampleSize <- round(100*5963/nrow(FNSEM),
     digits=0)
write(BisinRelativeSampleSize,file="crossreference0.tex")
write(100-RelativeSampleSize,file="crossreference2.tex")
write(nrow(U.Original),file="crossreference3.tex")
write(nrow(U),file="crossreference4.tex")



###################################################
### chunk number 102: LaTeX
###################################################
write(paste(
     "\\input docstrip.tex",
     "\\keepsilent",
     "\\askforoverwritefalse",
     "\\nopreamble",
     "\\nopostamble",
     "\\generate{",
     "\\file{araietal_paper_bis.tex}{",
     "     \\from{araietal_source_bis.tex}{paper}}",
     "\\file{araietal_techreport_bis.tex}{",
     "     \\from{araietal_source_bis.tex}{techreport}}",
     "\\file{araietal_present_bis.tex}{",
     "     \\from{araietal_source_bis.tex}{present}}",
     "}",
     "\\endbatchfile",
     "\n",sep="\n"),file="araietal_bis.ins")


###################################################
### chunk number 103: BibTex
###################################################
write(paste(
"@Manual{FNSEM,",
"author       = {FNSEM},",
"title        = {P1312 Fourth National Study of Etnic Minorities. {P}roject instructions},",
"note         = {Project 3685},",
"organization = {Social and Community Planning Research}, ",
"address      = {London},",
"year         = {1993},",
"url          = {http://www.data-archive.ac.uk/doc/3685/mrdoc/pdf/a3685uab.pdf}",
"}",
"\n",
"@Manual{FNSEM93b,",
"author       = {FNSEM},",
"title        = {UK Data Archive Data Dictionary}, ",
"note         = {An RTF file called \\url{UKDA-3685-tab/mrdoc/allissue/3685_UKDA_Data_Dictionary.rtf} available when the FNSEM data set is downloaded from the UK Data Archive}, ",
"organization = {Social andCommunity Planning Research}, ",
"address      = {London},",
"year         = {1993}",
"}",
"\n",
"@Article{ Bisin08,",
"author       = {Alberto Bisin and Eleonora Patacchini and Thierry Verdier and Yves Zenou},",
"title        = {Are Muslim Immigrants Different in Terms of Cultural Integration?},",
"journal      = {Journal of the European Economic Association},",
"pages        = {445--456},",
"volume       = {6},",
"year         = {2008}",
"}",
"\n",
"@Manual{Rcore,",
"title        = {R: A Language and Environment for Statistical Computing},",
"author       = {{R Development Core Team}},",
"organization = {R Foundation for Statistical Computing},",
"address      = {Vienna, Austria},",
"year         = {2008},",
"note         = {{ISBN} 3-900051-07-0},",
"url          = {http://www.R-project.org}",
"}",
"\n",
"@Unpublished{Koenker07,",
"author      = {Koenker, Roger and Zeileis, Achim},",
"year        = {2007},",
"title       = {Reproducible Econometric Research (A Critical Review of the State of the Art)},",
"note        = {Report 60, Department of Statistics and Mathematics, Wirtschaftsuniversit�t Wien, Research Report Series},",
"url         = {http:epub.wu-wien.ac.at/dyn/virlib/wp/eng/mediate/epub-wu-01_c75.pdf?ID=epub-wu-01_c75}",
"}",
"\n",
"@Unpublished{Araietal08a,",
"author      = {Arai, Mahmood and Karlsson, Jonas and Lundholm, Michael},",
"year        = {2009},",
"title       = {On Fragile Grounds: A replication of \\emph{Are Muslim immigrants different in terms of cultural
integration?}},",
"note        = {Under publication in the Journal of the European Economic Association}",
"}",
"\n",
"@Unpublished{Araietal08b,",
"author      = {Arai, Mahmood and Karlsson, Jonas and Lundholm, Michael},",
"year        = {2009},",
"title       = {On Fragile Grounds: {A} replication of \\emph{Are Muslim immigrants different in terms of cultural integration? {T}echnical documentation}},",
"note        = {Unpublished manuscript, Department of Economics and SULCIS, Stockholm University},",
"url         = {http://people.su.se/~lundh/fragile_grounds/",
"}",
"\n",
"@Unpublished{Leisch02,",
"author      = {Friedrich Leisch},",
"title       = {Sweave User Manual},",
"year        = {2002},",
"url         = {http://www.ci.tuwien.ac.at/~leisch/Sweave/}",
"}",
"\n",
"@Manual{Berthoud,",
"title       = {Fourth National Survey of Ethnic Minorities, 1993-1994 [computer file]},",
"author      = {Berthoud, R.G. and Modood, T. and Smith, P. and Prior, G.},",
"publisher   = {UK Data Archive [distributor]},",
"address     = {Colchester, Essex: UK},",
"year        = {1997},",
"note        = {SN: 3685},",
"url         = {http://www.data-archive.ac.uk/doc/3685%5Cmrdoc%5CUKDA%5CUKDA_Study_3685_Information.htm}",
"}",
sep="\n"),file="araietal.bib")


###################################################
### chunk number 104: Extracting R code
###################################################
Stangle("araietal_source_bis.Rnw")


###################################################
### chunk number 105: Script to make LaTeX files
###################################################
write(paste(
"system(\"latex araietal_bis.ins\")",
"system(\"pdflatex araietal_techreport_bis.tex\")",
"system(\"bibtex araietal_techreport_bis\")",
"system(\"pdflatex araietal_techreport_bis.tex\")",
"system(\"pdflatex araietal_techreport_bis.tex\")",
"system(\"latex araietal_paper_bis.tex\")",
"system(\"bibtex araietal_paper_bis\")",
"system(\"latex araietal_paper_bis.tex\")",
"system(\"latex araietal_paper_bis.tex\")",
"system(\"dvips araietal_paper_bis.dvi\")",
"system(\"ps2pdf araietal_paper_bis.ps\")",
"system(\"latex araietal_present_bis.tex\")",
"system(\"dvips -o araietal_present1.ps -pp 1:14 araietal_present_bis.dvi\")",
"system(\"dvips -o araietal_present2.ps -pp 11:15 -x 1095 araietal_paper_bis.dvi\")",
"system(\"dvips -o araietal_present3.ps -p 20 araietal_present_bis.dvi\")",
"system(\"gs -q -r2400 -dNOPAUSE -dBATCH -sDEVICE=pdfwrite -sOutputFile=araietal_present_bis.pdf -f  araietal_present1.ps araietal_present2.ps araietal_present3.ps\")",
"system(\"pdflatex araietal_paper_bis.tex\")",
sep="\n"),file="script.R")