EduMod 2: 1-Make the imputed datasets
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[edit] 1 Background
Return to EduMod-2: polygyny Imputation and Regression
[edit] 2 Instructions
Edits in the program to make polygyny the dependent variables are ###ed
###You edit this new wiki page by copying ###the needed files for program 1 into the this page ###then add any new links ###commenting what you did ###and comment out any replaced commands
[edit] 3 Results
These results were not well specified for dependent variable "polygyny v860"
bbb
coef Fstat ddf pvalue VIF
(Intercept) -0.907 0.460 6023439.7 0.498 NA
fyll 0.396 0.614 3913513.4 0.433 2.013
fydd 0.879 23.100 813528.1 0.000 1.905
settype 0.001 0.000 125505780.0 0.982 1.688
cultints -0.060 0.745 18139926.0 0.388 1.935
roots 0.123 0.264 7104232.7 0.608 1.229
foodtrade -0.010 1.226 22770826.2 0.268 1.053
exogamy 0.002 0.001 4661104.6 0.975 1.086
femsubs 0.095 2.183 3923392.3 0.140 1.271
fish -0.092 2.922 13276483.2 0.087 1.262
r2
R2:final model R2:IV(distance) R2:IV(language)
0.3096654 0.9784302 0.9737484
ccc
Fstat df pvalue
RESET 0.295 165748.813 0.587
F on restrs. 6.677 722.943 0.010
NCV 0.070 3072704.599 0.792
SWnormal 6.944 1369626.131 0.008
lagll 0.687 5091825.628 0.407
lagdd 2.948 74398.731 0.086
A new set of independent variables are needed.
[edit] 4 Program 1 (modified)
#Program 1 #Program 2 Estimate model, combine results #Click image to enlarge#
[edit] Program 1 (9-19-2009) obsolete
#MI--make the imputed datasets
#--change the following path to the directory with your data and program--
#setwd("d:/projects/MI")
setwd("c:/My Documents/MI")
rm(list=ls(all=TRUE))
options(echo=TRUE)
#--you need the following two packages--you must install them first--
library(foreign)
library(mice)
#--To find the citation for a package, use this function:---
citation("mice")
#-----------------------------
#--Read in data, rearrange----
#-----------------------------
#--Read in auxilliary variables---
load("vaux.Rdata",.GlobalEnv)
row.names(vaux)<-NULL
#--Read in the SCCS dataset---
load("SCCS.Rdata",.GlobalEnv)
#--look at first 6 rows of vaux--
head(vaux)
#--look at field names of vaux--
names(vaux)
#--check to see that rows are properly aligned in the two datasets--
#--sum should equal 186---
sum((SCCS$socname==vaux$socname)*1)
#--remove the society name field--
vaux<-vaux[,-28]
names(vaux)
#--Two nominal variables: brg and rlg----
#--brg: consolidated Burton Regions-----
#0 = (rest of world) circumpolar, South and Meso-America, west North America
#1 = Subsaharan Africa
#2 = Middle Old World
#3 = Southeast Asia, Insular Pacific, Sahul
#4 = Eastern Americas
#--rlg: Religion---
#'0 (no world religion)'
#'1 (Christianity)'
#'2 (Islam)'
#'3 (Hindu/Buddhist)'
#--check to see number of missing values in vaux,
#--whether variables are numeric,
#--and number of discrete values for each variable---
vvn<-names(vaux)
pp<-NULL
for (i in 1:length(vvn)){
nmiss<-length(which(is.na(vaux[,vvn[i]])))
numeric<-is.numeric(vaux[,vvn[i]])
numDiscrVals<-length(table(vaux[,vvn[i]]))
pp<-rbind(pp,cbind(data.frame(numeric),nmiss,numDiscrVals))
}
row.names(pp)<-vvn
pp
#--extract variables to be used from SCCS, put in dataframe fx--
###dateobs=SCCS$v838, #ADDED BY DRW
fx<-data.frame(
socname=SCCS$socname,socID=SCCS$"sccs#",
polygyny=SCCS$v860, ##DRW:NEW
valchild=(SCCS$v473+SCCS$v474+SCCS$v475+SCCS$v476),
cultints=SCCS$v232,roots=(SCCS$v233==5)*1,
cereals=(SCCS$v233==6)*1,gath=SCCS$v203,hunt=SCCS$v204,
fish=SCCS$v205,anim=SCCS$v206,femsubs=SCCS$v890,
pigs=(SCCS$v244==2)*1,milk=(SCCS$v245>1)*1,plow=(SCCS$v243>1)*1,
bovines=(SCCS$v244==7)*1,tree=(SCCS$v233==4)*1,
foodtrade=SCCS$v819,foodscarc=SCCS$v1685,
dateobs=SCCS$v838,
ecorich=SCCS$v857,popdens=SCCS$v156,pathstress=SCCS$v1260,
CVrain=SCCS$v1914/SCCS$v1913,rain=SCCS$v854,temp=SCCS$v855,
AP1=SCCS$v921,AP2=SCCS$v928,ndrymonth=SCCS$v196,
exogamy=SCCS$v72,ncmallow=SCCS$v227,famsize=SCCS$v80,
settype=SCCS$v234,localjh=(SCCS$v236-1),superjh=SCCS$v237,
moralgods=SCCS$v238,fempower=SCCS$v663,
sexratio=1+(SCCS$v1689>85)+(SCCS$v1689>115),
war=SCCS$v1648,himilexp=(SCCS$v899==1)*1,
money=SCCS$v155,wagelabor=SCCS$v1732,
migr=(SCCS$v677==2)*1,brideprice=(SCCS$v208==1)*1,
nuclearfam=(SCCS$v210<=3)*1,pctFemPolyg=SCCS$v872
)
#--look at first 6 rows of fx--
head(fx)
#--check to see number of missing values--
#--also check whether numeric--
vvn<-names(fx)
pp<-NULL
for (i in 1:length(vvn)){
nmiss<-length(which(is.na(fx[,vvn[i]])))
numeric<-is.numeric(fx[,vvn[i]])
pp<-rbind(pp,cbind(nmiss,data.frame(numeric)))
}
row.names(pp)<-vvn
pp
#--identify variables with missing values--
z<-which(pp[,1]>0)
zv1<-vvn[z]
zv1
#--identify variables with non-missing values--
z<-which(pp[,1]==0)
zv2<-vvn[z]
zv2
#-----------------------------
#----Multiple imputation------
#-----------------------------
#--number of imputed data sets to create--
nimp<-10
#--one at a time, loop through those variables with missing values--
for (i in 1:length(zv1)){
#--attach the imputand to the auxiliary data--
zxx<-data.frame(cbind(vaux,fx[,zv1[i]]))
#--in the following line, the imputation is done--
aqq<-complete(mice(zxx,maxit=100,m=nimp),action="long")
#--during first iteration of the loop, create dataframe impdat--
if (i==1){
impdat<-data.frame(aqq[,c(".id",".imp")])
}
#--the imputand is placed as a field in impdat and named--
impdat<-cbind(impdat,data.frame(aqq[,NCOL(zxx)]))
names(impdat)[NCOL(impdat)]<-zv1[i]
}
#--now the non-missing variables are attached to impdat--
gg<-NULL
for (i in 1:nimp){
gg<-rbind(gg,data.frame(fx[,zv2]))
}
impdat<-cbind(impdat,gg)
#--take a look at the top 6 and bottom 6 rows of impdat--
head(impdat)
tail(impdat)
#--impdat is saved as an R-format data file--
save(impdat,file="impdat.Rdata")
[edit] Program 1 (9-22-2009) current
#MI--make the imputed datasets
#--change the following path to the directory with your data and program--
###setwd("d:/projects/MI")
setwd("c:/My Documents/MI")
rm(list=ls(all=TRUE))
options(echo=TRUE)
#--you need the following two packages--you must install them first--
library(foreign)
library(mice)
#--To find the citation for a package, use this function:---
citation("mice")
#-----------------------------
#--Read in data, rearrange----
#-----------------------------
#--Read in auxilliary variables---
load("vaux.Rdata",.GlobalEnv)
row.names(vaux)<-NULL
#--Read in the SCCS dataset---
load("SCCS.Rdata",.GlobalEnv)
#--look at first 6 rows of vaux--
head(vaux)
#--look at field names of vaux--
names(vaux)
#--check to see that rows are properly aligned in the two datasets--
#--sum should equal 186---
sum((SCCS$socname==vaux$socname)*1)
#--remove the society name field--
vaux<-vaux[,-28]
names(vaux)
#--Two nominal variables: brg and rlg----
#--brg: consolidated Burton Regions-----
#0 = (rest of world) circumpolar, South and Meso-America, west North America
#1 = Subsaharan Africa
#2 = Middle Old World
#3 = Southeast Asia, Insular Pacific, Sahul
#4 = Eastern Americas
#--rlg: Religion---
#'0 (no world religion)'
#'1 (Christianity)'
#'2 (Islam)'
#'3 (Hindu/Buddhist)'
#--check to see number of missing values in vaux,
#--whether variables are numeric,
#--and number of discrete values for each variable---
vvn<-names(vaux)
pp<-NULL
for (i in 1:length(vvn)){
nmiss<-length(which(is.na(vaux[,vvn[i]])))
numeric<-is.numeric(vaux[,vvn[i]])
numDiscrVals<-length(table(vaux[,vvn[i]]))
pp<-rbind(pp,cbind(data.frame(numeric),nmiss,numDiscrVals))
}
row.names(pp)<-vvn
pp
#--extract variables to be used from SCCS, put in dataframe fx--
###dateobs=SCCS$v838, #ADDED BY DRW
fx<-data.frame(
socname=SCCS$socname,socID=SCCS$"sccs#",
polygyny=SCCS$v860, ##DRW:NEW
valchild=(SCCS$v473+SCCS$v474+SCCS$v475+SCCS$v476),
cultints=SCCS$v232,roots=(SCCS$v233==5)*1,
cereals=(SCCS$v233==6)*1,gath=SCCS$v203,hunt=SCCS$v204,
fish=SCCS$v205,anim=SCCS$v206,femsubs=SCCS$v890,
pigs=(SCCS$v244==2)*1,milk=(SCCS$v245>1)*1,plow=(SCCS$v243>1)*1,
bovines=(SCCS$v244==7)*1,tree=(SCCS$v233==4)*1,
foodtrade=SCCS$v819,foodscarc=SCCS$v1685,
dateobs=SCCS$v838,
ecorich=SCCS$v857,popdens=SCCS$v156,pathstress=SCCS$v1260,
CVrain=SCCS$v1914/SCCS$v1913,rain=SCCS$v854,temp=SCCS$v855,
AP1=SCCS$v921,AP2=SCCS$v928,ndrymonth=SCCS$v196,
exogamy=SCCS$v72,ncmallow=SCCS$v227,famsize=SCCS$v80,
settype=SCCS$v234,localjh=(SCCS$v236-1),superjh=SCCS$v237,
moralgods=SCCS$v238,fempower=SCCS$v663,
sexratio=1+(SCCS$v1689>85)+(SCCS$v1689>115),
war=SCCS$v1648,himilexp=(SCCS$v899==1)*1,
money=SCCS$v155,wagelabor=SCCS$v1732,
migr=(SCCS$v677==2)*1,brideprice=(SCCS$v208==1)*1,
nuclearfam=(SCCS$v210<=3)*1,pctFemPolyg=SCCS$v872
)
#--look at first 6 rows of fx--
head(fx)
#--check to see number of missing values--
#--also check whether numeric--
vvn<-names(fx)
pp<-NULL
for (i in 1:length(vvn)){
nmiss<-length(which(is.na(fx[,vvn[i]])))
numeric<-is.numeric(fx[,vvn[i]])
pp<-rbind(pp,cbind(nmiss,data.frame(numeric)))
}
row.names(pp)<-vvn
pp
#--identify variables with missing values--
z<-which(pp[,1]>0)
zv1<-vvn[z]
zv1
#--identify variables with non-missing values--
z<-which(pp[,1]==0)
zv2<-vvn[z]
zv2
#-----------------------------
#----Multiple imputation------
#-----------------------------
#--number of imputed data sets to create--
nimp<-10
#--one at a time, loop through those variables with missing values--
for (i in 1:length(zv1)){
#--attach the imputand to the auxiliary data--
zxx<-data.frame(cbind(vaux,fx[,zv1[i]]))
#--in the following line, the imputation is done--
aqq<-complete(mice(zxx,maxit=100,m=nimp),action="long")
#--during first iteration of the loop, create dataframe impdat--
if (i==1){
impdat<-data.frame(aqq[,c(".id",".imp")])
}
#--the imputand is placed as a field in impdat and named--
impdat<-cbind(impdat,data.frame(aqq[,NCOL(zxx)]))
names(impdat)[NCOL(impdat)]<-zv1[i]
}
#--now the non-missing variables are attached to impdat--
gg<-NULL
for (i in 1:nimp){
gg<-rbind(gg,data.frame(fx[,zv2]))
}
impdat<-cbind(impdat,gg)
#--take a look at the top 6 and bottom 6 rows of impdat--
head(impdat)
tail(impdat)
#--impdat is saved as an R-format data file--
save(impdat,file="impdat.Rdata")
