Data
Load and treat Wikipedia data.
#wikipedia data
d = read.csv("data.csv", header=TRUE, row.names=1, encoding="UTF-8"); d$State = rownames(d)
#names alfabetical?
d.alfa = arrange(d, State)
identical(rownames(d), d.alfa$State) #yes
## [1] TRUE
d$State = NULL #revemo redundant col
#calculate mean values
poverty.mean = apply(d[grep("Poverty", colnames(d))], 1, mean)
unemployment.mean = apply(d[grep("Unemploy", colnames(d))], 1, mean)
homocide.mean = apply(d[grep("Homicides", colnames(d))], 1, mean)
homicide.mean.per.cap = homocide.mean/d$Population
d$HDI.mean = apply(d[grep("HDI", colnames(d))], 1, mean)
infant.mortaility.per.cap = d$Infant.mortality.2006/d$Population
#make d2
d2 = data.frame(
poverty.mean,
unemployment.mean,
homicide.mean.per.cap,
infant.mortaility.per.cap,
Fertility.Rate.2010=d$Fertility.Rate.2010,
LE_2007_men=d$LE_2007_Men,
LE_2007_women=d$LE_2007_Women,
Lit.boys=d$Lit.boys,
Lit.girls=d$Lit.girls,
GDP.per.cap=d$GDP.Per.capita.2007.USD
)
#without FD
d_noFD = d[-10, ]
d2_noFD = d2[-10, ]
Load and treat INEG data.
#load data
n = read.csv("data2.csv", skip=1, header=TRUE, row.names=1, encoding = "UTF-8");n$State = rownames(n)
#does the order match?
n.alfa = arrange(n, State);rownames(n.alfa)=n.alfa$State #reordered version
#was initially alfabetical?
identical(rownames(n),n.alfa$State) #no
## [1] FALSE
n = n.alfa #replace old
#identical with first dataset after reorder?
identical(rownames(d),rownames(n)) #no
## [1] FALSE
#compare order
cbind(rownames(d),rownames(n)) #federal district in wrong place
## [,1] [,2]
## [1,] "Aguascalientes" "Aguascalientes"
## [2,] "Baja California" "Baja California"
## [3,] "Baja California Sur" "Baja California Sur"
## [4,] "Campeche" "Campeche"
## [5,] "Chiapas" "Chiapas"
## [6,] "Chihuahua" "Chihuahua"
## [7,] "Coahuila" "Coahuila de Zaragoza"
## [8,] "Colima" "Colima"
## [9,] "Durango" "Distrito Federal"
## [10,] "Federal District" "Durango"
## [11,] "Guanajuato" "Guanajuato"
## [12,] "Guerrero" "Guerrero"
## [13,] "Hidalgo" "Hidalgo"
## [14,] "Jalisco" "Jalisco"
## [15,] "México" "México"
## [16,] "Michoacán" "Michoacán de Ocampo"
## [17,] "Morelos" "Morelos"
## [18,] "Nayarit" "Nayarit"
## [19,] "Nuevo León" "Nuevo León"
## [20,] "Oaxaca" "Oaxaca"
## [21,] "Puebla" "Puebla"
## [22,] "Querétaro" "Querétaro"
## [23,] "Quintana Roo" "Quintana Roo"
## [24,] "San Luis PotosÃ" "San Luis PotosÃ"
## [25,] "Sinaloa" "Sinaloa"
## [26,] "Sonora" "Sonora"
## [27,] "Tabasco" "Tabasco"
## [28,] "Tamaulipas" "Tamaulipas"
## [29,] "Tlaxcala" "Tlaxcala"
## [30,] "Veracruz" "Veracruz de Ignacio de la Llave"
## [31,] "Yucatán" "Yucatán"
## [32,] "Zacatecas" "Zacatecas"
n.new = n[c(1:8,10,9,11:32),]
cbind(rownames(d),rownames(n.new)) #worked? yes
## [,1] [,2]
## [1,] "Aguascalientes" "Aguascalientes"
## [2,] "Baja California" "Baja California"
## [3,] "Baja California Sur" "Baja California Sur"
## [4,] "Campeche" "Campeche"
## [5,] "Chiapas" "Chiapas"
## [6,] "Chihuahua" "Chihuahua"
## [7,] "Coahuila" "Coahuila de Zaragoza"
## [8,] "Colima" "Colima"
## [9,] "Durango" "Durango"
## [10,] "Federal District" "Distrito Federal"
## [11,] "Guanajuato" "Guanajuato"
## [12,] "Guerrero" "Guerrero"
## [13,] "Hidalgo" "Hidalgo"
## [14,] "Jalisco" "Jalisco"
## [15,] "México" "México"
## [16,] "Michoacán" "Michoacán de Ocampo"
## [17,] "Morelos" "Morelos"
## [18,] "Nayarit" "Nayarit"
## [19,] "Nuevo León" "Nuevo León"
## [20,] "Oaxaca" "Oaxaca"
## [21,] "Puebla" "Puebla"
## [22,] "Querétaro" "Querétaro"
## [23,] "Quintana Roo" "Quintana Roo"
## [24,] "San Luis PotosÃ" "San Luis PotosÃ"
## [25,] "Sinaloa" "Sinaloa"
## [26,] "Sonora" "Sonora"
## [27,] "Tabasco" "Tabasco"
## [28,] "Tamaulipas" "Tamaulipas"
## [29,] "Tlaxcala" "Tlaxcala"
## [30,] "Veracruz" "Veracruz de Ignacio de la Llave"
## [31,] "Yucatán" "Yucatán"
## [32,] "Zacatecas" "Zacatecas"
n = n.new #replace with new
#Calculate means
GDP.change = apply(n[grep("GDP.change",colnames(n))], 1, mean)
cost.crime1 = apply(n[grep("cost.crime.\\d",colnames(n))], 1, mean)
cost.crime2 = apply(n[grep("Cost.crime2",colnames(n))], 1, mean)
crime.per.econ = apply(n[grep("Crime.rate.per.10k.econ",colnames(n))], 1, mean)
crime.rate.per.adult = apply(n[grep("Crime.rate.per.100k.adult",colnames(n))], 1, mean)
Dark.crime1 = apply(n[grep("Dark.crime.pct",colnames(n))], 1, mean)
Dark.crime2 = apply(n[grep("Dark.crime.2",colnames(n))], 1, mean)
Doctors.per.pers = apply(n[grep("Doctors.per",colnames(n))], 1, mean)
Econ.units = apply(n[grep("Econ.unit",colnames(n))], 1, mean)
Econ.active.15plus = apply(n[grep("Econ.active.\\d",colnames(n))], 1, mean)
Unemploy.15plus = apply(n[grep("Unemployed.\\d",colnames(n))], 1, mean)
Elec.users = apply(n[grep("Electricity.users.\\d",colnames(n))], 1, mean)
High.income = apply(n[grep("High.income.\\d",colnames(n))], 1, mean)
No.income.work = apply(n[grep("No.income.\\d",colnames(n))], 1, mean)
Low.income = apply(n[grep("Low.income.\\d",colnames(n))], 1, mean)
Fertility.teen = apply(n[grep("Fertility.rate.adol.\\d",colnames(n))], 1, mean)
Cervical.cancer.mort.rate = apply(n[grep("Cervical.cancer.\\d",colnames(n))], 1, mean)
Total.fertility = apply(n[grep("Total.fertility.\\d",colnames(n))], 1, mean)
Women.participation = apply(n[grep("Women.parti",colnames(n))], 1, mean)
Hospital.beds.per.pers = apply(n[grep("Hospital.beds.\\d",colnames(n))], 1, mean)
Households = apply(n[grep("Households.\\d",colnames(n))], 1, mean)
Has.computer = apply(n[grep("Households.computer.\\d",colnames(n))], 1, mean)
Has.toilet = apply(n[grep("Households.toilet.\\d",colnames(n))], 1, mean)
Has.refrig = apply(n[grep("Households.refrig.\\d",colnames(n))], 1, mean)
Has.water.net = apply(n[grep("Households.water.net.\\d",colnames(n))], 1, mean)
Has.drainage = apply(n[grep("Households.drainage.\\d",colnames(n))], 1, mean)
Has.elec = apply(n[grep("Households.elec.\\d",colnames(n))], 1, mean)
Has.wash.mach = apply(n[grep("Households.wash.mach.\\d",colnames(n))], 1, mean)
Has.tv = apply(n[grep("Households.tv.\\d",colnames(n))], 1, mean)
Prison.inmates = apply(n[grep("Prison.inmates.\\d",colnames(n))], 1, mean)
Life.expect = apply(n[grep("Life.expec.\\d",colnames(n))], 1, mean)
Lit.young.women = apply(n[grep("Lit.young.women.\\d",colnames(n))], 1, mean)
Lit.young.men = apply(n[grep("Lit.young.men.\\d",colnames(n))], 1, mean)
Median.age = apply(n[grep("Median.age.\\d",colnames(n))], 1, mean)
Nurses.per.pers = apply(n[grep("Nurses.per",colnames(n))], 1, mean)
Victims.crime.households = apply(n[grep("Victim.crime.households",colnames(n))], 1, mean)
Home.births.pct = apply(n[grep("Home.births",colnames(n))], 1, mean)
Prof.tech.employ.pct = apply(n[grep("Prof.tech",colnames(n))], 1, mean)
Piped.water.pct = apply(n[grep("Household.water",colnames(n))], 1, mean)
Elec.pct = apply(n[grep("Household.elec",colnames(n))], 1, mean)
Good.sani.prop = apply(n[grep("Good.sani",colnames(n))], 1, mean)
Good.water.prop = apply(n[grep("Good.water",colnames(n))], 1, mean)
Prisoner.rate = apply(n[grep("Prisoner.rate",colnames(n))], 1, mean)
Maternal.death.rate = apply(n[grep("Maternal.death",colnames(n))], 1, mean)
Unsafe.neighborhood.percept.rate = apply(n[grep("Neighborhood.unsafe",colnames(n))], 1, mean)
Unsafe.state.percept.rate = apply(n[grep("State.unsafe",colnames(n))], 1, mean)
Sentence.rate = apply(n[grep("Sentence.rate",colnames(n))], 1, mean)
GDP = apply(n[grep("GDP.\\d",colnames(n))], 1, mean)
Population = apply(n[grep("Population.\\d",colnames(n))], 1, mean)
Child.resp.death.rate = apply(n[grep("Child.death.resp.rate",colnames(n))], 1, mean)
Child.diar.death.rate = apply(n[grep("Child.death.diar.rate",colnames(n))], 1, mean)
Unemploy.men.rate = apply(n[grep("Unemploy.rate.men",colnames(n))], 1, mean)
Unemploy.women.rate = apply(n[grep("Unemploy.rate.women",colnames(n))], 1, mean)
#Per capita calculations
Cost.crime1.per.pers = cost.crime1/Population
Cost.crime2.per.pers = cost.crime2/Population
Econ.units.per.pers = Econ.units/Population
Elec.users.per.pers = Elec.users/Population
Households.per.pers = Households/Population
Inmates.per.pers = Prison.inmates/Population
GDP.per.pers = GDP/Population
#Per household calculations
Has.computer.per.household = Has.computer/Households
Has.toilet.per.household = Has.toilet/Households
Has.refrig.per.household = Has.refrig/Households
Has.water.net.per.hh = Has.water.net/Households
Has.drainage.per.hh = Has.drainage/Households
Has.elec.per.hh = Has.elec/Households
Has.wash.mach.per.hh = Has.wash.mach/Households
Has.tv.per.hh = Has.tv/Households
#Employment
Unemployed.15plus.peap = Unemploy.15plus/Econ.active.15plus
Low.income.peap = Low.income/Econ.active.15plus
High.income.peap = High.income/Econ.active.15plus
No.income.peap = No.income.work/Econ.active.15plus
s = data.frame( #select everything
GDP.change, #Economic
Econ.units.per.pers,
Unemployed.15plus.peap,
Unemploy.men.rate,
Unemploy.women.rate,
Low.income.peap,
High.income.peap,
No.income.peap,
Prof.tech.employ.pct,
Cost.crime1.per.pers, #Crime
Cost.crime2.per.pers,
Dark.crime1,
Dark.crime2,
crime.rate.per.adult,
Sentence.rate,
Inmates.per.pers,
Prisoner.rate,
Victims.crime.households,
Unsafe.neighborhood.percept.rate,
Unsafe.state.percept.rate,
Has.computer.per.household, #Appliances and home
Has.toilet.per.household,
Has.refrig.per.household,
Has.water.net.per.hh,
Piped.water.pct,
Good.water.prop,
Has.drainage.per.hh,
Good.sani.prop,
Has.elec.per.hh,
Elec.pct,
Has.wash.mach.per.hh,
Has.tv.per.hh,
Doctors.per.pers, #Health
Nurses.per.pers,
Hospital.beds.per.pers,
Total.fertility,
Fertility.teen,
Home.births.pct,
Maternal.death.rate,
Cervical.cancer.mort.rate,
Life.expect,
Median.age,
Child.resp.death.rate,
Child.diar.death.rate,
Women.participation, #Gender equality
Lit.young.women, #education
Lit.young.men
)
s2 = data.frame( #select chosen variables
GDP.change, #Economic
Unemploy.men.rate,
Unemploy.women.rate,
Low.income.peap,
High.income.peap,
Prof.tech.employ.pct,
crime.rate.per.adult, #Crime
Inmates.per.pers,
Unsafe.neighborhood.percept.rate,
Has.water.net.per.hh, #Materials
Elec.pct,
Has.wash.mach.per.hh,
Doctors.per.pers, #Health
Nurses.per.pers,
Hospital.beds.per.pers,
Total.fertility,
Home.births.pct,
Maternal.death.rate,
Life.expect,
Women.participation, #Gender equality
Lit.young.women #education
)
s3 = remove_redundant_vars(s, threshold = 0.80)
## The following variable pairs had stronger intercorrelations than |0.8|:
## Var1 Var2 r
## 1200 Piped.water.pct Good.water.prop 1.00
## 1152 Has.water.net.per.hh Piped.water.pct 1.00
## 1199 Has.water.net.per.hh Good.water.prop 0.99
## 1728 Total.fertility Fertility.teen 0.99
## 1296 Has.drainage.per.hh Good.sani.prop 0.98
## 813 crime.rate.per.adult Victims.crime.households 0.97
## 1584 Doctors.per.pers Nurses.per.pers 0.96
## 1632 Nurses.per.pers Hospital.beds.per.pers 0.94
## 1771 Has.tv.per.hh Home.births.pct -0.94
## 2208 Lit.young.women Lit.young.men 0.94
## 1392 Has.elec.per.hh Elec.pct 0.94
## 2153 Home.births.pct Lit.young.women -0.93
## 1433 Has.refrig.per.household Has.wash.mach.per.hh 0.93
## 768 Inmates.per.pers Prisoner.rate 0.90
## 1764 Piped.water.pct Home.births.pct -0.90
## 1765 Good.water.prop Home.births.pct -0.90
## 1763 Has.water.net.per.hh Home.births.pct -0.89
## 2200 Home.births.pct Lit.young.men -0.89
## 192 Unemploy.men.rate Unemploy.women.rate 0.89
## 767 Sentence.rate Prisoner.rate 0.88
## 2089 Has.computer.per.household Women.participation 0.88
## 1631 Doctors.per.pers Hospital.beds.per.pers 0.87
## 1482 Piped.water.pct Has.tv.per.hh 0.87
## 949 Prof.tech.employ.pct Has.computer.per.household 0.87
## 144 Unemployed.15plus.peap Unemploy.men.rate 0.87
## 1483 Good.water.prop Has.tv.per.hh 0.86
## 1486 Has.elec.per.hh Has.tv.per.hh 0.86
## 1963 Total.fertility Median.age -0.86
## 1481 Has.water.net.per.hh Has.tv.per.hh 0.86
## 2147 Has.tv.per.hh Lit.young.women 0.86
## 1040 Low.income.peap Has.refrig.per.household -0.86
## 1480 Has.refrig.per.household Has.tv.per.hh 0.86
## 1488 Has.wash.mach.per.hh Has.tv.per.hh 0.85
## 1343 Has.drainage.per.hh Has.elec.per.hh 0.85
## 1770 Has.wash.mach.per.hh Home.births.pct -0.85
## 1344 Good.sani.prop Has.elec.per.hh 0.85
## 2044 Has.refrig.per.household Child.diar.death.rate -0.85
## 1936 Prof.tech.employ.pct Median.age 0.85
## 1485 Good.sani.prop Has.tv.per.hh 0.84
## 1277 No.income.peap Good.sani.prop -0.84
## 946 Low.income.peap Has.computer.per.household -0.84
## 2187 Piped.water.pct Lit.young.men 0.83
## 1487 Elec.pct Has.tv.per.hh 0.83
## 1391 Good.sani.prop Elec.pct 0.83
## 2186 Has.water.net.per.hh Lit.young.men 0.83
## 2188 Good.water.prop Lit.young.men 0.82
## 1390 Has.drainage.per.hh Elec.pct 0.82
## 1762 Has.refrig.per.household Home.births.pct -0.82
## 1465 No.income.peap Has.tv.per.hh -0.82
## 2194 Has.tv.per.hh Lit.young.men 0.81
## 1964 Fertility.teen Median.age -0.81
## 1560 Prof.tech.employ.pct Nurses.per.pers 0.81
## 1745 Low.income.peap Home.births.pct 0.81
## 191 Unemployed.15plus.peap Unemploy.women.rate 0.80
## 1905 Piped.water.pct Life.expect 0.80
## 1607 Prof.tech.employ.pct Hospital.beds.per.pers 0.80
## 1484 Has.drainage.per.hh Has.tv.per.hh 0.80
## The following variables were excluded:
## Good.water.prop, Piped.water.pct, Fertility.teen, Good.sani.prop, Victims.crime.households, Nurses.per.pers, Home.births.pct, Lit.young.men, Elec.pct, Has.wash.mach.per.hh, Prisoner.rate, Unemploy.women.rate, Women.participation, Hospital.beds.per.pers, Has.computer.per.household, Unemploy.men.rate, Has.tv.per.hh, Median.age, Has.refrig.per.household, Has.elec.per.hh
#noFD variants
s_noFD = s[-10, ]
s2_noFD = s2[-10, ]
s3_noFD = remove_redundant_vars(s[-10, ], threshold = 0.80)
## The following variable pairs had stronger intercorrelations than |0.8|:
## Var1 Var2 r
## 1200 Piped.water.pct Good.water.prop 1.00
## 1152 Has.water.net.per.hh Piped.water.pct 1.00
## 1199 Has.water.net.per.hh Good.water.prop 0.99
## 1728 Total.fertility Fertility.teen 0.98
## 1296 Has.drainage.per.hh Good.sani.prop 0.98
## 813 crime.rate.per.adult Victims.crime.households 0.97
## 1771 Has.tv.per.hh Home.births.pct -0.94
## 2208 Lit.young.women Lit.young.men 0.94
## 768 Inmates.per.pers Prisoner.rate 0.94
## 1584 Doctors.per.pers Nurses.per.pers 0.94
## 1392 Has.elec.per.hh Elec.pct 0.93
## 2153 Home.births.pct Lit.young.women -0.93
## 1433 Has.refrig.per.household Has.wash.mach.per.hh 0.92
## 1764 Piped.water.pct Home.births.pct -0.90
## 1765 Good.water.prop Home.births.pct -0.90
## 2200 Home.births.pct Lit.young.men -0.89
## 1763 Has.water.net.per.hh Home.births.pct -0.89
## 767 Sentence.rate Prisoner.rate 0.88
## 1632 Nurses.per.pers Hospital.beds.per.pers 0.88
## 192 Unemploy.men.rate Unemploy.women.rate 0.88
## 946 Low.income.peap Has.computer.per.household -0.87
## 1482 Piped.water.pct Has.tv.per.hh 0.86
## 144 Unemployed.15plus.peap Unemploy.men.rate 0.86
## 2089 Has.computer.per.household Women.participation 0.86
## 1483 Good.water.prop Has.tv.per.hh 0.86
## 1486 Has.elec.per.hh Has.tv.per.hh 0.86
## 1481 Has.water.net.per.hh Has.tv.per.hh 0.86
## 2147 Has.tv.per.hh Lit.young.women 0.86
## 1040 Low.income.peap Has.refrig.per.household -0.85
## 1480 Has.refrig.per.household Has.tv.per.hh 0.85
## 1488 Has.wash.mach.per.hh Has.tv.per.hh 0.85
## 1770 Has.wash.mach.per.hh Home.births.pct -0.85
## 720 Sentence.rate Inmates.per.pers 0.85
## 2044 Has.refrig.per.household Child.diar.death.rate -0.85
## 1343 Has.drainage.per.hh Has.elec.per.hh 0.84
## 1043 Prof.tech.employ.pct Has.refrig.per.household 0.84
## 1344 Good.sani.prop Has.elec.per.hh 0.84
## 949 Prof.tech.employ.pct Has.computer.per.household 0.84
## 1277 No.income.peap Good.sani.prop -0.84
## 1485 Good.sani.prop Has.tv.per.hh 0.83
## 1419 Prof.tech.employ.pct Has.wash.mach.per.hh 0.83
## 1055 Has.computer.per.household Has.refrig.per.household 0.83
## 2187 Piped.water.pct Lit.young.men 0.83
## 1487 Elec.pct Has.tv.per.hh 0.82
## 2186 Has.water.net.per.hh Lit.young.men 0.82
## 2188 Good.water.prop Lit.young.men 0.82
## 1762 Has.refrig.per.household Home.births.pct -0.82
## 1391 Good.sani.prop Elec.pct 0.81
## 1465 No.income.peap Has.tv.per.hh -0.81
## 1390 Has.drainage.per.hh Elec.pct 0.81
## 2194 Has.tv.per.hh Lit.young.men 0.81
## 1745 Low.income.peap Home.births.pct 0.80
## The following variables were excluded:
## Good.water.prop, Piped.water.pct, Fertility.teen, Good.sani.prop, Victims.crime.households, Home.births.pct, Lit.young.men, Prisoner.rate, Nurses.per.pers, Elec.pct, Has.wash.mach.per.hh, Unemploy.women.rate, Has.computer.per.household, Unemploy.men.rate, Has.tv.per.hh, Has.refrig.per.household, Inmates.per.pers, Has.elec.per.hh
#examine missing data
matrixplot(s, labels = substr(colnames(s), 1,8))
matrixplot(s2, labels = substr(colnames(s2), 1,8))
matrixplot(s3, labels = substr(colnames(s3), 1,8))
#impute
#s = irmi(s) #doesnt work becus too few cases
s2 = irmi(s2, noise = F)
s3 = irmi(s3, noise = F)
s2_noFD = irmi(s2_noFD, noise = F)
s3_noFD = irmi(s3_noFD, noise = F)
## No missings in x. Nothing to impute
## Warning in kNN_work(as.data.table(data), variable, metric, k, dist_var, :
## Nothing to impute, because no NA are present (also after using makeNA)
Analyses with FD
Wikipedia S analysis
The Wikipedia data S analysis.
#factor analysis
S.fa = fa(d2) #standard
S.fa_reg = S.fa$scores %>% as.vector
S.fa
## Factor Analysis using method = minres
## Call: fa(r = d2)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## poverty.mean -0.80 0.6357 0.364 1
## unemployment.mean 0.71 0.5066 0.493 1
## homicide.mean.per.cap 0.09 0.0079 0.992 1
## infant.mortaility.per.cap -0.57 0.3212 0.679 1
## Fertility.Rate.2010 -0.47 0.2164 0.784 1
## LE_2007_men 0.74 0.5497 0.450 1
## LE_2007_women 0.72 0.5221 0.478 1
## Lit.boys 0.99 0.9752 0.025 1
## Lit.girls 0.99 0.9730 0.027 1
## GDP.per.cap 0.50 0.2500 0.750 1
##
## MR1
## SS loadings 5.0
## Proportion Var 0.5
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 11 with Chi Square of 305
## The degrees of freedom for the model are 35 and the objective function was 4.1
##
## The root mean square of the residuals (RMSR) is 0.13
## The df corrected root mean square of the residuals is 0.15
##
## The harmonic number of observations is 32 with the empirical chi square 52 with prob < 0.031
## The total number of observations was 32 with Likelihood Chi Square = 107 with prob < 3.3e-09
##
## Tucker Lewis Index of factoring reliability = 0.63
## RMSEA index = 0.066 and the 90 % confidence intervals are 0.066 0.31
## BIC = -14
## Fit based upon off diagonal values = 0.94
## Measures of factor score adequacy
## MR1
## Correlation of scores with factors 0.99
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.97
fa_plot_loadings(S.fa)
ggsave("figures/S_wiki.png")
## Saving 7 x 5 in image
#factor analysis Bartlett's
S.fa = fa(d2, scores = "Bartlett") #Bartlett's
S.fa
## Factor Analysis using method = minres
## Call: fa(r = d2, scores = "Bartlett")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## poverty.mean -0.80 0.6357 0.364 1
## unemployment.mean 0.71 0.5066 0.493 1
## homicide.mean.per.cap 0.09 0.0079 0.992 1
## infant.mortaility.per.cap -0.57 0.3212 0.679 1
## Fertility.Rate.2010 -0.47 0.2164 0.784 1
## LE_2007_men 0.74 0.5497 0.450 1
## LE_2007_women 0.72 0.5221 0.478 1
## Lit.boys 0.99 0.9752 0.025 1
## Lit.girls 0.99 0.9730 0.027 1
## GDP.per.cap 0.50 0.2500 0.750 1
##
## MR1
## SS loadings 5.0
## Proportion Var 0.5
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 11 with Chi Square of 305
## The degrees of freedom for the model are 35 and the objective function was 4.1
##
## The root mean square of the residuals (RMSR) is 0.13
## The df corrected root mean square of the residuals is 0.15
##
## The harmonic number of observations is 32 with the empirical chi square 52 with prob < 0.031
## The total number of observations was 32 with Likelihood Chi Square = 107 with prob < 3.3e-09
##
## Tucker Lewis Index of factoring reliability = 0.63
## RMSEA index = 0.066 and the 90 % confidence intervals are 0.066 0.31
## BIC = -14
## Fit based upon off diagonal values = 0.94
## Measures of factor score adequacy
## MR1
## Correlation of scores with factors 0.99
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.97
fa_plot_loadings(S.fa)
ggsave("figures/S_wiki_Bartlett.png")
## Saving 7 x 5 in image
#insert into d2
d2$S.wiki = as.vector(S.fa$scores)
d3 = data.frame(d, d2) #merge d and d2
d4 = subset(d3, select=c(
S.wiki,
HDI.mean,
Achievement,
Euro,
Afri,
Amer))
#correlations
wtd.cors(d3)
## Poverty.Rate.2012 Poverty.Rate.2010
## Poverty.Rate.2012 1.000 0.9709
## Poverty.Rate.2010 0.971 1.0000
## Unemployment.2010.1 -0.673 -0.6113
## Unemployment.2010.2 -0.673 -0.6162
## Unemployment.2010.3 -0.669 -0.6019
## Unemployment.2010.4 -0.589 -0.5290
## Fertility.Rate.2010 0.480 0.4629
## Homicides..2011. -0.164 -0.1966
## Homicides..2010. -0.196 -0.1917
## Infant.mortality.2006 0.357 0.3233
## LE_2007_Men -0.569 -0.5886
## LE_2007_Women -0.591 -0.6128
## Lit.boys -0.771 -0.7744
## Lit.girls -0.762 -0.7572
## GDP.Per.capita.2007.USD -0.744 -0.7304
## HDI2015 -0.865 -0.8520
## HDI2014 -0.887 -0.8784
## Population -0.049 -0.0744
## Achievement -0.699 -0.7430
## Euro -0.654 -0.6332
## Afri 0.043 0.0069
## Amer 0.678 0.6643
## HDI.mean -0.881 -0.8698
## poverty.mean 0.992 0.9932
## unemployment.mean -0.665 -0.6026
## homicide.mean.per.cap -0.133 -0.1621
## infant.mortaility.per.cap 0.701 0.6968
## Fertility.Rate.2010.1 0.480 0.4629
## LE_2007_men -0.569 -0.5886
## LE_2007_women -0.591 -0.6128
## Lit.boys.1 -0.771 -0.7744
## Lit.girls.1 -0.762 -0.7572
## GDP.per.cap -0.744 -0.7304
## S.wiki -0.790 -0.7889
## Unemployment.2010.1 Unemployment.2010.2
## Poverty.Rate.2012 -0.673 -0.673
## Poverty.Rate.2010 -0.611 -0.616
## Unemployment.2010.1 1.000 0.945
## Unemployment.2010.2 0.945 1.000
## Unemployment.2010.3 0.943 0.951
## Unemployment.2010.4 0.935 0.922
## Fertility.Rate.2010 -0.239 -0.294
## Homicides..2011. 0.227 0.166
## Homicides..2010. 0.281 0.215
## Infant.mortality.2006 -0.377 -0.312
## LE_2007_Men 0.562 0.555
## LE_2007_Women 0.549 0.519
## Lit.boys 0.700 0.705
## Lit.girls 0.682 0.686
## GDP.Per.capita.2007.USD 0.500 0.525
## HDI2015 0.669 0.675
## HDI2014 0.668 0.671
## Population -0.043 0.035
## Achievement 0.458 0.470
## Euro 0.488 0.399
## Afri 0.089 0.174
## Amer -0.530 -0.453
## HDI.mean 0.671 0.676
## poverty.mean -0.646 -0.649
## unemployment.mean 0.978 0.977
## homicide.mean.per.cap 0.262 0.181
## infant.mortaility.per.cap -0.406 -0.431
## Fertility.Rate.2010.1 -0.239 -0.294
## LE_2007_men 0.562 0.555
## LE_2007_women 0.549 0.519
## Lit.boys.1 0.700 0.705
## Lit.girls.1 0.682 0.686
## GDP.per.cap 0.500 0.525
## S.wiki 0.709 0.713
## Unemployment.2010.3 Unemployment.2010.4
## Poverty.Rate.2012 -0.67 -0.589
## Poverty.Rate.2010 -0.60 -0.529
## Unemployment.2010.1 0.94 0.935
## Unemployment.2010.2 0.95 0.922
## Unemployment.2010.3 1.00 0.935
## Unemployment.2010.4 0.94 1.000
## Fertility.Rate.2010 -0.21 -0.188
## Homicides..2011. 0.19 0.058
## Homicides..2010. 0.25 0.062
## Infant.mortality.2006 -0.37 -0.319
## LE_2007_Men 0.53 0.468
## LE_2007_Women 0.51 0.471
## Lit.boys 0.67 0.675
## Lit.girls 0.66 0.674
## GDP.Per.capita.2007.USD 0.47 0.395
## HDI2015 0.64 0.621
## HDI2014 0.65 0.606
## Population -0.08 -0.021
## Achievement 0.42 0.377
## Euro 0.45 0.411
## Afri 0.13 0.038
## Amer -0.50 -0.439
## HDI.mean 0.65 0.616
## poverty.mean -0.64 -0.562
## unemployment.mean 0.98 0.972
## homicide.mean.per.cap 0.24 0.086
## infant.mortaility.per.cap -0.42 -0.380
## Fertility.Rate.2010.1 -0.21 -0.188
## LE_2007_men 0.53 0.468
## LE_2007_women 0.51 0.471
## Lit.boys.1 0.67 0.675
## Lit.girls.1 0.66 0.674
## GDP.per.cap 0.47 0.395
## S.wiki 0.68 0.686
## Fertility.Rate.2010 Homicides..2011.
## Poverty.Rate.2012 0.480 -0.1642
## Poverty.Rate.2010 0.463 -0.1966
## Unemployment.2010.1 -0.239 0.2270
## Unemployment.2010.2 -0.294 0.1657
## Unemployment.2010.3 -0.207 0.1898
## Unemployment.2010.4 -0.188 0.0581
## Fertility.Rate.2010 1.000 0.0112
## Homicides..2011. 0.011 1.0000
## Homicides..2010. 0.010 0.9153
## Infant.mortality.2006 0.040 0.1110
## LE_2007_Men -0.370 -0.0248
## LE_2007_Women -0.449 -0.0512
## Lit.boys -0.426 0.0382
## Lit.girls -0.455 0.0211
## GDP.Per.capita.2007.USD -0.520 0.1806
## HDI2015 -0.636 0.0054
## HDI2014 -0.584 0.0491
## Population -0.448 0.1999
## Achievement -0.327 0.1637
## Euro 0.066 0.1994
## Afri -0.232 0.1710
## Amer -0.024 -0.2447
## HDI.mean -0.610 0.0291
## poverty.mean 0.475 -0.1823
## unemployment.mean -0.237 0.1616
## homicide.mean.per.cap 0.123 0.8728
## infant.mortaility.per.cap 0.490 -0.0295
## Fertility.Rate.2010.1 1.000 0.0112
## LE_2007_men -0.370 -0.0248
## LE_2007_women -0.449 -0.0512
## Lit.boys.1 -0.426 0.0382
## Lit.girls.1 -0.455 0.0211
## GDP.per.cap -0.520 0.1806
## S.wiki -0.458 0.0368
## Homicides..2010. Infant.mortality.2006
## Poverty.Rate.2012 -0.196 0.3574
## Poverty.Rate.2010 -0.192 0.3233
## Unemployment.2010.1 0.281 -0.3771
## Unemployment.2010.2 0.215 -0.3118
## Unemployment.2010.3 0.246 -0.3659
## Unemployment.2010.4 0.062 -0.3195
## Fertility.Rate.2010 0.010 0.0397
## Homicides..2011. 0.915 0.1110
## Homicides..2010. 1.000 0.0752
## Infant.mortality.2006 0.075 1.0000
## LE_2007_Men 0.110 -0.2607
## LE_2007_Women 0.044 -0.1583
## Lit.boys 0.082 -0.2782
## Lit.girls 0.059 -0.3282
## GDP.Per.capita.2007.USD 0.177 -0.2286
## HDI2015 0.017 -0.4412
## HDI2014 0.073 -0.4996
## Population 0.114 0.6989
## Achievement 0.169 -0.0238
## Euro 0.283 -0.3073
## Afri 0.219 -0.0097
## Amer -0.341 0.3240
## HDI.mean 0.047 -0.4747
## poverty.mean -0.195 0.3423
## unemployment.mean 0.202 -0.3508
## homicide.mean.per.cap 0.879 -0.1498
## infant.mortaility.per.cap 0.050 0.6783
## Fertility.Rate.2010.1 0.010 0.0397
## LE_2007_men 0.110 -0.2607
## LE_2007_women 0.044 -0.1583
## Lit.boys.1 0.082 -0.2782
## Lit.girls.1 0.059 -0.3282
## GDP.per.cap 0.177 -0.2286
## S.wiki 0.080 -0.3135
## LE_2007_Men LE_2007_Women Lit.boys Lit.girls
## Poverty.Rate.2012 -0.569 -0.591 -0.771 -0.762
## Poverty.Rate.2010 -0.589 -0.613 -0.774 -0.757
## Unemployment.2010.1 0.562 0.549 0.700 0.682
## Unemployment.2010.2 0.555 0.519 0.705 0.686
## Unemployment.2010.3 0.533 0.507 0.668 0.656
## Unemployment.2010.4 0.468 0.471 0.675 0.674
## Fertility.Rate.2010 -0.370 -0.449 -0.426 -0.455
## Homicides..2011. -0.025 -0.051 0.038 0.021
## Homicides..2010. 0.110 0.044 0.082 0.059
## Infant.mortality.2006 -0.261 -0.158 -0.278 -0.328
## LE_2007_Men 1.000 0.942 0.732 0.703
## LE_2007_Women 0.942 1.000 0.714 0.677
## Lit.boys 0.732 0.714 1.000 0.985
## Lit.girls 0.703 0.677 0.985 1.000
## GDP.Per.capita.2007.USD 0.474 0.511 0.478 0.433
## HDI2015 0.550 0.575 0.714 0.714
## HDI2014 0.557 0.548 0.723 0.728
## Population -0.042 0.056 0.030 -0.017
## Achievement 0.620 0.635 0.784 0.754
## Euro 0.300 0.258 0.580 0.584
## Afri 0.175 0.130 0.124 0.101
## Amer -0.349 -0.296 -0.634 -0.633
## HDI.mean 0.556 0.563 0.722 0.725
## poverty.mean -0.583 -0.607 -0.778 -0.765
## unemployment.mean 0.541 0.523 0.704 0.691
## homicide.mean.per.cap 0.067 0.013 0.089 0.082
## infant.mortaility.per.cap -0.346 -0.312 -0.525 -0.555
## Fertility.Rate.2010.1 -0.370 -0.449 -0.426 -0.455
## LE_2007_men 1.000 0.942 0.732 0.703
## LE_2007_women 0.942 1.000 0.714 0.677
## Lit.boys.1 0.732 0.714 1.000 0.985
## Lit.girls.1 0.703 0.677 0.985 1.000
## GDP.per.cap 0.474 0.511 0.478 0.433
## S.wiki 0.740 0.721 0.996 0.994
## GDP.Per.capita.2007.USD HDI2015 HDI2014
## Poverty.Rate.2012 -0.744 -0.8655 -0.887
## Poverty.Rate.2010 -0.730 -0.8520 -0.878
## Unemployment.2010.1 0.500 0.6686 0.668
## Unemployment.2010.2 0.525 0.6747 0.671
## Unemployment.2010.3 0.465 0.6413 0.654
## Unemployment.2010.4 0.395 0.6213 0.606
## Fertility.Rate.2010 -0.520 -0.6361 -0.584
## Homicides..2011. 0.181 0.0054 0.049
## Homicides..2010. 0.177 0.0169 0.073
## Infant.mortality.2006 -0.229 -0.4412 -0.500
## LE_2007_Men 0.474 0.5501 0.557
## LE_2007_Women 0.511 0.5747 0.548
## Lit.boys 0.478 0.7138 0.723
## Lit.girls 0.433 0.7139 0.728
## GDP.Per.capita.2007.USD 1.000 0.8073 0.773
## HDI2015 0.807 1.0000 0.984
## HDI2014 0.773 0.9836 1.000
## Population 0.341 0.1484 0.067
## Achievement 0.457 0.5291 0.530
## Euro 0.195 0.4410 0.516
## Afri 0.101 0.0878 0.069
## Amer -0.225 -0.4805 -0.556
## HDI.mean 0.792 0.9952 0.997
## poverty.mean -0.742 -0.8648 -0.889
## unemployment.mean 0.481 0.6667 0.665
## homicide.mean.per.cap 0.047 -0.0166 0.056
## infant.mortaility.per.cap -0.692 -0.7992 -0.817
## Fertility.Rate.2010.1 -0.520 -0.6361 -0.584
## LE_2007_men 0.474 0.5501 0.557
## LE_2007_women 0.511 0.5747 0.548
## Lit.boys.1 0.478 0.7138 0.723
## Lit.girls.1 0.433 0.7139 0.728
## GDP.per.cap 1.000 0.8073 0.773
## S.wiki 0.490 0.7392 0.750
## Population Achievement Euro Afri Amer
## Poverty.Rate.2012 -0.049 -0.699 -0.654 0.0433 0.678
## Poverty.Rate.2010 -0.074 -0.743 -0.633 0.0069 0.664
## Unemployment.2010.1 -0.043 0.458 0.488 0.0886 -0.530
## Unemployment.2010.2 0.035 0.470 0.399 0.1741 -0.453
## Unemployment.2010.3 -0.080 0.416 0.449 0.1333 -0.498
## Unemployment.2010.4 -0.021 0.377 0.411 0.0384 -0.439
## Fertility.Rate.2010 -0.448 -0.327 0.066 -0.2317 -0.024
## Homicides..2011. 0.200 0.164 0.199 0.1710 -0.245
## Homicides..2010. 0.114 0.169 0.283 0.2194 -0.341
## Infant.mortality.2006 0.699 -0.024 -0.307 -0.0097 0.324
## LE_2007_Men -0.042 0.620 0.300 0.1752 -0.349
## LE_2007_Women 0.056 0.635 0.258 0.1303 -0.296
## Lit.boys 0.030 0.784 0.580 0.1241 -0.634
## Lit.girls -0.017 0.754 0.584 0.1009 -0.633
## GDP.Per.capita.2007.USD 0.341 0.457 0.195 0.1012 -0.225
## HDI2015 0.148 0.529 0.441 0.0878 -0.481
## HDI2014 0.067 0.530 0.516 0.0687 -0.556
## Population 1.000 0.107 -0.203 0.0446 0.203
## Achievement 0.107 1.000 0.468 0.2384 -0.538
## Euro -0.203 0.468 1.000 -0.3471 -0.983
## Afri 0.045 0.238 -0.347 1.0000 0.170
## Amer 0.203 -0.538 -0.983 0.1704 1.000
## HDI.mean 0.105 0.532 0.484 0.0778 -0.523
## poverty.mean -0.062 -0.727 -0.648 0.0247 0.676
## unemployment.mean -0.027 0.440 0.446 0.1102 -0.490
## homicide.mean.per.cap -0.147 0.116 0.271 0.1543 -0.317
## infant.mortaility.per.cap 0.061 -0.342 -0.364 -0.0333 0.389
## Fertility.Rate.2010.1 -0.448 -0.327 0.066 -0.2317 -0.024
## LE_2007_men -0.042 0.620 0.300 0.1752 -0.349
## LE_2007_women 0.056 0.635 0.258 0.1303 -0.296
## Lit.boys.1 0.030 0.784 0.580 0.1241 -0.634
## Lit.girls.1 -0.017 0.754 0.584 0.1009 -0.633
## GDP.per.cap 0.341 0.457 0.195 0.1012 -0.225
## S.wiki 0.013 0.776 0.582 0.1153 -0.634
## HDI.mean poverty.mean unemployment.mean
## Poverty.Rate.2012 -0.881 0.992 -0.665
## Poverty.Rate.2010 -0.870 0.993 -0.603
## Unemployment.2010.1 0.671 -0.646 0.978
## Unemployment.2010.2 0.676 -0.649 0.977
## Unemployment.2010.3 0.651 -0.639 0.980
## Unemployment.2010.4 0.616 -0.562 0.972
## Fertility.Rate.2010 -0.610 0.475 -0.237
## Homicides..2011. 0.029 -0.182 0.162
## Homicides..2010. 0.047 -0.195 0.202
## Infant.mortality.2006 -0.475 0.342 -0.351
## LE_2007_Men 0.556 -0.583 0.541
## LE_2007_Women 0.563 -0.607 0.523
## Lit.boys 0.722 -0.778 0.704
## Lit.girls 0.725 -0.765 0.691
## GDP.Per.capita.2007.USD 0.792 -0.742 0.481
## HDI2015 0.995 -0.865 0.667
## HDI2014 0.997 -0.889 0.665
## Population 0.105 -0.062 -0.027
## Achievement 0.532 -0.727 0.440
## Euro 0.484 -0.648 0.446
## Afri 0.078 0.025 0.110
## Amer -0.523 0.676 -0.490
## HDI.mean 1.000 -0.882 0.668
## poverty.mean -0.882 1.000 -0.638
## unemployment.mean 0.668 -0.638 1.000
## homicide.mean.per.cap 0.023 -0.149 0.195
## infant.mortaility.per.cap -0.812 0.704 -0.419
## Fertility.Rate.2010.1 -0.610 0.475 -0.237
## LE_2007_men 0.556 -0.583 0.541
## LE_2007_women 0.563 -0.607 0.523
## Lit.boys.1 0.722 -0.778 0.704
## Lit.girls.1 0.725 -0.765 0.691
## GDP.per.cap 0.792 -0.742 0.481
## S.wiki 0.748 -0.795 0.713
## homicide.mean.per.cap infant.mortaility.per.cap
## Poverty.Rate.2012 -0.133 0.701
## Poverty.Rate.2010 -0.162 0.697
## Unemployment.2010.1 0.262 -0.406
## Unemployment.2010.2 0.181 -0.431
## Unemployment.2010.3 0.243 -0.420
## Unemployment.2010.4 0.086 -0.380
## Fertility.Rate.2010 0.123 0.490
## Homicides..2011. 0.873 -0.029
## Homicides..2010. 0.879 0.050
## Infant.mortality.2006 -0.150 0.678
## LE_2007_Men 0.067 -0.346
## LE_2007_Women 0.013 -0.312
## Lit.boys 0.089 -0.525
## Lit.girls 0.082 -0.555
## GDP.Per.capita.2007.USD 0.047 -0.692
## HDI2015 -0.017 -0.799
## HDI2014 0.056 -0.817
## Population -0.147 0.061
## Achievement 0.116 -0.342
## Euro 0.271 -0.364
## Afri 0.154 -0.033
## Amer -0.317 0.389
## HDI.mean 0.023 -0.812
## poverty.mean -0.149 0.704
## unemployment.mean 0.195 -0.419
## homicide.mean.per.cap 1.000 -0.022
## infant.mortaility.per.cap -0.022 1.000
## Fertility.Rate.2010.1 0.123 0.490
## LE_2007_men 0.067 -0.346
## LE_2007_women 0.013 -0.312
## Lit.boys.1 0.089 -0.525
## Lit.girls.1 0.082 -0.555
## GDP.per.cap 0.047 -0.692
## S.wiki 0.089 -0.559
## Fertility.Rate.2010.1 LE_2007_men LE_2007_women
## Poverty.Rate.2012 0.480 -0.569 -0.591
## Poverty.Rate.2010 0.463 -0.589 -0.613
## Unemployment.2010.1 -0.239 0.562 0.549
## Unemployment.2010.2 -0.294 0.555 0.519
## Unemployment.2010.3 -0.207 0.533 0.507
## Unemployment.2010.4 -0.188 0.468 0.471
## Fertility.Rate.2010 1.000 -0.370 -0.449
## Homicides..2011. 0.011 -0.025 -0.051
## Homicides..2010. 0.010 0.110 0.044
## Infant.mortality.2006 0.040 -0.261 -0.158
## LE_2007_Men -0.370 1.000 0.942
## LE_2007_Women -0.449 0.942 1.000
## Lit.boys -0.426 0.732 0.714
## Lit.girls -0.455 0.703 0.677
## GDP.Per.capita.2007.USD -0.520 0.474 0.511
## HDI2015 -0.636 0.550 0.575
## HDI2014 -0.584 0.557 0.548
## Population -0.448 -0.042 0.056
## Achievement -0.327 0.620 0.635
## Euro 0.066 0.300 0.258
## Afri -0.232 0.175 0.130
## Amer -0.024 -0.349 -0.296
## HDI.mean -0.610 0.556 0.563
## poverty.mean 0.475 -0.583 -0.607
## unemployment.mean -0.237 0.541 0.523
## homicide.mean.per.cap 0.123 0.067 0.013
## infant.mortaility.per.cap 0.490 -0.346 -0.312
## Fertility.Rate.2010.1 1.000 -0.370 -0.449
## LE_2007_men -0.370 1.000 0.942
## LE_2007_women -0.449 0.942 1.000
## Lit.boys.1 -0.426 0.732 0.714
## Lit.girls.1 -0.455 0.703 0.677
## GDP.per.cap -0.520 0.474 0.511
## S.wiki -0.458 0.740 0.721
## Lit.boys.1 Lit.girls.1 GDP.per.cap S.wiki
## Poverty.Rate.2012 -0.771 -0.762 -0.744 -0.790
## Poverty.Rate.2010 -0.774 -0.757 -0.730 -0.789
## Unemployment.2010.1 0.700 0.682 0.500 0.709
## Unemployment.2010.2 0.705 0.686 0.525 0.713
## Unemployment.2010.3 0.668 0.656 0.465 0.680
## Unemployment.2010.4 0.675 0.674 0.395 0.686
## Fertility.Rate.2010 -0.426 -0.455 -0.520 -0.458
## Homicides..2011. 0.038 0.021 0.181 0.037
## Homicides..2010. 0.082 0.059 0.177 0.080
## Infant.mortality.2006 -0.278 -0.328 -0.229 -0.313
## LE_2007_Men 0.732 0.703 0.474 0.740
## LE_2007_Women 0.714 0.677 0.511 0.721
## Lit.boys 1.000 0.985 0.478 0.996
## Lit.girls 0.985 1.000 0.433 0.994
## GDP.Per.capita.2007.USD 0.478 0.433 1.000 0.490
## HDI2015 0.714 0.714 0.807 0.739
## HDI2014 0.723 0.728 0.773 0.750
## Population 0.030 -0.017 0.341 0.013
## Achievement 0.784 0.754 0.457 0.776
## Euro 0.580 0.584 0.195 0.582
## Afri 0.124 0.101 0.101 0.115
## Amer -0.634 -0.633 -0.225 -0.634
## HDI.mean 0.722 0.725 0.792 0.748
## poverty.mean -0.778 -0.765 -0.742 -0.795
## unemployment.mean 0.704 0.691 0.481 0.713
## homicide.mean.per.cap 0.089 0.082 0.047 0.089
## infant.mortaility.per.cap -0.525 -0.555 -0.692 -0.559
## Fertility.Rate.2010.1 -0.426 -0.455 -0.520 -0.458
## LE_2007_men 0.732 0.703 0.474 0.740
## LE_2007_women 0.714 0.677 0.511 0.721
## Lit.boys.1 1.000 0.985 0.478 0.996
## Lit.girls.1 0.985 1.000 0.433 0.994
## GDP.per.cap 0.478 0.433 1.000 0.490
## S.wiki 0.996 0.994 0.490 1.000
wtd.cors(d4)
## S.wiki HDI.mean Achievement Euro Afri Amer
## S.wiki 1.00 0.748 0.78 0.58 0.115 -0.63
## HDI.mean 0.75 1.000 0.53 0.48 0.078 -0.52
## Achievement 0.78 0.532 1.00 0.47 0.238 -0.54
## Euro 0.58 0.484 0.47 1.00 -0.347 -0.98
## Afri 0.12 0.078 0.24 -0.35 1.000 0.17
## Amer -0.63 -0.523 -0.54 -0.98 0.170 1.00
INEG S analysis
The INEG data S analysis.
#Regression method
#all variables
s.fa = fa(s)
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was
## done
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## In factor.scores, the correlation matrix is singular, an approximation is used
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was
## done
s.fa_orig_scores = s.fa$scores %>% as.vector
fa_plot_loadings(s.fa)
ggsave("figures/S_self_all.png")
## Saving 7 x 5 in image
#selected
s2.fa = fa(s2)
s2.fa_orig_scores = s2.fa$scores %>% as.vector
fa_plot_loadings(s2.fa)
ggsave("figures/S_self_chosen.png")
## Saving 7 x 5 in image
#automatically selected
s3.fa = fa(s3)
s3.fa_orig_scores = s3.fa$scores %>% as.vector
fa_plot_loadings(s3.fa)
ggsave("figures/S_self_automatic.png")
## Saving 7 x 5 in image
#Bartlett's method
#all variables
s.fa = fa(s, scores = "Bartlett")
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was
## done
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
fa_plot_loadings(s.fa)
ggsave("figures/S_self_all_Bartlett.png")
## Saving 7 x 5 in image
#selected
s2.fa = fa(s2, scores = "Bartlett")
fa_plot_loadings(s2.fa)
ggsave("figures/S_schosen_Bartlett.png")
## Saving 7 x 5 in image
#automatically selected
s3.fa = fa(s3, scores = "Bartlett")
fa_plot_loadings(s3.fa)
ggsave("figures/S_self_automatic_Bartlett.png")
## Saving 7 x 5 in image
#correlate loadings
load1 = data.frame(all = as.vector(s.fa$loadings)); rownames(load1) = rownames(s.fa$loadings)
load2 = data.frame(chosen = as.vector(s2.fa$loadings)); rownames(load2) = rownames(s2.fa$loadings)
load3 = data.frame(automatic = as.vector(s3.fa$loadings)); rownames(load3) = rownames(s3.fa$loadings)
loadings = merge_datasets(load1, load2)
loadings = merge_datasets(loadings, load3)
wtd.cors(loadings)
## all chosen automatic
## all 1.00 0.98 1.00
## chosen 0.98 1.00 0.99
## automatic 1.00 0.99 1.00
count.pairwise(loadings)
## all chosen automatic
## all 47 21 27
## chosen 21 21 13
## automatic 27 13 27
Main results
#results
#regression
s$S.all_reg = s.fa_orig_scores
s$S.chosen_reg = s2.fa_orig_scores
s$S.automatic_reg = s3.fa_orig_scores
s$S.wiki_reg = S.fa_reg
#bartlett
s$S.all = as.vector(s.fa$scores)
s$S.chosen = as.vector(s2.fa$scores)
s$S.automatic = as.vector(s3.fa$scores)
s$S.wiki = d4$S.wiki
#other
s$HDI.mean = d4$HDI.mean
s$Cognitive.ability = d3$Achievement
#s$Euro = d3$Euro #saving this result for the admixture paper
#reorder and save
s = s[order(rownames(s)), ] #to match up with admixture paper dataset
write.csv(wtd.cors(s)[48:53,48:53], "results/correlations.csv")
write.csv(s["S.chosen"], "S factor scores.csv")
#examine some correlations
wtd.cors(s[c("S.all_reg", "S.chosen_reg", "S.automatic_reg", "S.wiki_reg", "HDI.mean", "Cognitive.ability")]) %>% write_clipboard()
## S all reg S chosen reg S automatic reg S wiki reg
## S all reg 1.00 -0.08 -0.04 0.08
## S chosen reg -0.08 1.00 0.94 0.83
## S automatic reg -0.04 0.94 1.00 0.91
## S wiki reg 0.08 0.83 0.91 1.00
## HDI mean -0.17 0.93 0.89 0.76
## Cognitive ability -0.12 0.65 0.74 0.78
## HDI mean Cognitive ability
## S all reg -0.17 -0.12
## S chosen reg 0.93 0.65
## S automatic reg 0.89 0.74
## S wiki reg 0.76 0.78
## HDI mean 1.00 0.53
## Cognitive ability 0.53 1.00
wtd.cors(s[c("S.all", "S.chosen", "S.automatic", "S.wiki", "HDI.mean", "Cognitive.ability")]) %>% write_clipboard()
## S all S chosen S automatic S wiki HDI mean
## S all 1.00 0.96 0.99 0.93 0.86
## S chosen 0.96 1.00 0.96 0.86 0.93
## S automatic 0.99 0.96 1.00 0.91 0.88
## S wiki 0.93 0.86 0.91 1.00 0.75
## HDI mean 0.86 0.93 0.88 0.75 1.00
## Cognitive ability 0.76 0.70 0.76 0.78 0.53
## Cognitive ability
## S all 0.76
## S chosen 0.70
## S automatic 0.76
## S wiki 0.78
## HDI mean 0.53
## Cognitive ability 1.00
#plots
#plot all 6 plots automatically
temp = s[str_detect(colnames(s) ,"S\\.")]
temp$HDI = s$HDI.mean
ggpairs(temp, axisLabels = "none")
## Warning: Removed 3 rows containing non-finite values (stat_density).
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing non-finite values (stat_density).
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
#Scatterplot with names
GG_scatter(s, "Cognitive.ability", "S.chosen")
ggsave("figures/CA_S_chosen.png")
## Saving 7 x 5 in image
#MCV
#S.all
fa_Jensens_method(s.fa, s, criterion = "Cognitive.ability") +
xlab("Loading on S (all variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_all.png")
## Saving 7 x 5 in image
#S.chosen
s2$ACH = d3$Achievement
fa_Jensens_method(s2.fa, s2, criterion = "ACH") +
xlab("Loading on S (chosen variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_chosen.png")
## Saving 7 x 5 in image
#S.automatic
s3$ACH = d3$Achievement
fa_Jensens_method(s3.fa, s3, criterion = "ACH") +
xlab("Loading on S (automatically chosen variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_automatic.png")
## Saving 7 x 5 in image
#S.wiki
fa_Jensens_method(S.fa, d3, criterion = "Achievement") +
xlab("Loading on S (Wikipedia variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_wiki.png")
## Saving 7 x 5 in image
Analyses without FD
This is some of the above code chunks repeated. The only difference are some plot names. First we simply swap out the variables with their alternate versions.
#Wikipedia data
d = d_noFD
d2 = d2_noFD
#INEG data
s = s_noFD
s2 = s2_noFD
s3 = s3_noFD
Wikipedia S analysis
The Wikipedia data S analysis.
#factor analysis
S.fa = fa(d2) #standard
S.fa
## Factor Analysis using method = minres
## Call: fa(r = d2)
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## poverty.mean -0.78 0.603 0.397 1
## unemployment.mean 0.69 0.482 0.518 1
## homicide.mean.per.cap 0.11 0.012 0.988 1
## infant.mortaility.per.cap -0.54 0.289 0.711 1
## Fertility.Rate.2010 -0.44 0.191 0.809 1
## LE_2007_men 0.72 0.514 0.486 1
## LE_2007_women 0.69 0.480 0.520 1
## Lit.boys 0.99 0.980 0.020 1
## Lit.girls 0.99 0.988 0.012 1
## GDP.per.cap 0.47 0.217 0.783 1
##
## MR1
## SS loadings 4.76
## Proportion Var 0.48
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 11 with Chi Square of 290
## The degrees of freedom for the model are 35 and the objective function was 4
##
## The root mean square of the residuals (RMSR) is 0.13
## The df corrected root mean square of the residuals is 0.15
##
## The harmonic number of observations is 31 with the empirical chi square 51 with prob < 0.041
## The total number of observations was 31 with Likelihood Chi Square = 101 with prob < 2.4e-08
##
## Tucker Lewis Index of factoring reliability = 0.64
## RMSEA index = 0.063 and the 90 % confidence intervals are 0.063 0.3
## BIC = -19
## Fit based upon off diagonal values = 0.93
## Measures of factor score adequacy
## MR1
## Correlation of scores with factors 1.00
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.99
fa_plot_loadings(S.fa)
ggsave("figures/S_wiki_noFD.png")
## Saving 7 x 5 in image
#factor analysis Bartlett's
S.fa = fa(d2, scores = "Bartlett") #Bartlett's
S.fa
## Factor Analysis using method = minres
## Call: fa(r = d2, scores = "Bartlett")
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1 h2 u2 com
## poverty.mean -0.78 0.603 0.397 1
## unemployment.mean 0.69 0.482 0.518 1
## homicide.mean.per.cap 0.11 0.012 0.988 1
## infant.mortaility.per.cap -0.54 0.289 0.711 1
## Fertility.Rate.2010 -0.44 0.191 0.809 1
## LE_2007_men 0.72 0.514 0.486 1
## LE_2007_women 0.69 0.480 0.520 1
## Lit.boys 0.99 0.980 0.020 1
## Lit.girls 0.99 0.988 0.012 1
## GDP.per.cap 0.47 0.217 0.783 1
##
## MR1
## SS loadings 4.76
## Proportion Var 0.48
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## The degrees of freedom for the null model are 45 and the objective function was 11 with Chi Square of 290
## The degrees of freedom for the model are 35 and the objective function was 4
##
## The root mean square of the residuals (RMSR) is 0.13
## The df corrected root mean square of the residuals is 0.15
##
## The harmonic number of observations is 31 with the empirical chi square 51 with prob < 0.041
## The total number of observations was 31 with Likelihood Chi Square = 101 with prob < 2.4e-08
##
## Tucker Lewis Index of factoring reliability = 0.64
## RMSEA index = 0.063 and the 90 % confidence intervals are 0.063 0.3
## BIC = -19
## Fit based upon off diagonal values = 0.93
## Measures of factor score adequacy
## MR1
## Correlation of scores with factors 1.00
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.99
fa_plot_loadings(S.fa)
ggsave("figures/S_wiki_Bartlett_noFD.png")
## Saving 7 x 5 in image
#insert into d2
d2$S.wiki = as.vector(S.fa$scores)
d3 = data.frame(d, d2) #merge d and d2
d4 = subset(d3, select=c(
S.wiki,
HDI.mean,
Achievement,
Euro,
Afri,
Amer))
#correlations
wtd.cors(d3)
## Poverty.Rate.2012 Poverty.Rate.2010
## Poverty.Rate.2012 1.000 0.969
## Poverty.Rate.2010 0.969 1.000
## Unemployment.2010.1 -0.658 -0.593
## Unemployment.2010.2 -0.655 -0.594
## Unemployment.2010.3 -0.665 -0.596
## Unemployment.2010.4 -0.575 -0.513
## Fertility.Rate.2010 0.435 0.411
## Homicides..2011. -0.158 -0.191
## Homicides..2010. -0.192 -0.187
## Infant.mortality.2006 0.403 0.368
## LE_2007_Men -0.547 -0.568
## LE_2007_Women -0.565 -0.588
## Lit.boys -0.760 -0.764
## Lit.girls -0.755 -0.750
## GDP.Per.capita.2007.USD -0.789 -0.768
## HDI2015 -0.887 -0.870
## HDI2014 -0.896 -0.884
## Population 0.186 0.153
## Achievement -0.703 -0.749
## Euro -0.718 -0.698
## Afri 0.066 0.029
## Amer 0.740 0.727
## HDI.mean -0.896 -0.882
## poverty.mean 0.992 0.993
## unemployment.mean -0.652 -0.586
## homicide.mean.per.cap -0.163 -0.193
## infant.mortaility.per.cap 0.680 0.675
## Fertility.Rate.2010.1 0.435 0.411
## LE_2007_men -0.547 -0.568
## LE_2007_women -0.565 -0.588
## Lit.boys.1 -0.760 -0.764
## Lit.girls.1 -0.755 -0.750
## GDP.per.cap -0.789 -0.768
## S.wiki -0.771 -0.769
## Unemployment.2010.1 Unemployment.2010.2
## Poverty.Rate.2012 -0.658 -0.65
## Poverty.Rate.2010 -0.593 -0.59
## Unemployment.2010.1 1.000 0.94
## Unemployment.2010.2 0.943 1.00
## Unemployment.2010.3 0.945 0.96
## Unemployment.2010.4 0.934 0.92
## Fertility.Rate.2010 -0.157 -0.20
## Homicides..2011. 0.222 0.16
## Homicides..2010. 0.278 0.21
## Infant.mortality.2006 -0.415 -0.36
## LE_2007_Men 0.544 0.53
## LE_2007_Women 0.526 0.49
## Lit.boys 0.687 0.69
## Lit.girls 0.673 0.68
## GDP.Per.capita.2007.USD 0.492 0.50
## HDI2015 0.670 0.66
## HDI2014 0.658 0.65
## Population -0.284 -0.21
## Achievement 0.454 0.47
## Euro 0.535 0.45
## Afri 0.073 0.16
## Amer -0.576 -0.51
## HDI.mean 0.666 0.66
## poverty.mean -0.629 -0.63
## unemployment.mean 0.978 0.98
## homicide.mean.per.cap 0.290 0.21
## infant.mortaility.per.cap -0.368 -0.39
## Fertility.Rate.2010.1 -0.157 -0.20
## LE_2007_men 0.544 0.53
## LE_2007_women 0.526 0.49
## Lit.boys.1 0.687 0.69
## Lit.girls.1 0.673 0.68
## GDP.per.cap 0.492 0.50
## S.wiki 0.689 0.69
## Unemployment.2010.3 Unemployment.2010.4
## Poverty.Rate.2012 -0.66 -0.575
## Poverty.Rate.2010 -0.60 -0.513
## Unemployment.2010.1 0.95 0.934
## Unemployment.2010.2 0.96 0.922
## Unemployment.2010.3 1.00 0.935
## Unemployment.2010.4 0.93 1.000
## Fertility.Rate.2010 -0.17 -0.122
## Homicides..2011. 0.19 0.051
## Homicides..2010. 0.24 0.056
## Infant.mortality.2006 -0.39 -0.348
## LE_2007_Men 0.52 0.451
## LE_2007_Women 0.50 0.451
## Lit.boys 0.66 0.665
## Lit.girls 0.65 0.666
## GDP.Per.capita.2007.USD 0.51 0.385
## HDI2015 0.68 0.633
## HDI2014 0.67 0.603
## Population -0.25 -0.205
## Achievement 0.41 0.371
## Euro 0.48 0.446
## Afri 0.12 0.025
## Amer -0.52 -0.472
## HDI.mean 0.68 0.619
## poverty.mean -0.63 -0.547
## unemployment.mean 0.98 0.971
## homicide.mean.per.cap 0.26 0.105
## infant.mortaility.per.cap -0.41 -0.352
## Fertility.Rate.2010.1 -0.17 -0.122
## LE_2007_men 0.52 0.451
## LE_2007_women 0.50 0.451
## Lit.boys.1 0.66 0.665
## Lit.girls.1 0.65 0.666
## GDP.per.cap 0.51 0.385
## S.wiki 0.67 0.673
## Fertility.Rate.2010 Homicides..2011.
## Poverty.Rate.2012 0.435 -0.1578
## Poverty.Rate.2010 0.411 -0.1912
## Unemployment.2010.1 -0.157 0.2224
## Unemployment.2010.2 -0.203 0.1594
## Unemployment.2010.3 -0.173 0.1859
## Unemployment.2010.4 -0.122 0.0515
## Fertility.Rate.2010 1.000 0.0465
## Homicides..2011. 0.046 1.0000
## Homicides..2010. 0.041 0.9151
## Infant.mortality.2006 0.143 0.1058
## LE_2007_Men -0.313 -0.0353
## LE_2007_Women -0.378 -0.0658
## Lit.boys -0.387 0.0294
## Lit.girls -0.451 0.0139
## GDP.Per.capita.2007.USD -0.253 0.1940
## HDI2015 -0.496 -0.0203
## HDI2014 -0.459 0.0323
## Population -0.047 0.2452
## Achievement -0.349 0.1609
## Euro -0.028 0.2094
## Afri -0.222 0.1677
## Amer 0.072 -0.2544
## HDI.mean -0.478 0.0086
## poverty.mean 0.426 -0.1764
## unemployment.mean -0.167 0.1559
## homicide.mean.per.cap 0.077 0.8836
## infant.mortaility.per.cap 0.385 -0.0146
## Fertility.Rate.2010.1 1.000 0.0465
## LE_2007_men -0.313 -0.0353
## LE_2007_women -0.378 -0.0658
## Lit.boys.1 -0.387 0.0294
## Lit.girls.1 -0.451 0.0139
## GDP.per.cap -0.253 0.1940
## S.wiki -0.434 0.0235
## Homicides..2010. Infant.mortality.2006
## Poverty.Rate.2012 -0.1915 0.403
## Poverty.Rate.2010 -0.1873 0.368
## Unemployment.2010.1 0.2785 -0.415
## Unemployment.2010.2 0.2113 -0.355
## Unemployment.2010.3 0.2430 -0.387
## Unemployment.2010.4 0.0558 -0.348
## Fertility.Rate.2010 0.0414 0.143
## Homicides..2011. 0.9151 0.106
## Homicides..2010. 1.0000 0.070
## Infant.mortality.2006 0.0704 1.000
## LE_2007_Men 0.1041 -0.297
## LE_2007_Women 0.0343 -0.202
## Lit.boys 0.0752 -0.314
## Lit.girls 0.0533 -0.357
## GDP.Per.capita.2007.USD 0.1934 -0.403
## HDI2015 -0.0041 -0.587
## HDI2014 0.0609 -0.620
## Population 0.1238 0.898
## Achievement 0.1670 -0.034
## Euro 0.2929 -0.293
## Afri 0.2168 -0.022
## Amer -0.3509 0.311
## HDI.mean 0.0317 -0.608
## poverty.mean -0.1908 0.388
## unemployment.mean 0.1984 -0.384
## homicide.mean.per.cap 0.8890 -0.138
## infant.mortaility.per.cap 0.0684 0.775
## Fertility.Rate.2010.1 0.0414 0.143
## LE_2007_men 0.1041 -0.297
## LE_2007_women 0.0343 -0.202
## Lit.boys.1 0.0752 -0.314
## Lit.girls.1 0.0533 -0.357
## GDP.per.cap 0.1934 -0.403
## S.wiki 0.0663 -0.350
## LE_2007_Men LE_2007_Women Lit.boys Lit.girls
## Poverty.Rate.2012 -0.547 -0.565 -0.760 -0.755
## Poverty.Rate.2010 -0.568 -0.588 -0.764 -0.750
## Unemployment.2010.1 0.544 0.526 0.687 0.673
## Unemployment.2010.2 0.533 0.488 0.691 0.676
## Unemployment.2010.3 0.524 0.497 0.663 0.650
## Unemployment.2010.4 0.451 0.451 0.665 0.666
## Fertility.Rate.2010 -0.313 -0.378 -0.387 -0.451
## Homicides..2011. -0.035 -0.066 0.029 0.014
## Homicides..2010. 0.104 0.034 0.075 0.053
## Infant.mortality.2006 -0.297 -0.202 -0.314 -0.357
## LE_2007_Men 1.000 0.941 0.720 0.694
## LE_2007_Women 0.941 1.000 0.699 0.668
## Lit.boys 0.720 0.699 1.000 0.986
## Lit.girls 0.694 0.668 0.986 1.000
## GDP.Per.capita.2007.USD 0.451 0.461 0.460 0.435
## HDI2015 0.526 0.531 0.721 0.740
## HDI2014 0.530 0.500 0.719 0.740
## Population -0.295 -0.209 -0.180 -0.200
## Achievement 0.620 0.640 0.788 0.754
## Euro 0.344 0.313 0.632 0.623
## Afri 0.161 0.112 0.109 0.088
## Amer -0.391 -0.349 -0.684 -0.671
## HDI.mean 0.531 0.516 0.723 0.743
## poverty.mean -0.562 -0.581 -0.768 -0.758
## unemployment.mean 0.524 0.501 0.693 0.682
## homicide.mean.per.cap 0.092 0.042 0.113 0.101
## infant.mortaility.per.cap -0.301 -0.247 -0.495 -0.540
## Fertility.Rate.2010.1 -0.313 -0.378 -0.387 -0.451
## LE_2007_men 1.000 0.941 0.720 0.694
## LE_2007_women 0.941 1.000 0.699 0.668
## Lit.boys.1 0.720 0.699 1.000 0.986
## Lit.girls.1 0.694 0.668 0.986 1.000
## GDP.per.cap 0.451 0.461 0.460 0.435
## S.wiki 0.717 0.693 0.994 0.998
## GDP.Per.capita.2007.USD HDI2015 HDI2014
## Poverty.Rate.2012 -0.789 -0.8872 -0.896
## Poverty.Rate.2010 -0.768 -0.8697 -0.884
## Unemployment.2010.1 0.492 0.6697 0.658
## Unemployment.2010.2 0.501 0.6623 0.651
## Unemployment.2010.3 0.507 0.6764 0.673
## Unemployment.2010.4 0.385 0.6330 0.603
## Fertility.Rate.2010 -0.253 -0.4964 -0.459
## Homicides..2011. 0.194 -0.0203 0.032
## Homicides..2010. 0.193 -0.0041 0.061
## Infant.mortality.2006 -0.403 -0.5872 -0.620
## LE_2007_Men 0.451 0.5263 0.530
## LE_2007_Women 0.461 0.5312 0.500
## Lit.boys 0.460 0.7207 0.719
## Lit.girls 0.435 0.7404 0.740
## GDP.Per.capita.2007.USD 1.000 0.7354 0.718
## HDI2015 0.735 1.0000 0.983
## HDI2014 0.718 0.9829 1.000
## Population -0.227 -0.3649 -0.404
## Achievement 0.529 0.5687 0.554
## Euro 0.379 0.6017 0.651
## Afri 0.059 0.0511 0.035
## Amer -0.408 -0.6406 -0.689
## HDI.mean 0.729 0.9948 0.997
## poverty.mean -0.784 -0.8849 -0.897
## unemployment.mean 0.481 0.6754 0.661
## homicide.mean.per.cap 0.145 0.0405 0.112
## infant.mortaility.per.cap -0.655 -0.7730 -0.791
## Fertility.Rate.2010.1 -0.253 -0.4964 -0.459
## LE_2007_men 0.451 0.5263 0.530
## LE_2007_women 0.461 0.5312 0.500
## Lit.boys.1 0.460 0.7207 0.719
## Lit.girls.1 0.435 0.7404 0.740
## GDP.per.cap 1.000 0.7354 0.718
## S.wiki 0.462 0.7465 0.745
## Population Achievement Euro Afri Amer
## Poverty.Rate.2012 0.186 -0.703 -0.718 0.0658 0.740
## Poverty.Rate.2010 0.153 -0.749 -0.698 0.0287 0.727
## Unemployment.2010.1 -0.284 0.454 0.535 0.0729 -0.576
## Unemployment.2010.2 -0.206 0.467 0.453 0.1586 -0.505
## Unemployment.2010.3 -0.245 0.412 0.476 0.1245 -0.523
## Unemployment.2010.4 -0.205 0.371 0.446 0.0250 -0.472
## Fertility.Rate.2010 -0.047 -0.349 -0.028 -0.2218 0.072
## Homicides..2011. 0.245 0.161 0.209 0.1677 -0.254
## Homicides..2010. 0.124 0.167 0.293 0.2168 -0.351
## Infant.mortality.2006 0.898 -0.034 -0.293 -0.0218 0.311
## LE_2007_Men -0.295 0.620 0.344 0.1610 -0.391
## LE_2007_Women -0.209 0.640 0.313 0.1115 -0.349
## Lit.boys -0.180 0.788 0.632 0.1090 -0.684
## Lit.girls -0.200 0.754 0.623 0.0885 -0.671
## GDP.Per.capita.2007.USD -0.227 0.529 0.379 0.0593 -0.408
## HDI2015 -0.365 0.569 0.602 0.0511 -0.641
## HDI2014 -0.404 0.554 0.651 0.0346 -0.689
## Population 1.000 0.080 -0.132 -0.0305 0.144
## Achievement 0.080 1.000 0.486 0.2336 -0.556
## Euro -0.132 0.486 1.000 -0.3388 -0.983
## Afri -0.031 0.234 -0.339 1.0000 0.160
## Amer 0.144 -0.556 -0.983 0.1599 1.000
## HDI.mean -0.388 0.563 0.631 0.0422 -0.670
## poverty.mean 0.170 -0.733 -0.713 0.0470 0.739
## unemployment.mean -0.239 0.435 0.488 0.0963 -0.530
## homicide.mean.per.cap -0.103 0.125 0.260 0.1651 -0.306
## infant.mortaility.per.cap 0.486 -0.339 -0.447 -0.0039 0.470
## Fertility.Rate.2010.1 -0.047 -0.349 -0.028 -0.2218 0.072
## LE_2007_men -0.295 0.620 0.344 0.1610 -0.391
## LE_2007_women -0.209 0.640 0.313 0.1115 -0.349
## Lit.boys.1 -0.180 0.788 0.632 0.1090 -0.684
## Lit.girls.1 -0.200 0.754 0.623 0.0885 -0.671
## GDP.per.cap -0.227 0.529 0.379 0.0593 -0.408
## S.wiki -0.199 0.772 0.630 0.0971 -0.680
## HDI.mean poverty.mean unemployment.mean
## Poverty.Rate.2012 -0.8958 0.992 -0.652
## Poverty.Rate.2010 -0.8815 0.993 -0.586
## Unemployment.2010.1 0.6662 -0.629 0.978
## Unemployment.2010.2 0.6587 -0.628 0.978
## Unemployment.2010.3 0.6776 -0.634 0.981
## Unemployment.2010.4 0.6193 -0.547 0.971
## Fertility.Rate.2010 -0.4778 0.426 -0.167
## Homicides..2011. 0.0086 -0.176 0.156
## Homicides..2010. 0.0317 -0.191 0.198
## Infant.mortality.2006 -0.6077 0.388 -0.384
## LE_2007_Men 0.5306 -0.562 0.524
## LE_2007_Women 0.5163 -0.581 0.501
## Lit.boys 0.7227 -0.768 0.693
## Lit.girls 0.7432 -0.758 0.682
## GDP.Per.capita.2007.USD 0.7287 -0.784 0.481
## HDI2015 0.9948 -0.885 0.675
## HDI2014 0.9965 -0.897 0.661
## Population -0.3878 0.170 -0.239
## Achievement 0.5628 -0.733 0.435
## Euro 0.6314 -0.713 0.488
## Afri 0.0422 0.047 0.096
## Amer -0.6701 0.739 -0.530
## HDI.mean 1.0000 -0.895 0.670
## poverty.mean -0.8953 1.000 -0.623
## unemployment.mean 0.6702 -0.623 1.000
## homicide.mean.per.cap 0.0801 -0.180 0.219
## infant.mortaility.per.cap -0.7862 0.682 -0.387
## Fertility.Rate.2010.1 -0.4778 0.426 -0.167
## LE_2007_men 0.5306 -0.562 0.524
## LE_2007_women 0.5163 -0.581 0.501
## Lit.boys.1 0.7227 -0.768 0.693
## Lit.girls.1 0.7432 -0.758 0.682
## GDP.per.cap 0.7287 -0.784 0.481
## S.wiki 0.7489 -0.776 0.696
## homicide.mean.per.cap infant.mortaility.per.cap
## Poverty.Rate.2012 -0.163 0.6799
## Poverty.Rate.2010 -0.193 0.6746
## Unemployment.2010.1 0.290 -0.3678
## Unemployment.2010.2 0.213 -0.3860
## Unemployment.2010.3 0.259 -0.4076
## Unemployment.2010.4 0.105 -0.3516
## Fertility.Rate.2010 0.077 0.3855
## Homicides..2011. 0.884 -0.0146
## Homicides..2010. 0.889 0.0684
## Infant.mortality.2006 -0.138 0.7747
## LE_2007_Men 0.092 -0.3005
## LE_2007_Women 0.042 -0.2471
## Lit.boys 0.113 -0.4952
## Lit.girls 0.101 -0.5397
## GDP.Per.capita.2007.USD 0.145 -0.6554
## HDI2015 0.040 -0.7730
## HDI2014 0.112 -0.7910
## Population -0.103 0.4864
## Achievement 0.125 -0.3385
## Euro 0.260 -0.4475
## Afri 0.165 -0.0039
## Amer -0.306 0.4699
## HDI.mean 0.080 -0.7862
## poverty.mean -0.180 0.6824
## unemployment.mean 0.219 -0.3867
## homicide.mean.per.cap 1.000 -0.0617
## infant.mortaility.per.cap -0.062 1.0000
## Fertility.Rate.2010.1 0.077 0.3855
## LE_2007_men 0.092 -0.3005
## LE_2007_women 0.042 -0.2471
## Lit.boys.1 0.113 -0.4952
## Lit.girls.1 0.101 -0.5397
## GDP.per.cap 0.145 -0.6554
## S.wiki 0.109 -0.5336
## Fertility.Rate.2010.1 LE_2007_men LE_2007_women
## Poverty.Rate.2012 0.435 -0.547 -0.565
## Poverty.Rate.2010 0.411 -0.568 -0.588
## Unemployment.2010.1 -0.157 0.544 0.526
## Unemployment.2010.2 -0.203 0.533 0.488
## Unemployment.2010.3 -0.173 0.524 0.497
## Unemployment.2010.4 -0.122 0.451 0.451
## Fertility.Rate.2010 1.000 -0.313 -0.378
## Homicides..2011. 0.046 -0.035 -0.066
## Homicides..2010. 0.041 0.104 0.034
## Infant.mortality.2006 0.143 -0.297 -0.202
## LE_2007_Men -0.313 1.000 0.941
## LE_2007_Women -0.378 0.941 1.000
## Lit.boys -0.387 0.720 0.699
## Lit.girls -0.451 0.694 0.668
## GDP.Per.capita.2007.USD -0.253 0.451 0.461
## HDI2015 -0.496 0.526 0.531
## HDI2014 -0.459 0.530 0.500
## Population -0.047 -0.295 -0.209
## Achievement -0.349 0.620 0.640
## Euro -0.028 0.344 0.313
## Afri -0.222 0.161 0.112
## Amer 0.072 -0.391 -0.349
## HDI.mean -0.478 0.531 0.516
## poverty.mean 0.426 -0.562 -0.581
## unemployment.mean -0.167 0.524 0.501
## homicide.mean.per.cap 0.077 0.092 0.042
## infant.mortaility.per.cap 0.385 -0.301 -0.247
## Fertility.Rate.2010.1 1.000 -0.313 -0.378
## LE_2007_men -0.313 1.000 0.941
## LE_2007_women -0.378 0.941 1.000
## Lit.boys.1 -0.387 0.720 0.699
## Lit.girls.1 -0.451 0.694 0.668
## GDP.per.cap -0.253 0.451 0.461
## S.wiki -0.434 0.717 0.693
## Lit.boys.1 Lit.girls.1 GDP.per.cap S.wiki
## Poverty.Rate.2012 -0.760 -0.755 -0.789 -0.771
## Poverty.Rate.2010 -0.764 -0.750 -0.768 -0.769
## Unemployment.2010.1 0.687 0.673 0.492 0.689
## Unemployment.2010.2 0.691 0.676 0.501 0.692
## Unemployment.2010.3 0.663 0.650 0.507 0.666
## Unemployment.2010.4 0.665 0.666 0.385 0.673
## Fertility.Rate.2010 -0.387 -0.451 -0.253 -0.434
## Homicides..2011. 0.029 0.014 0.194 0.023
## Homicides..2010. 0.075 0.053 0.193 0.066
## Infant.mortality.2006 -0.314 -0.357 -0.403 -0.350
## LE_2007_Men 0.720 0.694 0.451 0.717
## LE_2007_Women 0.699 0.668 0.461 0.693
## Lit.boys 1.000 0.986 0.460 0.994
## Lit.girls 0.986 1.000 0.435 0.998
## GDP.Per.capita.2007.USD 0.460 0.435 1.000 0.462
## HDI2015 0.721 0.740 0.735 0.747
## HDI2014 0.719 0.740 0.718 0.745
## Population -0.180 -0.200 -0.227 -0.199
## Achievement 0.788 0.754 0.529 0.772
## Euro 0.632 0.623 0.379 0.630
## Afri 0.109 0.088 0.059 0.097
## Amer -0.684 -0.671 -0.408 -0.680
## HDI.mean 0.723 0.743 0.729 0.749
## poverty.mean -0.768 -0.758 -0.784 -0.776
## unemployment.mean 0.693 0.682 0.481 0.696
## homicide.mean.per.cap 0.113 0.101 0.145 0.109
## infant.mortaility.per.cap -0.495 -0.540 -0.655 -0.534
## Fertility.Rate.2010.1 -0.387 -0.451 -0.253 -0.434
## LE_2007_men 0.720 0.694 0.451 0.717
## LE_2007_women 0.699 0.668 0.461 0.693
## Lit.boys.1 1.000 0.986 0.460 0.994
## Lit.girls.1 0.986 1.000 0.435 0.998
## GDP.per.cap 0.460 0.435 1.000 0.462
## S.wiki 0.994 0.998 0.462 1.000
wtd.cors(d4)
## S.wiki HDI.mean Achievement Euro Afri Amer
## S.wiki 1.000 0.749 0.77 0.63 0.097 -0.68
## HDI.mean 0.749 1.000 0.56 0.63 0.042 -0.67
## Achievement 0.772 0.563 1.00 0.49 0.234 -0.56
## Euro 0.630 0.631 0.49 1.00 -0.339 -0.98
## Afri 0.097 0.042 0.23 -0.34 1.000 0.16
## Amer -0.680 -0.670 -0.56 -0.98 0.160 1.00
INEG S analysis
The INEG data S analysis.
#Regression method
#all variables
s.fa = fa(s)
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was
## done
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
## In factor.scores, the correlation matrix is singular, an approximation is used
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was
## done
fa_plot_loadings(s.fa)
ggsave("figures/S_self_all_noFD.png")
## Saving 7 x 5 in image
#selected
s2.fa = fa(s2)
fa_plot_loadings(s2.fa)
ggsave("figures/S_self_chosen_noFD.png")
## Saving 7 x 5 in image
#automatically selected
s3.fa = fa(s3)
fa_plot_loadings(s3.fa)
ggsave("figures/S_self_automatic_noFD.png")
## Saving 7 x 5 in image
#Bartlett's method
#all variables
s.fa = fa(s, scores = "Bartlett")
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(R): Matrix was not positive definite, smoothing was
## done
## Warning in cor.smooth(r): Matrix was not positive definite, smoothing was
## done
## The estimated weights for the factor scores are probably incorrect. Try a different factor extraction method.
fa_plot_loadings(s.fa)
ggsave("figures/S_self_all_Bartlett_noFD.png")
## Saving 7 x 5 in image
#selected
s2.fa = fa(s2, scores = "Bartlett")
fa_plot_loadings(s2.fa)
ggsave("figures/S_schosen_Bartlett_noFD.png")
## Saving 7 x 5 in image
#automatically selected
s3.fa = fa(s3, scores = "Bartlett")
fa_plot_loadings(s3.fa)
ggsave("figures/S_self_automatic_Bartlett_noFD.png")
## Saving 7 x 5 in image
#correlate loadings
load1 = data.frame(all = as.vector(s.fa$loadings)); rownames(load1) = rownames(s.fa$loadings)
load2 = data.frame(chosen = as.vector(s2.fa$loadings)); rownames(load2) = rownames(s2.fa$loadings)
load3 = data.frame(automatic = as.vector(s3.fa$loadings)); rownames(load3) = rownames(s3.fa$loadings)
loadings = merge_datasets(load1, load2)
loadings = merge_datasets(loadings, load3)
wtd.cors(loadings)
## all chosen automatic
## all 1 1.00 1.00
## chosen 1 1.00 0.99
## automatic 1 0.99 1.00
Main results
#results
s$S.all = as.vector(s.fa$scores)
s$S.chosen = as.vector(s2.fa$scores)
s$S.automatic = as.vector(s3.fa$scores)
s$S.wiki = d4$S.wiki
s$HDI.mean = d4$HDI.mean
s$Cognitive.ability = d3$Achievement
#s$Euro = d3$Euro #saving this result for the admixture paper
#reorder and save
s = s[order(rownames(s)),] #to match up with admixture paper dataset
write.csv(wtd.cors(s)[48:53,48:53], "results/correlations_noFD.csv")
write.csv(s["S.chosen"], "S factor scores_noFD.csv")
#examine some correlations
wtd.cors(s[c("S.all", "S.chosen", "S.automatic", "S.wiki", "HDI.mean", "Cognitive.ability")]) %>% write_clipboard()
## S all S chosen S automatic S wiki HDI mean
## S all 1.00 0.99 0.99 0.93 0.85
## S chosen 0.99 1.00 0.98 0.93 0.88
## S automatic 0.99 0.98 1.00 0.91 0.89
## S wiki 0.93 0.93 0.91 1.00 0.75
## HDI mean 0.85 0.88 0.89 0.75 1.00
## Cognitive ability 0.78 0.80 0.79 0.77 0.56
## Cognitive ability
## S all 0.78
## S chosen 0.80
## S automatic 0.79
## S wiki 0.77
## HDI mean 0.56
## Cognitive ability 1.00
#plots
#plot all 6 plots automatically
temp = s[str_detect(colnames(s),"S\\.")]
temp$HDI = s$HDI.mean
ggpairs(temp, axisLabels = "none")
## Warning: Removed 3 rows containing non-finite values (stat_density).
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning in (function (data, mapping, alignPercent = 0.6, method =
## "pearson", : Removed 3 rows containing missing values
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
#Scatterplot with names
GG_scatter(s, "Cognitive.ability", "S.chosen")
ggsave("figures/CA_S_chosen_noFD.png")
## Saving 7 x 5 in image
#MCV
#S.all
fa_Jensens_method(s.fa, s, criterion = "Cognitive.ability") +
xlab("Loading on S (all variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_all_noFD.png")
## Saving 7 x 5 in image
#S.chosen
s2$ACH = d3$Achievement
fa_Jensens_method(s2.fa, s2, criterion = "ACH") +
xlab("Loading on S (chosen variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_chosen_noFD.png")
## Saving 7 x 5 in image
#S.automatic
s3$ACH = d3$Achievement
fa_Jensens_method(s3.fa, s3, criterion = "ACH") +
xlab("Loading on S (automatically chosen variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_automatic_noFD.png")
## Saving 7 x 5 in image
#S.wiki
fa_Jensens_method(S.fa, d3, criterion = "Achievement") +
xlab("Loading on S (Wikipedia variables)") +
ylab("Correlation with cognitive ability")
## Using Pearson correlations for the criterion-indicators relationships.
ggsave("figures/MCV_S_wiki_noFD.png")
## Saving 7 x 5 in image