library(rio)
mortalidad = import("https://docs.google.com/spreadsheets/d/14atPTrwM6x5j49Ui7dKApHOqR1rNT2y5U4PeX29GRbc/edit#gid=1122338962")
head(mortalidad)
## sex padreSector fechaNacimiento edadDejaEstudio muere naceFueraMatri
## 1 male Agricultura 1853-05-23 15.000 0 no
## 2 male Agricultura 1853-07-19 15.000 0 no
## 3 male NoFijo 1861-11-17 15.000 0 no
## 4 male Agricultura 1872-11-16 15.000 0 no
## 5 female NoFijo 1855-07-19 0.559 1 no
## 6 male Agricultura 1855-09-29 0.315 1 no
## madreEdad
## 1 35.009
## 2 30.609
## 3 29.320
## 4 41.183
## 5 42.138
## 6 32.931
dataAdmi = import("https://docs.google.com/spreadsheets/d/1aq7pz3W7QOjmMkmz2T5-qEOAX8y1t99F2bdDIfw94OA/edit#gid=59881833")
head(dataAdmi)
## admitido gre gpa prestigio
## 1 no 380 3.61 Bajo
## 2 si 660 3.67 Bajo
## 3 si 800 4.00 MuyAlto
## 4 si 640 3.19 MuyBajo
## 5 no 520 2.93 MuyBajo
## 6 si 760 3.00 Alto
library(rio)
GPI <- import("GPI.xlsx")
head(GPI)
## Country iso3c Safety Milita Conflicto
## 1 Afghanistan AFG 4.127 2.472 3.650
## 2 Albania ALB 2.120 1.666 1.403
## 3 Algeria DZA 2.302 2.041 2.068
## 4 Angola AGO 2.413 1.706 1.666
## 5 Argentina ARG 2.656 1.611 1.201
## 6 Armenia ARM 1.977 2.041 1.990
library(rio)
et <- import("Ecological threat.xlsx")
## New names:
## * `` -> `...6`
## * `` -> `...7`
## * `` -> `...8`
## * `` -> `...9`
## * `` -> `...10`
## * `` -> `...11`
## * `` -> `...12`
## * `` -> `...13`
## * `` -> `...14`
## * `` -> `...15`
## * `` -> `...16`
## * `` -> `...17`
## * `` -> `...18`
## * `` -> `...19`
## * `` -> `...20`
## * `` -> `...21`
## * `` -> `...22`
## * `` -> `...23`
## * `` -> `...24`
## * `` -> `...25`
head(et)
## Country Food Risk Score Natural Disasters Score
## 1 Burundi 5 5
## 2 Central African Republic 5 5
## 3 Republic of the Congo 5 5
## 4 Kenya 5 5
## 5 Mozambique 5 5
## 6 Malawi 5 5
## Rapid Population Growth Score Water Risk Score ...6 ...7 ...8 ...9 ...10
## 1 5 5 NA NA NA NA NA
## 2 5 5 NA NA NA NA NA
## 3 5 5 NA NA NA NA NA
## 4 5 5 NA NA NA NA NA
## 5 5 5 NA NA NA NA NA
## 6 5 5 NA NA NA NA NA
## ...11 ...12 ...13 ...14 ...15 ...16 ...17 ...18 ...19 ...20 ...21 ...22 ...23
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## ...24 ...25
## 1 NA NA
## 2 NA NA
## 3 NA NA
## 4 NA NA
## 5 NA NA
## 6 NA NA
control3=merge(et,GPI)
str(control3)
## 'data.frame': 163 obs. of 29 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ Food Risk Score : num 4 2 2 5 2 2 1 1 2 2 ...
## $ Natural Disasters Score : num 5 4 5 3 4 2 4 3 1 1 ...
## $ Rapid Population Growth Score: num 5 1 3 5 2 1 3 1 1 3 ...
## $ Water Risk Score : num 5 3 3 5 3 4 3 1 5 3 ...
## $ ...6 : logi NA NA NA NA NA NA ...
## $ ...7 : logi NA NA NA NA NA NA ...
## $ ...8 : logi NA NA NA NA NA NA ...
## $ ...9 : logi NA NA NA NA NA NA ...
## $ ...10 : logi NA NA NA NA NA NA ...
## $ ...11 : logi NA NA NA NA NA NA ...
## $ ...12 : logi NA NA NA NA NA NA ...
## $ ...13 : logi NA NA NA NA NA NA ...
## $ ...14 : logi NA NA NA NA NA NA ...
## $ ...15 : logi NA NA NA NA NA NA ...
## $ ...16 : logi NA NA NA NA NA NA ...
## $ ...17 : logi NA NA NA NA NA NA ...
## $ ...18 : logi NA NA NA NA NA NA ...
## $ ...19 : logi NA NA NA NA NA NA ...
## $ ...20 : logi NA NA NA NA NA NA ...
## $ ...21 : logi NA NA NA NA NA NA ...
## $ ...22 : logi NA NA NA NA NA NA ...
## $ ...23 : logi NA NA NA NA NA NA ...
## $ ...24 : logi NA NA NA NA NA NA ...
## $ ...25 : logi NA NA NA NA NA NA ...
## $ iso3c : chr "AFG" "ALB" "DZA" "AGO" ...
## $ Safety : num 4.13 2.12 2.3 2.41 2.66 ...
## $ Milita : num 2.47 1.67 2.04 1.71 1.61 ...
## $ Conflicto : num 3.65 1.4 2.07 1.67 1.2 ...
control3= control3[,-c(6:26)]
str(control3)
## 'data.frame': 163 obs. of 8 variables:
## $ Country : chr "Afghanistan" "Albania" "Algeria" "Angola" ...
## $ Food Risk Score : num 4 2 2 5 2 2 1 1 2 2 ...
## $ Natural Disasters Score : num 5 4 5 3 4 2 4 3 1 1 ...
## $ Rapid Population Growth Score: num 5 1 3 5 2 1 3 1 1 3 ...
## $ Water Risk Score : num 5 3 3 5 3 4 3 1 5 3 ...
## $ Safety : num 4.13 2.12 2.3 2.41 2.66 ...
## $ Milita : num 2.47 1.67 2.04 1.71 1.61 ...
## $ Conflicto : num 3.65 1.4 2.07 1.67 1.2 ...
dontselect=c("Country")
select=setdiff(names(control3),dontselect)
theData=control3[,select]
library(polycor)
library(ggcorrplot)
## Loading required package: ggplot2
corMatrix=polycor::hetcor(theData)$correlations
ggcorrplot(corMatrix)
library(psych)
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
## The following object is masked from 'package:polycor':
##
## polyserial
psych::KMO(corMatrix)
## Kaiser-Meyer-Olkin factor adequacy
## Call: psych::KMO(r = corMatrix)
## Overall MSA = 0.76
## MSA for each item =
## Food Risk Score Natural Disasters Score
## 0.79 0.87
## Rapid Population Growth Score Water Risk Score
## 0.76 0.79
## Safety Milita
## 0.73 0.69
## Conflicto
## 0.71
library(matrixcalc)
cortest.bartlett(corMatrix,n=nrow(theData))$p.value>0.05
## [1] FALSE
is.singular.matrix(corMatrix)
## [1] FALSE
fa.parallel(theData, fa = 'fa',correct = T,plot = F)
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Parallel analysis suggests that the number of factors = 2 and the number of components = NA
library(GPArotation)
resfa <- fa(theData,
nfactors = 3,
cor = 'mixed',
rotate = "varimax",
fm="minres")
## Warning in fac(r = r, nfactors = nfactors, n.obs = n.obs, rotate = rotate, : An
## ultra-Heywood case was detected. Examine the results carefully
print(resfa$loadings)
##
## Loadings:
## MR3 MR1 MR2
## Food Risk Score 0.690 0.577 0.129
## Natural Disasters Score 0.116 0.431
## Rapid Population Growth Score 0.943 0.204 0.266
## Water Risk Score 0.668 0.720
## Safety 0.308 0.735 0.502
## Milita 0.586
## Conflicto 0.210 0.402 0.749
##
## MR3 MR1 MR2
## SS loadings 1.965 1.780 1.255
## Proportion Var 0.281 0.254 0.179
## Cumulative Var 0.281 0.535 0.714
print(resfa$loadings,cutoff = 0.5)
##
## Loadings:
## MR3 MR1 MR2
## Food Risk Score 0.690 0.577
## Natural Disasters Score
## Rapid Population Growth Score 0.943
## Water Risk Score 0.668 0.720
## Safety 0.735 0.502
## Milita 0.586
## Conflicto 0.749
##
## MR3 MR1 MR2
## SS loadings 1.965 1.780 1.255
## Proportion Var 0.281 0.254 0.179
## Cumulative Var 0.281 0.535 0.714
fa.diagram(resfa,main = "Resultados del EFA")
