Progress Report Project 2
The Effect of Diaspora on Civil Society in Developing Countries
Dasha Djukic-Min
4/27/2022
Clear memory
rm(list=ls())Load data: Civil Society Data (csdata)
csdata <- read.csv("/Users/dasha/Desktop/NEW1.csv")Check for missing values
is.na(csdata)## Country Year No_CSO_Increased. Rmtnc_Inflow_MIL GDP_Per_Capita
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## [200,] FALSE FALSERecode missing vaues into NA
csdata$Polity2[csdata$v1==' '] <- NARemove NA values
csdata1 <- na.omit(csdata) Names of variables
names(csdata1)## [1] "Country" "Year" "No_CSO_Increased."
## [4] "Rmtnc_Inflow_MIL" "GDP_Per_Capita" "Population"
## [7] "Polity2"Browse data
View(csdata1)Summary
summary(csdata1)## Country Year No_CSO_Increased. Rmtnc_Inflow_MIL
## Length:180 Min. :2005 Min. :0.0000 Min. : 0.00
## Class :character 1st Qu.:2005 1st Qu.:0.0000 1st Qu.: 65.68
## Mode :character Median :2015 Median :0.0000 Median : 500.74
## Mean :2010 Mean :0.4889 Mean : 3141.41
## 3rd Qu.:2015 3rd Qu.:1.0000 3rd Qu.: 2216.02
## Max. :2015 Max. :1.0000 Max. :68790.20
## GDP_Per_Capita Population Polity2
## Min. : 0 Min. :6.500e+01 Min. :-9.000e+00
## 1st Qu.: 811 1st Qu.:3.287e+06 1st Qu.:-3.000e+00
## Median : 1991 Median :1.107e+07 Median : 5.000e+00
## Mean : 5067340 Mean :5.691e+07 Mean : 1.332e+08
## 3rd Qu.: 4226 3rd Qu.:3.391e+07 3rd Qu.: 8.000e+00
## Max. :846741711 Max. :1.368e+09 Max. : 1.778e+10Create a new data subset that excludes non-numeric variable (country)
Then plot the data with smaller dots
cs <- csdata1[,c("No_CSO_Increased.", "Rmtnc_Inflow_MIL", "GDP_Per_Capita", "Population", "Polity2")]
pairs(cs,col=cs$No_CSO_Increased.,cex=.5)
Logistic regression
# Fit a logistic regression model to predict "Increase in Number of CSOs" using Remittances, Democracy, GDP/Capita and Population
glm.fit=glm(No_CSO_Increased.~Rmtnc_Inflow_MIL+GDP_Per_Capita+Population+Polity2, data=csdata1,family=binomial)## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurredsummary(glm.fit)##
## Call:
## glm(formula = No_CSO_Increased. ~ Rmtnc_Inflow_MIL + GDP_Per_Capita +
## Population + Polity2, family = binomial, data = csdata1)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8376 -1.1099 -0.1582 1.1301 1.6850
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.278e-01 2.484e-01 0.514 0.60693
## Rmtnc_Inflow_MIL -1.079e-05 4.515e-05 -0.239 0.81116
## GDP_Per_Capita -1.737e-04 6.508e-05 -2.670 0.00760 **
## Population 1.112e-08 5.135e-09 2.165 0.03040 *
## Polity2 1.818e-06 6.947e-07 2.616 0.00889 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 249.44 on 179 degrees of freedom
## Residual deviance: 225.13 on 175 degrees of freedom
## AIC: 235.13
##
## Number of Fisher Scoring iterations: 16Predict function
# I use the predict function to predict the probability of an increase in the number of CSOs, given values of the predictors.
glm.probs=predict(glm.fit,type="response")
glm.probs[1:5]## 2 3 4 5 6
## 0.5987587 0.4264919 0.3458949 0.4943204 0.4359150# I coded "Increase in CSOs" as 1, so glm.probs are the probabilities of No_CSO_Increased. going up. Here I create actual labels instead of probabilities.
glm.pred=ifelse(glm.probs>0.5,"Increase","No Increase")
# Confusion matrix
attach(csdata1)
table(glm.pred,No_CSO_Increased.) # predictions vs. truth## No_CSO_Increased.
## glm.pred 0 1
## Increase 37 54
## No Increase 55 34Check accuracy / Compute the fraction of No_CSO_Increased for which the prediction was correct
mean(glm.pred==No_CSO_Increased.)## [1] 0Split the data into training and test sets
# Create a vector corresponding to the observations from 2005
train = Year<2015
# Fit the logistic regression using only the train data
glm.fit=glm(No_CSO_Increased.~Rmtnc_Inflow_MIL+GDP_Per_Capita+Population+Polity2, data=csdata1,family=binomial, subset=train)
# Test the model by fitting the test data into the fitted model
glm.probs=predict(glm.fit,newdata=csdata1[!train,],type="response")
# Compute the predictions for 2015 and compare them to the actual
# increase in number of CSOs over that time period
glm.pred=ifelse(glm.probs >0.5,"1","0")
No_CSO_Increased..2015=csdata1$No_CSO_Increased[!train]
table(glm.pred,No_CSO_Increased..2015)## No_CSO_Increased..2015
## glm.pred 0 1
## 0 20 7
## 1 44 20Test error rate
mean(glm.pred==No_CSO_Increased..2015)## [1] 0.4395604Training error rate
mean(glm.pred != No_CSO_Increased..2015)## [1] 0.5604396Check accuracy rate
20/(44+20)## [1] 0.3125Plot remittances
plot(Rmtnc_Inflow_MIL)