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### ANALYTIC REPLICATIONS OF NOAH CARL'S Explaining Terrorism Threat Level Across Western Countries
# libs --------------------------------------------------------------------
library(pacman)
p_load(XLConnect, kirkegaard, psych, weights, magrittr, effsize, lsr, compute.es, MASS)
# data --------------------------------------------------------------------
d_main = readWorksheetFromFile(file = "Terrorism Threat.xlsx", sheet = 2)
rownames(d_main) = d_main$country
# derived variables ------------------------------------------
#dummies
d_main$west = as.logical(d_main$west)
d_main$europe = as.logical(d_main$europe)
d_main$turkey = as.logical(d_main$turkey)
#muslim 2015 estimation
d_main$muslim15 = d_main$muslim10 + .25 * (d_main$muslim30 - d_main$muslim10)
#death count
d_main$deaths2 = log(1 + d_main$deaths)
#log gdp
d_main$gdp_log = log(d_main$gdpcap14)
#factors
d_main$any_num = d_main$any
d_main$any = as.factor(d_main$any)
d_main$part = as.factor(d_main$part)
d_main$terror_f = as.factor(d_main$terror)
#winzor turkey's value
d_main["Turkey", "muslim15"] = 19.075
# subsets -----------------------------------------------------------------
d_west = d_main[d_main$west, ]
d_euro = d_main[d_main$europe, ]
# descriptive -------------------------------------------------------------
#threat level
psych::describe(d_west$terror)
#muslims
psych::describe(d_west$muslim15)
#deaths
psych::describe(d_west$deaths)
#any
table(d_west$any)
#against isis
table(d_west$part)
# cors --------------------------------------------------------------------
wtd.cors(d_west)
cor.test(d_west$terror, d_west$muslim15)
cor.test(d_west$terror, d_west$deaths2)
# simple lm's ---------------------------------------------------------------------
lm("terror ~ poly(muslim15, 1)", data = d_west) %>% summary()
lm("terror ~ poly(muslim15, 2)", data = d_west) %>% summary()
lm("terror ~ poly(deaths2, 1)", data = d_west) %>% summary()
lm("terror ~ poly(deaths2, 2)", data = d_west) %>% summary()
# SMD ---------------------------------------------------------------------
#calculate the d
SMD_matrix(d_west$terror, d_west$any)
cohen.d(d = d_west$terror, f = as.factor(d_west$any))
SMD_matrix(d_west$terror, d_west$part)
cohen.d(d = d_west$terror, f = as.factor(d_west$part))
#try to get a p value
des(d = cohen.d(d = d_west$terror, f = d_west$any)$estimate, n.1 = 21, n.2 = 7)
des(d = cohen.d(d = d_west$terror, f = d_west$part)$estimate, n.1 = 21, n.2 = 7)
# complex lm's ------------------------------------------------------------
#all west
d_betas = MOD_APSLM(dependent = "terror", predictors = c("muslim15", "any", "deaths2", "part", "gdp_log", "unemp14", "ineq0911"), data = d_west)[[1]] %>% round(2)
#the models we want
d_betas[c(8:10, 108, 112, 113), ]
#all oecd
d_betas = MOD_APSLM(dependent = "terror", predictors = c("muslim15", "any", "deaths2", "part", "gdp_log", "unemp14", "ineq0911"), data = d_main)[[1]] %>% round(2)
#the models we want
d_betas[c(8:10, 108, 112, 113), ]
#europe only
d_betas = MOD_APSLM(dependent = "terror", predictors = c("muslim15", "any", "deaths2", "part", "gdp_log", "unemp14", "ineq0911"), data = d_euro)[[1]] %>% round(2)
#the models we want
d_betas[c(8:10, 108, 112, 113), ]
#logistic
polr("terror_f ~ muslim15 + any", data = d_west) %>% summary()
polr("terror_f ~ muslim15 + deaths2", data = d_west) %>% summary()
polr("terror_f ~ muslim15 + part", data = d_west) %>% summary()
#lasso
d_betas_lasso = MOD_repeat_cv_glmnet(df = d_west, dependent = "terror", predictors = c("muslim15", "any", "deaths2", "part", "gdp_log", "unemp14", "ineq0911"), runs = 500)
MOD_summarize_models(d_betas_lasso)
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