library(Zelig)
data(coalition)
data(turnout)
data(macro)
data(sanction)
cluster <- c(rep(c(1:62),5), rep(c(63),4))
coalition$cluster <- cluster
z.out <- zelig(duration ~ fract + numst2,
id = "cluster",
model = "gamma.gee",
data = coalition,
corstr="exchangeable"
)
summary(z.out)
# Setting the explanatory variables at their default values
# (mode for factor variables and mean for non-factor variables),
# with numst2 set to the vector 0 = no crisis, 1 = crisis.
x.low <- setx(z.out, numst2 = 0)
x.high <- setx(z.out, numst2 = 1)
# Simulate quantities of interest
s.out <- sim(z.out, x = x.low, x1 = x.high)
summary(s.out)
# Generate a plot of quantities of interest:
plot(s.out)
## Attaching the sample turnout dataset:
turnout$cluster <- rep(c(1:200),10)
sorted.turnout <- turnout[order(turnout$cluster),]
z.out1 <- zelig(
vote ~ race + educate, model = "logit.gee",
id = "cluster",
data = turnout,
corstr = "stat_M_dep",
Mv=3
)
summary(z.out1)
x.out1 <- setx(z.out1)
s.out1 <- sim(z.out1, x = x.out1)
summary(s.out1)
plot(s.out1)
x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
summary(s.out2)
plot(s.out2)
##### Example 3: Example with Fixed Correlation Structure
## User-defined correlation structure
corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
diag(corr.mat) <- 1
## Generating empirical estimates:
z.out2 <- zelig(vote ~ race + educate, model = "logit.gee", id = "cluster",
data = sorted.turnout, robust = T, corstr = "fixed", R=corr.mat)
## Viewing the regression output:
summary(z.out2)
# NORMAL.GEE
# NORMAL.GEE
# NORMAL.GEE
z.out <- zelig(unem ~ gdp + capmob + trade, model = "normal.gee", id = "country", data = macro, robust=TRUE, corstr="AR-M", Mv=1)
summary(z.out)
# Set explanatory variables to their default (mean/mode) values, with
# high (80th percentile) and low (20th percentile) values:
x.high <- setx(z.out, trade = quantile(macro$trade, 0.8))
x.low <- setx(z.out, trade = quantile(macro$trade, 0.2))
# Generate first differences for the effect of high versus low trade on
# GDP:
s.out <- sim(z.out, x = x.high, x1 = x.low)
summary(s.out)
# Generate a plot of quantities of interest:
plot(s.out)
#
#
#
sanction$cluster <- c(rep(c(1:15),5),rep(c(16),3))
z.out <- zelig(num ~ target + coop, model = "poisson.gee", id = "cluster", data = sanction, robust=TRUE, corstr="exchangeable")
summary(z.out)
x.out <- setx(z.out)
s.out <- sim(z.out, x = x.out)
summary(s.out)
plot(s.out)
turnout$cluster <- rep(c(1:200),10)
z.out1 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster",
data = turnout, robust = T, corstr = "stat_M_dep", Mv=3)
summary(z.out1)
x.out1 <- setx(z.out1)
s.out1 <- sim(z.out1, x = x.out1)
plot(s.out1)
x.high <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.75))
x.low <- setx(z.out1, educate = quantile(turnout$educate, prob = 0.25))
s.out2 <- sim(z.out1, x = x.high, x1 = x.low)
summary(s.out2)
plot(s.out2)
##### Example 3: Example with Fixed Correlation Structure
## User-defined correlation structure
corr.mat <- matrix(rep(0.5,100), nrow=10, ncol=10)
diag(corr.mat) <- 1
## Generating empirical estimates:
z.out2 <- zelig(vote ~ race + educate, model = "probit.gee", id = "cluster",
data = turnout, robust = T, corstr = "fixed", R=corr.mat)
## Viewing the regression output:
summary(z.out2)
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