anal.r
setwd('~/uhk/dev/ar_ondrej_plachy/institution/')
data = read.table('data.tsv',sep="\t",header=TRUE)
data$time = data$year - 1983
data$random1 = rnorm(336, mean=0, sd=0.01)
data$random2 = rnorm(336, mean=0, sd=0.01)
attach(data)
fbalm2 =
ifelse(balance_of_parties_mandates<= 0.25, '0.0-0.25',
ifelse(balance_of_parties_mandates <= 0.5, '0.25-0.5',
ifelse(balance_of_parties_mandates <= 0.75, '0.5-0.75',
'0.75-1.0')))
model0a = glm(event~time + provincia + institution, family="binomial")
model1a2 = glm(event~time + provincia + fbalm2 + institution, family="binomial")
summary(model1a2)
#likelihood ratio test
library(epicalc)
lrtest (model0a, model1a2)
model1a = glm(event~time + provincia + balance_of_parties + institution, family="binomial")
model1b = glm(event~time + provincia + effective_number_of_parties + institution, family="binomial")
model1c = glm(event~time + provincia + raes_index + institution, family="binomial")
model2a = glm(event~time + provincia + balance_of_parties + effective_number_of_parties + institution, family="binomial")
model2b = glm(event~time + provincia + balance_of_parties + raes_index + institution, family="binomial")
model3a = glm(event~time + provincia + balance_of_parties + effective_number_of_parties + institution + balance_of_parties*effective_number_of_parties, family="binomial")
model3b = glm(event~time + provincia + balance_of_parties + raes_index + institution + balance_of_parties*effective_number_of_parties, family="binomial")
library("splines", lib.loc="/usr/lib/R/library")
model4a = glm(event~time + provincia + bs(balance_of_parties) + bs(effective_number_of_parties) + institution + bs(balance_of_parties)*bs(effective_number_of_parties), family="binomial")
#factors by quantiles
install.packages("gtools")
library("gtools", lib.loc="/home/michal/R/x86_64-pc-linux-gnu-library/3.1")
fbalm = quantcut(balance_of_parties_mandates, q=c(0,0.25,0.5,0.75,1))
fbalm2 =
ifelse(balance_of_parties_mandates<= 0.2, '0.0-0.2',
ifelse(balance_of_parties_mandates <= 0.4, '0.2-0.4',
ifelse(balance_of_parties_mandates <= 0.6, '0.4-0.6',
ifelse(balance_of_parties_mandates <= 0.8, '0.6-0.8',
'0.8-1.0'))))
fbalm2 =
ifelse(balance_of_parties_mandates<= 0.25, '0.0-0.25',
ifelse(balance_of_parties_mandates <= 0.5, '0.25-0.5',
ifelse(balance_of_parties_mandates <= 0.75, '0.5-0.75',
'0.75-1.0')))
model1a = glm(event~time + provincia + fbalm + institution, family="binomial")
model1a2 = glm(event~time + provincia + fbalm2 + institution, family="binomial")
model0a = glm(event~time + provincia + institution, family="binomial")
# http://www.cookbook-r.com/Graphs/Scatterplots_%28ggplot2%29/
library("ggplot2", lib.loc="/home/michal/R/x86_64-pc-linux-gnu-library/3.1")
ggplot(data,aes(x=balance_of_parties+random1,y=effective_number_of_parties+random2,color=as.factor(event))) + geom_point(shape=1)
ggplot(data,aes(x=balance_of_parties+random1,y=effective_number_of_parties+random2, color=as.factor(event), size=as.factor(event))) + geom_point(shape=1)
# http://stackoverflow.com/questions/11875941/3d-equivalent-of-the-curve-function-in-r
library(emdbook)
curve3d(-2.4966 -11.69859*x - 2.54043*y + 5.44407*x*y,from=c(0,0), to=c(1,5),sys3d=c("contour"))
curve3d(exp(-2.4966 -11.69859*x - 2.54043*y + 5.44407*x*y),from=c(0,0), to=c(1,5),sys3d=c("contour"))
#library("splines", lib.loc="/usr/lib/R/library")
termplot(model3b)
dev.off()
# contour lines:
zmodel3b = function(x,y) { #balance_of_parties, effective_number_of_parties
return (-2.4966 -11.69859*x - 2.54043*y + 5.44407*x*y)
}
x = seq(0.15, 1.025, by = 0.05)
y = seq(0.5, 4.5, by = 0.2)
z = array(0,c(length(x),length(y)))
i = 1
j = 1
for (iks in x) {
for (yps in y) {
z[i,j] = zmodel3b(iks,yps)
j = j + 1
}
i = i + 1
j = 1
}
a = contourLines(x,y,z)
xy = paste(unlist(lapply(a, function(z) {
xs = paste(round(z$x, 3), collapse = ",")
ys = paste(round(z$y, 3), collapse = ",")
sprintf("{\n \"x\": [%s],\n \"y\": [%s]\n}", xs, ys)
})), collapse = ", \n")
cat("<script>", sprintf("var data = [%s]", xy), "</script>", sep = "\n")
# minus controled
data$event_minus_controled = exp(resid(model3b,type="resp") + (-11.69859*balance_of_parties - 2.54043*effective_number_of_parties + 5.44407*balance_of_parties*effective_number_of_parties))
data$fitted = model3b$fitted
data$fitted_minus_controled = exp(-11.69859*balance_of_parties - 2.54043*effective_number_of_parties + 5.44407*balance_of_parties*effective_number_of_parties)
write.csv(data,file="data.csv")
#likelihood ratio test
library(epicalc)
lrtest (model0a, model1a)
contour_lines.json
[{
"x": [0.15,0.151],
"y": [4.495,4.5]
},
{
"x": [0.917,0.95,0.979,1],
"y": [0.5,0.612,0.7,0.756]
},
{
"x": [0.15,0.18,0.2,0.207,0.229],
"y": [3.915,4.1,4.246,4.3,4.5]
},
{
"x": [0.806,0.85,0.853,0.9,0.914,0.95,1,1],
"y": [0.5,0.69,0.7,0.858,0.9,0.992,1.1,1.1]
},
{
"x": [0.15,0.189,0.2,0.225,0.25,0.252,0.274,0.292,0.3,0.307],
"y": [3.335,3.5,3.557,3.7,3.882,3.9,4.1,4.3,4.402,4.5]
},
{
"x": [0.694,0.7,0.726,0.75,0.767,0.8,0.825,0.85,0.9,0.909,0.95,1],
"y": [0.5,0.54,0.7,0.824,0.9,1.022,1.1,1.169,1.282,1.3,1.372,1.445]
},
{
"x": [0.15,0.2,0.211,0.25,0.265,0.3,0.3,0.325,0.343,0.35,0.357,0.368,0.377,0.385],
"y": [2.755,2.868,2.9,3.034,3.1,3.3,3.3,3.5,3.7,3.794,3.9,4.1,4.3,4.5]
},
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"y": [0.5,0.7,0.71,0.9,1.1,1.103,1.3,1.327,1.472,1.5,1.573,1.648,1.7,1.706,1.752,1.789]
},
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"y": [2.175,2.179,2.186,2.198,2.219,2.271,2.3,2.5,2.638,2.7,2.9,3.1,3.3,3.5,3.7,3.9,4.1,4.3,4.5]
},
{
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"y": [0.5,0.7,0.9,1.1,1.3,1.5,1.7,1.9,1.905,2.051,2.088,2.1,2.104,2.114,2.12,2.124,2.128,2.13,2.132,2.134]
},
{
"x": [0.36,0.35,0.345,0.326,0.3,0.299,0.26,0.25,0.2,0.196,0.15],
"y": [0.5,0.644,0.7,0.9,1.095,1.1,1.3,1.339,1.491,1.5,1.594]
},
{
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"y": [2.478,2.5,2.512,2.554,2.607,2.675,2.7,2.768,2.9,2.901,3.1,3.106,3.3,3.465,3.5,3.7,3.9,4.1,4.255,4.3,4.5]
},
{
"x": [0.249,0.219,0.2,0.179,0.15],
"y": [0.5,0.7,0.802,0.9,1.014]
},
{
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"y": [2.822,2.892,2.9,2.978,3.086,3.1,3.226,3.3,3.417,3.5,3.688,3.7,3.9,4.1,4.108,4.3,4.5]
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{
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}]