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# rdmulti: analysis of RD designs with multiple cutoffs or scores
# Illustration file
# Authors: Matias Cattaneo, Rocio Titiunik, Gonzalo Vazquez-Bare
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rm(list = ls())

library(rdmulti)


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# Multiple cutoffs
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data <- read.csv('simdata_multic.csv')
Y <- data$y
X <- data$x
C <- data$c

aux <- rdmc(Y,X,C)
aux <- rdmc(Y,X,C,pooled_opt=paste('h=20','p=2',sep=','),verbose=TRUE)
aux <- rdmc(Y,X,C,h=c(11,10))
aux <- rdmc(Y,X,C,bwselect=c('msetwo','certwo'))

## Add four covariates

Z <- matrix(rnorm(4*length(Y)),ncol=4)

# Including all covariates in each cutoff

aux <- rdmc(Y,X,C,covs_mat=Z)

# Use covariates Z1 and Z2 in cutoff 1, all four covariates in cutoff 2

covlist <- list(c(1,2),seq(1,4,by=1))
aux <- rdmc(Y,X,C,covs_mat=Z,covs_list=covlist)

# Add weights

wvar <- runif(length(Y),0.8,1.2)
aux <- rdmc(Y,X,C,weightsvec=rep("wvar",2))


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# Plots
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aux <- rdmcplot(Y,X,C)
aux <- rdmcplot(Y,X,C,nobins=TRUE)
aux <- rdmcplot(Y,X,C,h=c(11,12),pvec=c(1,1))
aux <- rdmcplot(Y,X,C,covs_mat=Z,covs_list=covlist)


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# Cumulative cutoffs
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data <- read.csv('simdata_cumul.csv')
Y <- data$y
X <- data$x
cvec <- c(data$c[1],data$c[2])

aux <- rdms(Y,X,cvec)
aux <- rdms(Y,X,cvec,h=c(11,8),kernel=c('uniform','triangular'))
aux <- rdms(Y,X,cvec,range = matrix(c(0,33.5,65.5,100),ncol=2))
cutoff <- cvec[1]*(X<=49.5) + cvec[2]*(X>49.5)
aux <- rdmc(Y,X,cutoff)

aux <- rdmcplot(Y,X,cutoff)

## Add four covariates

Z <- matrix(rnorm(4*length(Y)),ncol=4)

# Including all covariates in each cutoff

aux <- rdms(Y,X,cvec,covs_mat=Z)

# Use covariates Z1 and Z2 in cutoff 1, all four covariates in cutoff 2

covlist <- list(c(1,2),seq(1,4,by=1))
aux <- rdms(Y,X,cvec,covs_mat=Z,covs_list=covlist)

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# Bivariate score
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data <- read.csv('simdata_multis.csv')
Y <- data$y
X1 <- data$x1
X2 <- data$x2
zvar <- data$t
cvec <- c(data$c1[1],data$c1[2],data$c1[3])
cvec2 <- c(data$c2[1],data$c2[2],data$c2[3])

aux <- rdms(Y,X1,cvec,X2,zvar,cvec2)
aux <- rdms(Y,X1,cvec,X2,zvar,cvec2,h=c(15,13,17))
xnorm <- apply(cbind(abs(50-X1),abs(50-X2)),1,min)*(2*zvar-1)
aux <- rdms(Y,X1,cvec,X2,zvar,cvec2,xnorm=xnorm)

## Add four covariates

Z <- matrix(rnorm(4*length(Y)),ncol=4)

# Including all covariates in each cutoff

aux <- rdms(Y,X1,cvec,X2,zvar,cvec2,covs_mat=Z)

# Use covariates Z1 and Z2 in cutoff 1, all four covariates in cutoff 2, covariates 1 and 3 in cutoff 3

covlist <- list(c(1,2),seq(1,4,by=1),c(1,3))
aux <- rdms(Y,X1,cvec,X2,zvar,cvec2,covs_mat=Z,covs_list=covlist)