Nothing
SN_test_multiparameter <- function(ts, k, paras_to_test){
n <- length(ts)
no_para <- length(paras_to_test)
D <- inter1 <- inter2 <- c()
for(para in paras_to_test){ # multiparameter test statistics
if(para=='mean'){
mean1 <- mean(ts[1:k])
mean2 <- mean(ts[(k+1):n])
tmp_D <- k*(n-k)/n^1.5*(mean1-mean2)
tmp_inter1 <- cumsum_mean_constrast(ts[1:k],'L')
tmp_inter2 <- cumsum_mean_constrast(ts[(k+1):n],'R')
}
if(para=='variance'){
var1 <- var(ts[1:k])*(k-1)/k
var2 <- var(ts[(k+1):n])*(n-k-1)/(n-k)
tmp_D <- k*(n-k)/n^1.5*(var1-var2)
tmp_inter1 <- cumsum_variance_constrast(ts[1:k],'L')
tmp_inter2 <- cumsum_variance_constrast(ts[(k+1):n],'R')
}
if(para=='acf'){
acf1 <- cpp_acf(ts[1:k])
acf2 <- cpp_acf(ts[(k+1):n])
tmp_D <- k*(n-k)/n^1.5*(acf1-acf2)
tmp_inter1 <- cumsum_acf_constrast(ts[1:k],'L')
tmp_inter1[is.na(tmp_inter1)] <- 0 # prevent the rare case where x1=x2 at the begining of the series
tmp_inter2 <- cumsum_acf_constrast(ts[(k+1):n],'R')
tmp_inter2[is.na(tmp_inter2)] <- 0 # prevent the rare case where x1=x2 at the begining of the series
}
# if(is.numeric(para)){ # quantile
if(!is.na(as.numeric(para))){
para <- as.numeric(para)
q <- para
quantile1 <- quantile(ts[1:k],q)
quantile2 <- quantile(ts[(k+1):n],q)
tmp_D <- k*(n-k)/n^1.5*(quantile1-quantile2)
tmp_inter1 <- cumsum_quantile_constrast_Cpp(ts[1:k],'L',q)
tmp_inter2 <- cumsum_quantile_constrast_Cpp(ts[(k+1):n],'R',q)
}
D <- c(D, tmp_D)
inter1 <- cbind(inter1, tmp_inter1)
inter2 <- cbind(inter2, tmp_inter2)
}
multiplier1 <- ((1:k)*((k-1):0))^2/n^2/k^2
multiplier2 <- ((0:(n-k-1))*((n-k):1))^2/n^2/(n-k)^2
M1 <- M2 <- matrix(0, no_para, no_para)
for(index1 in 1:(no_para-1)){
for(index2 in (index1+1):no_para){
M1[index1, index2] <- M1[index2, index1] <- sum(inter1[,index1]*inter1[,index2]*multiplier1)
M2[index1, index2] <- M2[index2, index1] <- sum(inter2[,index1]*inter2[,index2]*multiplier2)
}
}
for(index1 in 1:no_para){
M1[index1, index1] <- sum(inter1[,index1]^2*multiplier1)
M2[index1, index1] <- sum(inter2[,index1]^2*multiplier2)
}
test_SN <- t(D)%*%solve(M1+M2)%*%D
return(test_SN)
}
SN_sweep_multiparameter <- function(data, grid_size, paras_to_test){
n <- length(data)
substat <- list()
substat[1:(grid_size-1)] <- NA
for (k in grid_size:(n-grid_size)){
pre_grid_no <- floor(k/grid_size)
post_grid_no <- floor((n-k)/grid_size)
pre_grid_sets <- k-(pre_grid_no:1)*grid_size+1
post_grid_sets <- k+(1:post_grid_no)*grid_size
sn_grid_stat <- c()
for(pre_grid_position in pre_grid_sets){
for(post_grid_position in post_grid_sets){
sn_grid_stat <- rbind(sn_grid_stat,
c(SN_test_multiparameter(ts=data[pre_grid_position:post_grid_position], k=k-pre_grid_position+1, paras_to_test=paras_to_test),
pre_grid_position, k, post_grid_position))
}
}
substat[[k]] <- sn_grid_stat
}
substat[(n-grid_size+1):n] <- NA
return(substat)
}
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