[Yuima-commits] r785 - pkg/yuima/R
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Fri Jan 28 03:44:00 CET 2022
Author: kyuta
Date: 2022-01-28 03:43:59 +0100 (Fri, 28 Jan 2022)
New Revision: 785
Added:
pkg/yuima/R/wllag.R
Removed:
pkg/yuima/R/wllag.R
Log:
replaced wllag.R to fix a svn issue
Deleted: pkg/yuima/R/wllag.R
===================================================================
--- pkg/yuima/R/wllag.R 2022-01-28 02:06:02 UTC (rev 784)
+++ pkg/yuima/R/wllag.R 2022-01-28 02:43:59 UTC (rev 785)
@@ -1,318 +0,0 @@
-# Scale-by-scale lead-lag estimation by wavelets
-
-## function to compute Daubechies' extremal phase wavelet filter
-## The function is based on implementation of wavethresh package
-daubechies.wavelet <- function(N){
-
- switch (N,
- "1" = {
- H <- rep(0, 2)
- H[1] <- 1/sqrt(2)
- H[2] <- H[1]
- },
- "2" = {
- H <- rep(0, 4)
- H[1] <- 0.482962913145
- H[2] <- 0.836516303738
- H[3] <- 0.224143868042
- H[4] <- -0.129409522551
- },
- "3" = {
- H <- rep(0, 6)
- H[1] <- 0.33267055295
- H[2] <- 0.806891509311
- H[3] <- 0.459877502118
- H[4] <- -0.13501102001
- H[5] <- -0.085441273882
- H[6] <- 0.035226291882
- },
- "4" = {
- H <- rep(0, 8)
- H[1] <- 0.230377813309
- H[2] <- 0.714846570553
- H[3] <- 0.63088076793
- H[4] <- -0.027983769417
- H[5] <- -0.187034811719
- H[6] <- 0.030841381836
- H[7] <- 0.032883011667
- H[8] <- -0.010597401785
- },
- "5" = {
- H <- rep(0, 10)
- H[1] <- 0.160102397974
- H[2] <- 0.603829269797
- H[3] <- 0.724308528438
- H[4] <- 0.138428145901
- H[5] <- -0.242294887066
- H[6] <- -0.032244869585
- H[7] <- 0.07757149384
- H[8] <- -0.006241490213
- H[9] <- -0.012580752
- H[10] <- 0.003335725285
- },
- "6" = {
- H <- rep(0, 12)
- H[1] <- 0.11154074335
- H[2] <- 0.494623890398
- H[3] <- 0.751133908021
- H[4] <- 0.315250351709
- H[5] <- -0.226264693965
- H[6] <- -0.129766867567
- H[7] <- 0.097501605587
- H[8] <- 0.02752286553
- H[9] <- -0.031582039318
- H[10] <- 0.000553842201
- H[11] <- 0.004777257511
- H[12] <- -0.001077301085
- },
- "7" = {
- H <- rep(0, 14)
- H[1] <- 0.077852054085
- H[2] <- 0.396539319482
- H[3] <- 0.729132090846
- H[4] <- 0.469782287405
- H[5] <- -0.143906003929
- H[6] <- -0.224036184994
- H[7] <- 0.071309219267
- H[8] <- 0.080612609151
- H[9] <- -0.038029936935
- H[10] <- -0.016574541631
- H[11] <- 0.012550998556
- H[12] <- 0.000429577973
- H[13] <- -0.001801640704
- H[14] <- 0.0003537138
- },
- "8" = {
- H <- rep(0, 16)
- H[1] <- 0.054415842243
- H[2] <- 0.312871590914
- H[3] <- 0.675630736297
- H[4] <- 0.585354683654
- H[5] <- -0.015829105256
- H[6] <- -0.284015542962
- H[7] <- 0.000472484574
- H[8] <- 0.12874742662
- H[9] <- -0.017369301002
- H[10] <- -0.044088253931
- H[11] <- 0.013981027917
- H[12] <- 0.008746094047
- H[13] <- -0.004870352993
- H[14] <- -0.000391740373
- H[15] <- 0.000675449406
- H[16] <- -0.000117476784
- },
- "9" = {
- H <- rep(0, 18)
- H[1] <- 0.038077947364
- H[2] <- 0.243834674613
- H[3] <- 0.60482312369
- H[4] <- 0.657288078051
- H[5] <- 0.133197385825
- H[6] <- -0.293273783279
- H[7] <- -0.096840783223
- H[8] <- 0.148540749338
- H[9] <- 0.030725681479
- H[10] <- -0.067632829061
- H[11] <- 0.000250947115
- H[12] <- 0.022361662124
- H[13] <- -0.004723204758
- H[14] <- -0.004281503682
- H[15] <- 0.001847646883
- H[16] <- 0.000230385764
- H[17] <- -0.000251963189
- H[18] <- 3.934732e-05
- },
- "10" = {
- H <- rep(0, 20)
- H[1] <- 0.026670057901
- H[2] <- 0.188176800078
- H[3] <- 0.527201188932
- H[4] <- 0.688459039454
- H[5] <- 0.281172343661
- H[6] <- -0.249846424327
- H[7] <- -0.195946274377
- H[8] <- 0.127369340336
- H[9] <- 0.093057364604
- H[10] <- -0.071394147166
- H[11] <- -0.029457536822
- H[12] <- 0.033212674059
- H[13] <- 0.003606553567
- H[14] <- -0.010733175483
- H[15] <- 0.001395351747
- H[16] <- 0.001992405295
- H[17] <- -0.000685856695
- H[18] <- -0.000116466855
- H[19] <- 9.358867e-05
- H[20] <- -1.3264203e-05
- }
- )
-
- return(H)
-}
-
-## function to compute autocorrelation wavelets
-autocorrelation.wavelet <- function(J, N){
-
- h <- daubechies.wavelet(N)
- Phi1 <- convolve(h, h, type = "o")
-
- out <- vector(mode = "list", J)
- out[[1]] <- (-1)^((-2*N+1):(2*N-1)) * Phi1
-
- if(J > 1){
-
- Phi1 <- Phi1[seq(1,4*N-1,by=2)]
-
- for(j in 2:J){
-
- Lj <- (2^j - 1) * (2 * N - 1) + 1
- out[[j]] <- double(2*Lj - 1)
- out[[j]][seq(2*N,2*Lj-2*N,by=2)] <- out[[j-1]]
- out[[j]][seq(1,2*Lj-1,by=2)] <-
- convolve(out[[j-1]], Phi1, type = "o")
-
- }
- }
-
- return(out)
-}
-
-
-## main function
-wllag <- function(x, y, J = 8, N = 10, #family = "DaubExPhase",
- tau = 1e-3, from = -to, to = 100,
- verbose = FALSE, in.tau = FALSE, tol = 1e-6){
-
- time1 <- as.numeric(time(x))
- time2 <- as.numeric(time(y))
-
- grid <- seq(from, to, by = 1) * tau
-
- Lj <- (2^J - 1) * (2 * N - 1) + 1
- #Lj <- (2^J - 1) * (length(wavethresh::filter.select(N, family)$H) - 1) + 1
- grid2 <- seq(from - Lj + 1, to + Lj - 1, by = 1) * tau
-
- dx <- diff(as.numeric(x))
- dy <- diff(as.numeric(y))
-
- tmp <- .C("HYcrosscov2",
- as.integer(length(grid2)),
- as.integer(length(time2)),
- as.integer(length(time1)),
- as.double(grid2/tol),
- as.double(time2/tol),
- as.double(time1/tol),
- as.double(dy),
- as.double(dx),
- value=double(length(grid2)),
- PACKAGE = "yuima")$value
-
- #if(missing(J)) J <- floor(log2(length(grid)))
-
- #if(J < 2) stop("J must be larger than 1")
-
- #acw <- wavethresh::PsiJ(-J, filter.number = N, family = "DaubExPhase")
- #acw <- wavethresh::PsiJ(-J, filter.number = N, family = family)
- acw <- autocorrelation.wavelet(J, N)
-
- theta <- double(J)
- #covar <- double(J)
- #LLR <- double(J)
- corr <- double(J)
- crosscor <- vector("list", J)
-
- for(j in 1:J){
-
- wcov <- try(stats::filter(tmp, filter = acw[[j]], method = "c",
- sides = 2)[Lj:(length(grid) + Lj - 1)],
- silent = TRUE)
- #Mj <- (2^J - 2^j) * (2 * N - 1)
- #wcov <- try(convolve(tmp, acw[[j]], conj = FALSE, type = "filter")[(Mj + 1):(length(grid) + Mj)],
- # silent = TRUE)
-
- if(class(wcov) == "try-error"){
-
- theta[j] <- NA
- crosscor[[j]] <- NA
- corr[j] <- NA
-
- }else{
-
- #tmp.grid <- grid[-attr(wcov, "na.action")]
- crosscor[[j]] <- zoo(wcov, grid)
-
- obj <- abs(wcov)
- idx1 <- which(obj == max(obj, na.rm = TRUE))
- idx <- idx1[which.max(abs(grid[idx1]))]
- # if there are multiple peaks, take the lag farthest from zero
- theta[j] <- grid[idx]
- corr[j] <- crosscor[[j]][idx]
-
- }
-
- }
-
- if(verbose == TRUE){
-
- #obj0 <- tmp[(Lj + 1):(length(grid) + Lj)]
- obj0 <- tmp[Lj:(length(grid) + Lj - 1)]/sqrt(sum(dx^2)*sum(dy^2))
- obj <- abs(obj0)
- idx1 <- which(obj == max(obj, na.rm = TRUE))
- idx <- idx1[which.max(abs(grid[idx1]))]
- # if there are multiple peaks, the lag farthest from zero
- theta.hy <- grid[idx]
- corr.hy <- obj0[idx]
-
- if(in.tau == TRUE){
- theta <- round(theta/tau)
- theta.hy <- round(theta.hy/tau)
- }
-
- result <- list(lagtheta = theta, obj.values = corr,
- obj.fun = crosscor, theta.hry = theta.hy,
- cor.hry = corr.hy, ccor.hry = zoo(obj0, grid))
-
- class(result) <- "yuima.wllag"
-
- }else{
- if(in.tau == TRUE){
- result <- round(theta/tau)
- }else{
- result <- theta
- }
- }
-
- return(result)
-}
-
-# print method for yuima.wllag-class
-print.yuima.wllag <- function(x, ...){
-
- cat("Estimated scale-by-scale lead-lag parameters\n")
- print(x$lagtheta, ...)
- cat("Corresponding values of objective functions\n")
- print(x$obj.values, ...)
- cat("Estimated lead-lag parameter in the HRY sense\n")
- print(x$theta.hry, ...)
- cat("Corresponding correlation coefficient\n")
- print(x$cor.hry, ...)
-
-}
-
-# plot method for yuima.wllag class
-plot.yuima.wllag <- function(x, selectJ = NULL, xlab = expression(theta),
- ylab = "", ...){
-
- J <- length(x$lagtheta)
-
- if(is.null(selectJ)) selectJ <- 1:J
-
- for(j in selectJ){
- #plot(x$ccor[[j]], main=paste("j=",j), xlab=expression(theta),
- # ylab=expression(U[j](theta)), type = type, pch = pch, ...)
- plot(x$obj.fun[[j]], main = paste("j = ", j, sep =""),
- xlab = xlab, ylab = ylab, ...)
- abline(0, 0, lty = "dotted")
- }
-
-}
Added: pkg/yuima/R/wllag.R
===================================================================
--- pkg/yuima/R/wllag.R (rev 0)
+++ pkg/yuima/R/wllag.R 2022-01-28 02:43:59 UTC (rev 785)
@@ -0,0 +1,318 @@
+# Scale-by-scale lead-lag estimation by wavelets
+
+## function to compute Daubechies' extremal phase wavelet filter
+## The function is based on implementation of wavethresh package
+daubechies.wavelet <- function(N){
+
+ switch (N,
+ "1" = {
+ H <- rep(0, 2)
+ H[1] <- 1/sqrt(2)
+ H[2] <- H[1]
+ },
+ "2" = {
+ H <- rep(0, 4)
+ H[1] <- 0.482962913145
+ H[2] <- 0.836516303738
+ H[3] <- 0.224143868042
+ H[4] <- -0.129409522551
+ },
+ "3" = {
+ H <- rep(0, 6)
+ H[1] <- 0.33267055295
+ H[2] <- 0.806891509311
+ H[3] <- 0.459877502118
+ H[4] <- -0.13501102001
+ H[5] <- -0.085441273882
+ H[6] <- 0.035226291882
+ },
+ "4" = {
+ H <- rep(0, 8)
+ H[1] <- 0.230377813309
+ H[2] <- 0.714846570553
+ H[3] <- 0.63088076793
+ H[4] <- -0.027983769417
+ H[5] <- -0.187034811719
+ H[6] <- 0.030841381836
+ H[7] <- 0.032883011667
+ H[8] <- -0.010597401785
+ },
+ "5" = {
+ H <- rep(0, 10)
+ H[1] <- 0.160102397974
+ H[2] <- 0.603829269797
+ H[3] <- 0.724308528438
+ H[4] <- 0.138428145901
+ H[5] <- -0.242294887066
+ H[6] <- -0.032244869585
+ H[7] <- 0.07757149384
+ H[8] <- -0.006241490213
+ H[9] <- -0.012580752
+ H[10] <- 0.003335725285
+ },
+ "6" = {
+ H <- rep(0, 12)
+ H[1] <- 0.11154074335
+ H[2] <- 0.494623890398
+ H[3] <- 0.751133908021
+ H[4] <- 0.315250351709
+ H[5] <- -0.226264693965
+ H[6] <- -0.129766867567
+ H[7] <- 0.097501605587
+ H[8] <- 0.02752286553
+ H[9] <- -0.031582039318
+ H[10] <- 0.000553842201
+ H[11] <- 0.004777257511
+ H[12] <- -0.001077301085
+ },
+ "7" = {
+ H <- rep(0, 14)
+ H[1] <- 0.077852054085
+ H[2] <- 0.396539319482
+ H[3] <- 0.729132090846
+ H[4] <- 0.469782287405
+ H[5] <- -0.143906003929
+ H[6] <- -0.224036184994
+ H[7] <- 0.071309219267
+ H[8] <- 0.080612609151
+ H[9] <- -0.038029936935
+ H[10] <- -0.016574541631
+ H[11] <- 0.012550998556
+ H[12] <- 0.000429577973
+ H[13] <- -0.001801640704
+ H[14] <- 0.0003537138
+ },
+ "8" = {
+ H <- rep(0, 16)
+ H[1] <- 0.054415842243
+ H[2] <- 0.312871590914
+ H[3] <- 0.675630736297
+ H[4] <- 0.585354683654
+ H[5] <- -0.015829105256
+ H[6] <- -0.284015542962
+ H[7] <- 0.000472484574
+ H[8] <- 0.12874742662
+ H[9] <- -0.017369301002
+ H[10] <- -0.044088253931
+ H[11] <- 0.013981027917
+ H[12] <- 0.008746094047
+ H[13] <- -0.004870352993
+ H[14] <- -0.000391740373
+ H[15] <- 0.000675449406
+ H[16] <- -0.000117476784
+ },
+ "9" = {
+ H <- rep(0, 18)
+ H[1] <- 0.038077947364
+ H[2] <- 0.243834674613
+ H[3] <- 0.60482312369
+ H[4] <- 0.657288078051
+ H[5] <- 0.133197385825
+ H[6] <- -0.293273783279
+ H[7] <- -0.096840783223
+ H[8] <- 0.148540749338
+ H[9] <- 0.030725681479
+ H[10] <- -0.067632829061
+ H[11] <- 0.000250947115
+ H[12] <- 0.022361662124
+ H[13] <- -0.004723204758
+ H[14] <- -0.004281503682
+ H[15] <- 0.001847646883
+ H[16] <- 0.000230385764
+ H[17] <- -0.000251963189
+ H[18] <- 3.934732e-05
+ },
+ "10" = {
+ H <- rep(0, 20)
+ H[1] <- 0.026670057901
+ H[2] <- 0.188176800078
+ H[3] <- 0.527201188932
+ H[4] <- 0.688459039454
+ H[5] <- 0.281172343661
+ H[6] <- -0.249846424327
+ H[7] <- -0.195946274377
+ H[8] <- 0.127369340336
+ H[9] <- 0.093057364604
+ H[10] <- -0.071394147166
+ H[11] <- -0.029457536822
+ H[12] <- 0.033212674059
+ H[13] <- 0.003606553567
+ H[14] <- -0.010733175483
+ H[15] <- 0.001395351747
+ H[16] <- 0.001992405295
+ H[17] <- -0.000685856695
+ H[18] <- -0.000116466855
+ H[19] <- 9.358867e-05
+ H[20] <- -1.3264203e-05
+ }
+ )
+
+ return(H)
+}
+
+## function to compute autocorrelation wavelets
+autocorrelation.wavelet <- function(J, N){
+
+ h <- daubechies.wavelet(N)
+ Phi1 <- convolve(h, h, type = "o")
+
+ out <- vector(mode = "list", J)
+ out[[1]] <- (-1)^((-2*N+1):(2*N-1)) * Phi1
+
+ if(J > 1){
+
+ Phi1 <- Phi1[seq(1,4*N-1,by=2)]
+
+ for(j in 2:J){
+
+ Lj <- (2^j - 1) * (2 * N - 1) + 1
+ out[[j]] <- double(2*Lj - 1)
+ out[[j]][seq(2*N,2*Lj-2*N,by=2)] <- out[[j-1]]
+ out[[j]][seq(1,2*Lj-1,by=2)] <-
+ convolve(out[[j-1]], Phi1, type = "o")
+
+ }
+ }
+
+ return(out)
+}
+
+
+## main function
+wllag <- function(x, y, J = 8, N = 10, #family = "DaubExPhase",
+ tau = 1e-3, from = -to, to = 100,
+ verbose = FALSE, in.tau = FALSE, tol = 1e-6){
+
+ time1 <- as.numeric(time(x))
+ time2 <- as.numeric(time(y))
+
+ grid <- seq(from, to, by = 1) * tau
+
+ Lj <- (2^J - 1) * (2 * N - 1) + 1
+ #Lj <- (2^J - 1) * (length(wavethresh::filter.select(N, family)$H) - 1) + 1
+ grid2 <- seq(from - Lj + 1, to + Lj - 1, by = 1) * tau
+
+ dx <- diff(as.numeric(x))
+ dy <- diff(as.numeric(y))
+
+ tmp <- .C("HYcrosscov2",
+ as.integer(length(grid2)),
+ as.integer(length(time2)),
+ as.integer(length(time1)),
+ as.double(grid2/tol),
+ as.double(time2/tol),
+ as.double(time1/tol),
+ as.double(dy),
+ as.double(dx),
+ value=double(length(grid2)),
+ PACKAGE = "yuima")$value
+
+ #if(missing(J)) J <- floor(log2(length(grid)))
+
+ #if(J < 2) stop("J must be larger than 1")
+
+ #acw <- wavethresh::PsiJ(-J, filter.number = N, family = "DaubExPhase")
+ #acw <- wavethresh::PsiJ(-J, filter.number = N, family = family)
+ acw <- autocorrelation.wavelet(J, N)
+
+ theta <- double(J)
+ #covar <- double(J)
+ #LLR <- double(J)
+ corr <- double(J)
+ crosscor <- vector("list", J)
+
+ for(j in 1:J){
+
+ wcov <- try(stats::filter(tmp, filter = acw[[j]], method = "c",
+ sides = 2)[Lj:(length(grid) + Lj - 1)],
+ silent = TRUE)
+ #Mj <- (2^J - 2^j) * (2 * N - 1)
+ #wcov <- try(convolve(tmp, acw[[j]], conj = FALSE, type = "filter")[(Mj + 1):(length(grid) + Mj)],
+ # silent = TRUE)
+
+ if(class(wcov) == "try-error"){
+
+ theta[j] <- NA
+ crosscor[[j]] <- NA
+ corr[j] <- NA
+
+ }else{
+
+ #tmp.grid <- grid[-attr(wcov, "na.action")]
+ crosscor[[j]] <- zoo(wcov, grid)
+
+ obj <- abs(wcov)
+ idx1 <- which(obj == max(obj, na.rm = TRUE))
+ idx <- idx1[which.max(abs(grid[idx1]))]
+ # if there are multiple peaks, take the lag farthest from zero
+ theta[j] <- grid[idx]
+ corr[j] <- crosscor[[j]][idx]
+
+ }
+
+ }
+
+ if(verbose == TRUE){
+
+ #obj0 <- tmp[(Lj + 1):(length(grid) + Lj)]
+ obj0 <- tmp[Lj:(length(grid) + Lj - 1)]/sqrt(sum(dx^2)*sum(dy^2))
+ obj <- abs(obj0)
+ idx1 <- which(obj == max(obj, na.rm = TRUE))
+ idx <- idx1[which.max(abs(grid[idx1]))]
+ # if there are multiple peaks, the lag farthest from zero
+ theta.hy <- grid[idx]
+ corr.hy <- obj0[idx]
+
+ if(in.tau == TRUE){
+ theta <- round(theta/tau)
+ theta.hy <- round(theta.hy/tau)
+ }
+
+ result <- list(lagtheta = theta, obj.values = corr,
+ obj.fun = crosscor, theta.hry = theta.hy,
+ cor.hry = corr.hy, ccor.hry = zoo(obj0, grid))
+
+ class(result) <- "yuima.wllag"
+
+ }else{
+ if(in.tau == TRUE){
+ result <- round(theta/tau)
+ }else{
+ result <- theta
+ }
+ }
+
+ return(result)
+}
+
+# print method for yuima.wllag-class
+print.yuima.wllag <- function(x, ...){
+
+ cat("Estimated scale-by-scale lead-lag parameters\n")
+ print(x$lagtheta, ...)
+ cat("Corresponding values of objective functions\n")
+ print(x$obj.values, ...)
+ cat("Estimated lead-lag parameter in the HRY sense\n")
+ print(x$theta.hry, ...)
+ cat("Corresponding correlation coefficient\n")
+ print(x$cor.hry, ...)
+
+}
+
+# plot method for yuima.wllag class
+plot.yuima.wllag <- function(x, selectJ = NULL, xlab = expression(theta),
+ ylab = "", ...){
+
+ J <- length(x$lagtheta)
+
+ if(is.null(selectJ)) selectJ <- 1:J
+
+ for(j in selectJ){
+ #plot(x$ccor[[j]], main=paste("j=",j), xlab=expression(theta),
+ # ylab=expression(U[j](theta)), type = type, pch = pch, ...)
+ plot(x$obj.fun[[j]], main = paste("j = ", j, sep =""),
+ xlab = xlab, ylab = ylab, ...)
+ abline(0, 0, lty = "dotted")
+ }
+
+}
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