R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > y <- c(0.0357 + ,0.0332 + ,0.037 + ,0.0345 + ,0.037 + ,0.0373 + ,0.0393 + ,0.0385 + ,0.042 + ,0.0411 + ,0.0392 + ,0.0414 + ,0.0449 + ,0.0394 + ,0.0415 + ,0.0406 + ,0.0391 + ,0.0391 + ,0.0349 + ,0.0381 + ,0.0381 + ,0.036 + ,0.0381 + ,0.0375 + ,0.0379 + ,0.0362 + ,0.0381 + ,0.0358 + ,0.0363 + ,0.0335 + ,0.034 + ,0.0361 + ,0.0352 + ,0.0367 + ,0.0329 + ,0.0325 + ,0.0347 + ,0.0348 + ,0.0366 + ,0.0342 + ,0.0341 + ,0.0337 + ,0.0356 + ,0.037 + ,0.0338 + ,0.0347 + ,0.0334 + ,0.0361 + ,0.0333 + ,0.0327 + ,0.0342 + ,0.0363 + ,0.0351 + ,0.0369 + ,0.0418 + ,0.0391 + ,0.04 + ,0.0413 + ,0.0422 + ,0.0418 + ,0.0393 + ,0.0409 + ,0.0396 + ,0.0391 + ,0.0409 + ,0.04 + ,0.0379 + ,0.0392 + ,0.0374 + ,0.0376 + ,0.0399 + ,0.0395 + ,0.0389 + ,0.0376 + ,0.0358 + ,0.0363 + ,0.0387 + ,0.0357 + ,0.0339 + ,0.0355 + ,0.0352 + ,0.0328 + ,0.0322 + ,0.0338 + ,0.0331 + ,0.0329 + ,0.0337 + ,0.0346 + ,0.0338 + ,0.0316 + ,0.0339 + ,0.0345 + ,0.034 + ,0.034 + ,0.0335 + ,0.0339 + ,0.0378 + ,0.0329 + ,0.0365 + ,0.0357 + ,0.0356 + ,0.0356 + ,0.0391 + ,0.0374 + ,0.0345 + ,0.0348 + ,0.0385 + ,0.0345 + ,0.0392 + ,0.0381 + ,0.0386 + ,0.0409 + ,0.0409 + ,0.0374 + ,0.0377 + ,0.0359 + ,0.0403 + ,0.0382 + ,0.0407 + ,0.0369 + ,0.0396 + ,0.0381 + ,0.0363 + ,0.0386 + ,0.0362 + ,0.0369 + ,0.0378 + ,0.0354 + ,0.038 + ,0.0384 + ,0.0377 + ,0.0355 + ,0.0371 + ,0.0352 + ,0.0357 + ,0.036 + ,0.0368 + ,0.0334 + ,0.0361 + ,0.0332 + ,0.0341 + ,0.0337 + ,0.0341 + ,0.0382 + ,0.0351 + ,0.0339 + ,0.0359 + ,0.0359 + ,0.0366 + ,0.0385 + ,0.0366 + ,0.0377 + ,0.033 + ,0.0367 + ,0.0352 + ,0.0336 + ,0.0345 + ,0.0376 + ,0.0368 + ,0.0342 + ,0.0373 + ,0.0381 + ,0.0403 + ,0.0399 + ,0.0379 + ,0.038 + ,0.0382 + ,0.0349 + ,0.0379 + ,0.0357 + ,0.0382 + ,0.0347 + ,0.0379 + ,0.0381 + ,0.0383 + ,0.0361 + ,0.0351 + ,0.0358 + ,0.0353 + ,0.0349 + ,0.0355 + ,0.0349 + ,0.0351 + ,0.0355 + ,0.0376 + ,0.0363 + ,0.0358 + ,0.0356 + ,0.0381 + ,0.0339 + ,0.0351 + ,0.0344 + ,0.0346 + ,0.0369 + ,0.0385 + ,0.0377 + ,0.0383 + ,0.0375 + ,0.0374 + ,0.0405 + ,0.0376 + ,0.0385 + ,0.0391 + ,0.0438 + ,0.0409 + ,0.0447 + ,0.0421 + ,0.0412 + ,0.0378 + ,0.0365 + ,0.0364 + ,0.0372 + ,0.0392 + ,0.0382 + ,0.038 + ,0.0367 + ,0.0359 + ,0.0361 + ,0.0391 + ,0.035 + ,0.0374 + ,0.0375 + ,0.0351 + ,0.0357 + ,0.0357 + ,0.0368 + ,0.0375 + ,0.038 + ,0.0347 + ,0.0353 + ,0.0392 + ,0.0359 + ,0.0358 + ,0.0337 + ,0.0366 + ,0.0367 + ,0.0358 + ,0.0371 + ,0.0368 + ,0.0341 + ,0.0341 + ,0.0359 + ,0.0332 + ,0.0343 + ,0.035 + ,0.0328 + ,0.0333 + ,0.0382 + ,0.0348 + ,0.0388 + ,0.036 + ,0.0357 + ,0.0365 + ,0.0362 + ,0.0336 + ,0.0386 + ,0.0364 + ,0.0373 + ,0.0363 + ,0.0363 + ,0.0379 + ,0.0389 + ,0.0376 + ,0.0374 + ,0.0359 + ,0.0382 + ,0.0384 + ,0.0365 + ,0.0377 + ,0.0332 + ,0.035 + ,0.0343 + ,0.0384 + ,0.035 + ,0.0371 + ,0.0348 + ,0.0371 + ,0.0345 + ,0.034 + ,0.0367 + ,0.0327 + ,0.0338 + ,0.0346 + ,0.0346 + ,0.0347 + ,0.0353 + ,0.0367 + ,0.0338 + ,0.0357 + ,0.0343 + ,0.0347 + ,0.0355 + ,0.0363) > x <- c(0.0307 + ,0.0286 + ,0.0279 + ,0.031 + ,0.0304 + ,0.0324 + ,0.0312 + ,0.0315 + ,0.0317 + ,0.0339 + ,0.0323 + ,0.0341 + ,0.0316 + ,0.0335 + ,0.0376 + ,0.0363 + ,0.036 + ,0.0357 + ,0.0322 + ,0.0343 + ,0.0345 + ,0.0337 + ,0.0361 + ,0.0314 + ,0.0309 + ,0.0318 + ,0.0315 + ,0.0289 + ,0.0279 + ,0.0309 + ,0.0299 + ,0.029 + ,0.0313 + ,0.0285 + ,0.0281 + ,0.0305 + ,0.0279 + ,0.0262 + ,0.0283 + ,0.0317 + ,0.0281 + ,0.0321 + ,0.0284 + ,0.028 + ,0.0299 + ,0.0323 + ,0.029 + ,0.03 + ,0.0313 + ,0.0304 + ,0.0323 + ,0.0319 + ,0.0306 + ,0.0308 + ,0.0327 + ,0.0338 + ,0.0356 + ,0.0344 + ,0.0333 + ,0.0355 + ,0.0332 + ,0.0338 + ,0.0332 + ,0.0318 + ,0.0357 + ,0.0349 + ,0.0316 + ,0.0325 + ,0.0321 + ,0.0311 + ,0.0353 + ,0.0321 + ,0.0305 + ,0.0328 + ,0.0304 + ,0.0301 + ,0.0295 + ,0.0287 + ,0.0297 + ,0.031 + ,0.0292 + ,0.0299 + ,0.0296 + ,0.0306 + ,0.0299 + ,0.0305 + ,0.028 + ,0.0279 + ,0.0293 + ,0.0271 + ,0.0259 + ,0.0291 + ,0.0299 + ,0.0287 + ,0.0285 + ,0.0284 + ,0.0309 + ,0.0315 + ,0.0326 + ,0.0337 + ,0.031 + ,0.0334 + ,0.036 + ,0.0323 + ,0.0363 + ,0.0328 + ,0.0335 + ,0.0339 + ,0.033 + ,0.0351 + ,0.0344 + ,0.0337 + ,0.0312 + ,0.0311 + ,0.0349 + ,0.0322 + ,0.0309 + ,0.0308 + ,0.0319 + ,0.0312 + ,0.0293 + ,0.0311 + ,0.0321 + ,0.0309 + ,0.0325 + ,0.0297 + ,0.034 + ,0.0328 + ,0.0285 + ,0.0302 + ,0.0337 + ,0.0309 + ,0.0305 + ,0.0305 + ,0.0295 + ,0.0311 + ,0.034 + ,0.0295 + ,0.0283 + ,0.0303 + ,0.0305 + ,0.0331 + ,0.0278 + ,0.0296 + ,0.0302 + ,0.0296 + ,0.0314 + ,0.0317 + ,0.0322 + ,0.0308 + ,0.0304 + ,0.0313 + ,0.0314 + ,0.0324 + ,0.0318 + ,0.0337 + ,0.0327 + ,0.0374 + ,0.038 + ,0.0364 + ,0.0349 + ,0.0345 + ,0.0336 + ,0.0323 + ,0.031 + ,0.0338 + ,0.0316 + ,0.0306 + ,0.0305 + ,0.0323 + ,0.0301 + ,0.0333 + ,0.0318 + ,0.0308 + ,0.0308 + ,0.0302 + ,0.0305 + ,0.0307 + ,0.0319 + ,0.0313 + ,0.0296 + ,0.0297 + ,0.0302 + ,0.0313 + ,0.0313 + ,0.0296 + ,0.0309 + ,0.0307 + ,0.0297 + ,0.029 + ,0.0294 + ,0.0319 + ,0.0326 + ,0.032 + ,0.0363 + ,0.0332 + ,0.0351 + ,0.0355 + ,0.0307 + ,0.0334 + ,0.0323 + ,0.0323 + ,0.0347 + ,0.0314 + ,0.0327 + ,0.032 + ,0.0334 + ,0.0334 + ,0.0327 + ,0.0312 + ,0.0332 + ,0.0309 + ,0.0331 + ,0.0329 + ,0.0345 + ,0.0321 + ,0.0326 + ,0.0315 + ,0.0324 + ,0.0307 + ,0.0321 + ,0.0321 + ,0.03 + ,0.0292 + ,0.0328 + ,0.0291 + ,0.0304 + ,0.0301 + ,0.0287 + ,0.0314 + ,0.0279 + ,0.0292 + ,0.0284 + ,0.0302 + ,0.0293 + ,0.0298 + ,0.0304 + ,0.0295 + ,0.0311 + ,0.0293 + ,0.0303 + ,0.0317 + ,0.0277 + ,0.0302 + ,0.0276 + ,0.0311 + ,0.0294 + ,0.0289 + ,0.0306 + ,0.0321 + ,0.0286 + ,0.0303 + ,0.029 + ,0.0314 + ,0.0308 + ,0.0314 + ,0.0331 + ,0.0314 + ,0.0336 + ,0.0302 + ,0.0313 + ,0.0296 + ,0.0335 + ,0.0311 + ,0.0309 + ,0.0317 + ,0.0322 + ,0.0327 + ,0.0317 + ,0.0319 + ,0.0322 + ,0.0321 + ,0.0311 + ,0.0313 + ,0.0286 + ,0.0303 + ,0.0304 + ,0.0305 + ,0.0307 + ,0.0286 + ,0.0293 + ,0.0304 + ,0.0305 + ,0.0291 + ,0.0302 + ,0.0307 + ,0.029 + ,0.031 + ,0.0309 + ,0.0298 + ,0.0298 + ,0.0295 + ,0.0308) > par8 = 'na.fail' > par7 = '0' > par6 = '0' > par5 = '1' > par4 = '1' > par3 = '0' > par2 = '0' > par1 = '1' > ylab = 'y' > xlab = 'x' > par8 <- 'na.fail' > par7 <- '0' > par6 <- '0' > par5 <- '1' > par4 <- '1' > par3 <- '0' > par2 <- '0' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., (2012), Cross Correlation Function (v1.0.8) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_cross.wasp/ > #Source of accompanying publication: > # > par1 <- as.numeric(par1) > par2 <- as.numeric(par2) > par3 <- as.numeric(par3) > par4 <- as.numeric(par4) > par5 <- as.numeric(par5) > par6 <- as.numeric(par6) > par7 <- as.numeric(par7) > if (par8=='na.fail') par8 <- na.fail else par8 <- na.pass > ccf <- function (x, y, lag.max = NULL, type = c('correlation', 'covariance'), plot = TRUE, na.action = na.fail, ...) { + type <- match.arg(type) + if (is.matrix(x) || is.matrix(y)) + stop('univariate time series only') + X <- na.action(ts.intersect(as.ts(x), as.ts(y))) + colnames(X) <- c(deparse(substitute(x))[1L], deparse(substitute(y))[1L]) + acf.out <- acf(X, lag.max = lag.max, plot = FALSE, type = type, na.action=na.action) + lag <- c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2]) + y <- c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2]) + acf.out$acf <- array(y, dim = c(length(y), 1L, 1L)) + acf.out$lag <- array(lag, dim = c(length(y), 1L, 1L)) + acf.out$snames <- paste(acf.out$snames, collapse = ' & ') + if (plot) { + plot(acf.out, ...) + return(invisible(acf.out)) + } + else return(acf.out) + } > if (par1 == 0) { + x <- log(x) + } else { + x <- (x ^ par1 - 1) / par1 + } > if (par5 == 0) { + y <- log(y) + } else { + y <- (y ^ par5 - 1) / par5 + } > if (par2 > 0) x <- diff(x,lag=1,difference=par2) > if (par6 > 0) y <- diff(y,lag=1,difference=par6) > if (par3 > 0) x <- diff(x,lag=par4,difference=par3) > if (par7 > 0) y <- diff(y,lag=par4,difference=par7) > x [1] -0.9693 -0.9714 -0.9721 -0.9690 -0.9696 -0.9676 -0.9688 -0.9685 -0.9683 [10] -0.9661 -0.9677 -0.9659 -0.9684 -0.9665 -0.9624 -0.9637 -0.9640 -0.9643 [19] -0.9678 -0.9657 -0.9655 -0.9663 -0.9639 -0.9686 -0.9691 -0.9682 -0.9685 [28] -0.9711 -0.9721 -0.9691 -0.9701 -0.9710 -0.9687 -0.9715 -0.9719 -0.9695 [37] -0.9721 -0.9738 -0.9717 -0.9683 -0.9719 -0.9679 -0.9716 -0.9720 -0.9701 [46] -0.9677 -0.9710 -0.9700 -0.9687 -0.9696 -0.9677 -0.9681 -0.9694 -0.9692 [55] -0.9673 -0.9662 -0.9644 -0.9656 -0.9667 -0.9645 -0.9668 -0.9662 -0.9668 [64] -0.9682 -0.9643 -0.9651 -0.9684 -0.9675 -0.9679 -0.9689 -0.9647 -0.9679 [73] -0.9695 -0.9672 -0.9696 -0.9699 -0.9705 -0.9713 -0.9703 -0.9690 -0.9708 [82] -0.9701 -0.9704 -0.9694 -0.9701 -0.9695 -0.9720 -0.9721 -0.9707 -0.9729 [91] -0.9741 -0.9709 -0.9701 -0.9713 -0.9715 -0.9716 -0.9691 -0.9685 -0.9674 [100] -0.9663 -0.9690 -0.9666 -0.9640 -0.9677 -0.9637 -0.9672 -0.9665 -0.9661 [109] -0.9670 -0.9649 -0.9656 -0.9663 -0.9688 -0.9689 -0.9651 -0.9678 -0.9691 [118] -0.9692 -0.9681 -0.9688 -0.9707 -0.9689 -0.9679 -0.9691 -0.9675 -0.9703 [127] -0.9660 -0.9672 -0.9715 -0.9698 -0.9663 -0.9691 -0.9695 -0.9695 -0.9705 [136] -0.9689 -0.9660 -0.9705 -0.9717 -0.9697 -0.9695 -0.9669 -0.9722 -0.9704 [145] -0.9698 -0.9704 -0.9686 -0.9683 -0.9678 -0.9692 -0.9696 -0.9687 -0.9686 [154] -0.9676 -0.9682 -0.9663 -0.9673 -0.9626 -0.9620 -0.9636 -0.9651 -0.9655 [163] -0.9664 -0.9677 -0.9690 -0.9662 -0.9684 -0.9694 -0.9695 -0.9677 -0.9699 [172] -0.9667 -0.9682 -0.9692 -0.9692 -0.9698 -0.9695 -0.9693 -0.9681 -0.9687 [181] -0.9704 -0.9703 -0.9698 -0.9687 -0.9687 -0.9704 -0.9691 -0.9693 -0.9703 [190] -0.9710 -0.9706 -0.9681 -0.9674 -0.9680 -0.9637 -0.9668 -0.9649 -0.9645 [199] -0.9693 -0.9666 -0.9677 -0.9677 -0.9653 -0.9686 -0.9673 -0.9680 -0.9666 [208] -0.9666 -0.9673 -0.9688 -0.9668 -0.9691 -0.9669 -0.9671 -0.9655 -0.9679 [217] -0.9674 -0.9685 -0.9676 -0.9693 -0.9679 -0.9679 -0.9700 -0.9708 -0.9672 [226] -0.9709 -0.9696 -0.9699 -0.9713 -0.9686 -0.9721 -0.9708 -0.9716 -0.9698 [235] -0.9707 -0.9702 -0.9696 -0.9705 -0.9689 -0.9707 -0.9697 -0.9683 -0.9723 [244] -0.9698 -0.9724 -0.9689 -0.9706 -0.9711 -0.9694 -0.9679 -0.9714 -0.9697 [253] -0.9710 -0.9686 -0.9692 -0.9686 -0.9669 -0.9686 -0.9664 -0.9698 -0.9687 [262] -0.9704 -0.9665 -0.9689 -0.9691 -0.9683 -0.9678 -0.9673 -0.9683 -0.9681 [271] -0.9678 -0.9679 -0.9689 -0.9687 -0.9714 -0.9697 -0.9696 -0.9695 -0.9693 [280] -0.9714 -0.9707 -0.9696 -0.9695 -0.9709 -0.9698 -0.9693 -0.9710 -0.9690 [289] -0.9691 -0.9702 -0.9702 -0.9705 -0.9692 > y [1] -0.9643 -0.9668 -0.9630 -0.9655 -0.9630 -0.9627 -0.9607 -0.9615 -0.9580 [10] -0.9589 -0.9608 -0.9586 -0.9551 -0.9606 -0.9585 -0.9594 -0.9609 -0.9609 [19] -0.9651 -0.9619 -0.9619 -0.9640 -0.9619 -0.9625 -0.9621 -0.9638 -0.9619 [28] -0.9642 -0.9637 -0.9665 -0.9660 -0.9639 -0.9648 -0.9633 -0.9671 -0.9675 [37] -0.9653 -0.9652 -0.9634 -0.9658 -0.9659 -0.9663 -0.9644 -0.9630 -0.9662 [46] -0.9653 -0.9666 -0.9639 -0.9667 -0.9673 -0.9658 -0.9637 -0.9649 -0.9631 [55] -0.9582 -0.9609 -0.9600 -0.9587 -0.9578 -0.9582 -0.9607 -0.9591 -0.9604 [64] -0.9609 -0.9591 -0.9600 -0.9621 -0.9608 -0.9626 -0.9624 -0.9601 -0.9605 [73] -0.9611 -0.9624 -0.9642 -0.9637 -0.9613 -0.9643 -0.9661 -0.9645 -0.9648 [82] -0.9672 -0.9678 -0.9662 -0.9669 -0.9671 -0.9663 -0.9654 -0.9662 -0.9684 [91] -0.9661 -0.9655 -0.9660 -0.9660 -0.9665 -0.9661 -0.9622 -0.9671 -0.9635 [100] -0.9643 -0.9644 -0.9644 -0.9609 -0.9626 -0.9655 -0.9652 -0.9615 -0.9655 [109] -0.9608 -0.9619 -0.9614 -0.9591 -0.9591 -0.9626 -0.9623 -0.9641 -0.9597 [118] -0.9618 -0.9593 -0.9631 -0.9604 -0.9619 -0.9637 -0.9614 -0.9638 -0.9631 [127] -0.9622 -0.9646 -0.9620 -0.9616 -0.9623 -0.9645 -0.9629 -0.9648 -0.9643 [136] -0.9640 -0.9632 -0.9666 -0.9639 -0.9668 -0.9659 -0.9663 -0.9659 -0.9618 [145] -0.9649 -0.9661 -0.9641 -0.9641 -0.9634 -0.9615 -0.9634 -0.9623 -0.9670 [154] -0.9633 -0.9648 -0.9664 -0.9655 -0.9624 -0.9632 -0.9658 -0.9627 -0.9619 [163] -0.9597 -0.9601 -0.9621 -0.9620 -0.9618 -0.9651 -0.9621 -0.9643 -0.9618 [172] -0.9653 -0.9621 -0.9619 -0.9617 -0.9639 -0.9649 -0.9642 -0.9647 -0.9651 [181] -0.9645 -0.9651 -0.9649 -0.9645 -0.9624 -0.9637 -0.9642 -0.9644 -0.9619 [190] -0.9661 -0.9649 -0.9656 -0.9654 -0.9631 -0.9615 -0.9623 -0.9617 -0.9625 [199] -0.9626 -0.9595 -0.9624 -0.9615 -0.9609 -0.9562 -0.9591 -0.9553 -0.9579 [208] -0.9588 -0.9622 -0.9635 -0.9636 -0.9628 -0.9608 -0.9618 -0.9620 -0.9633 [217] -0.9641 -0.9639 -0.9609 -0.9650 -0.9626 -0.9625 -0.9649 -0.9643 -0.9643 [226] -0.9632 -0.9625 -0.9620 -0.9653 -0.9647 -0.9608 -0.9641 -0.9642 -0.9663 [235] -0.9634 -0.9633 -0.9642 -0.9629 -0.9632 -0.9659 -0.9659 -0.9641 -0.9668 [244] -0.9657 -0.9650 -0.9672 -0.9667 -0.9618 -0.9652 -0.9612 -0.9640 -0.9643 [253] -0.9635 -0.9638 -0.9664 -0.9614 -0.9636 -0.9627 -0.9637 -0.9637 -0.9621 [262] -0.9611 -0.9624 -0.9626 -0.9641 -0.9618 -0.9616 -0.9635 -0.9623 -0.9668 [271] -0.9650 -0.9657 -0.9616 -0.9650 -0.9629 -0.9652 -0.9629 -0.9655 -0.9660 [280] -0.9633 -0.9673 -0.9662 -0.9654 -0.9654 -0.9653 -0.9647 -0.9633 -0.9662 [289] -0.9643 -0.9657 -0.9653 -0.9645 -0.9637 > postscript(file="/var/wessaorg/rcomp/tmp/1pxx41408272683.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > (r <- ccf(x,y,na.action=par8,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)')) Autocorrelations of series 'X', by lag -21 -20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -0.264 -0.253 -0.201 -0.194 -0.075 -0.056 -0.042 0.040 0.054 0.153 0.175 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 0.235 0.269 0.315 0.330 0.408 0.367 0.425 0.467 0.484 0.458 0.464 1 2 3 4 5 6 7 8 9 10 11 0.457 0.404 0.414 0.296 0.294 0.309 0.238 0.250 0.187 0.078 0.059 12 13 14 15 16 17 18 19 20 21 -0.042 -0.099 -0.174 -0.209 -0.288 -0.344 -0.328 -0.376 -0.400 -0.400 > dev.off() null device 1 > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Cross Correlation Function',2,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Parameter',header=TRUE) > a<-table.element(a,'Value',header=TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE) > a<-table.element(a,par1) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE) > a<-table.element(a,par2) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE) > a<-table.element(a,par3) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Seasonal Period (s)',header=TRUE) > a<-table.element(a,par4) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE) > a<-table.element(a,par5) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE) > a<-table.element(a,par6) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE) > a<-table.element(a,par7) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'k',header=TRUE) > a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE) > a<-table.row.end(a) > mylength <- length(r$acf) > myhalf <- floor((mylength-1)/2) > for (i in 1:mylength) { + a<-table.row.start(a) + a<-table.element(a,i-myhalf-1,header=TRUE) + a<-table.element(a,r$acf[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/2mspm1408272683.tab") > > try(system("convert tmp/1pxx41408272683.ps tmp/1pxx41408272683.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 0.655 0.132 0.791