Annual Average Global Surface Temperature
Time Series of length 113 of annual average global surface temperature deviations from 1880 to 1992.
data(globtemp)
This is Example 1 of the COBS paper, where the hypothesis of a monotonely increasing trend is considered; Koenker and Schorfheide (1994) consider modeling the autocorrelations.
‘temp.data’ in file ‘cobs.shar’ available from http://www.cba.nau.edu/pin-ng/cobs.html.
He, X. and Ng, P. (1999) COBS: Qualitatively Constrained Smoothing via Linear Programming; Computational Statistics 14, 315–337.
Koenker, R. and Schorfheide F. (1994) Quantile Spline Models for Global Temperature Change; Climate Change 28, 395–404.
data(globtemp) plot(globtemp, main = "Annual Global Temperature Deviations") str(globtemp) ## forget about time-series, just use numeric vectors: year <- as.vector(time(globtemp)) temp <- as.vector(globtemp) ##---- Code for Figure 1a of He and Ng (1999) ---------- a50 <- cobs(year, temp, knots.add = TRUE, degree = 1, constraint = "increase") summary(a50) ## As suggested in the warning message, we increase the number of knots to 9 a50 <- cobs(year, temp, nknots = 9, knots.add = TRUE, degree = 1, constraint = "increase") summary(a50) ## Here, we use the same knots sequence chosen for the 50th percentile a10 <- cobs(year, temp, nknots = length(a50$knots), knots = a50$knot, degree = 1, tau = 0.1, constraint = "increase") summary(a10) a90 <- cobs(year, temp, nknots = length(a50$knots), knots = a50$knot, degree = 1, tau = 0.9, constraint = "increase") summary(a90) which(hot.idx <- temp >= a90$fit) which(cold.idx <- temp <= a10$fit) normal.idx <- !hot.idx & !cold.idx plot(year, temp, type = "n", ylab = "Temperature (C)", ylim = c(-.7,.6)) lines(predict(a50, year, interval = "both"), col = 2) lines(predict(a10, year, interval = "both"), col = 3) lines(predict(a90, year, interval = "both"), col = 3) points(year, temp, pch = c(1,3)[2 - normal.idx]) ## label the "hot" and "cold" days text(year[hot.idx], temp[hot.idx] + .03, labels = year[hot.idx]) text(year[cold.idx],temp[cold.idx]- .03, labels = year[cold.idx])
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