approx.cor | R Documentation |
Allows to correlate time-series having different sampling rate, if they have a comparable depth or time scale
approx.cor(xy1, dt1, xy2, dt2, plot = T, output = T, type = "p", ...)
xy1 |
intensity values for the first data set |
dt1 |
depth or time scale for the first data set |
xy2 |
intensity values for the second data set |
dt2 |
depth or time scale for the second data set |
plot |
whether to plot |
output |
whether to output |
type |
type of points in the plot (see help page of |
... |
additional parameters to feed to the |
a list of correlation ($cor), slope ($slope), intercept ($intercept) (two values for each: interpolation to fit dt1 and dt2 respectively), and of the xy1 and xy2 values, interpolated for dt1 ($df1) and df2 ($df2)
set.seed(42) n <- 600 t <- seq_len(n) p1 <- 30 p2 <- 240 xy.pure <- (1 + 0.6 * sin(t*2*pi/p2)) * sin(t*2*pi/p1) + 2 * sin(t*2*pi/p2) xy <- xy.pure + rnorm(n, sd = 0.5) inter_dt <- round(runif(length(xy), min = 0.5, max = 1.5),1) dt.pure <- cumsum(inter_dt) keep <- runif(length(dt.pure)) < 0.5 xy <- xy[keep] dt <- dt.pure[keep] + rnorm(sum(keep), -0.2, 0.2) par(mfrow = c(1,2)) plot(xy, dt, type = "o", pch = 19) plot(xy.pure, dt.pure, type = "o", pch = 19) par(mfrow = c(1,1)) out <- approx.cor(xy, dt, xy.pure, dt.pure) out$cor out$slope out$intercept
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