Using the data you provided, a combination of slope and height comes
close:
X <- seq(Y)
high <- Y > 0.6
upslope <- c(FALSE, diff(Y) > 0)
section <- rep(1, length(Y))
section[upslope==TRUE & high==TRUE] <- 2
section[upslope==FALSE & high==TRUE] <- 3
plot(X, Y, col=section)
Or you could base the slope on the a smooth fit of the data rather than Y
itself:
P <- loess(Y ~ X, degree=2, span=1/10)$fitted
upslope <- c(FALSE, diff(P) > 0)
section <- rep(1, length(Y))
section[upslope==TRUE & high==TRUE] <- 2
section[upslope==FALSE & high==TRUE] <- 3
plot(X, Y, col=section)
Jean
"chuck.01" <charliethebrow...@gmail.com> wrote on 10/26/2012 03:14:29 AM:
>
> hello,
> I have some data that looks similar to this (only not as nice as this):
>
> Y <- c(abs(rnorm(100, 0.10, .1)), seq(.10, 1.0, .3)+rnorm(1, 0, .5) ,
> seq(0.8, 4.0, .31)+rnorm(1, 0, .5)
> , seq(3.9, .20, -.2)+rnorm(1, 0, .5) , abs(rnorm(100, 0.13, .1)) ,
seq(.10,
> 1.2, .35)+rnorm(1, 0, .5)
> , seq(0.7, 6.0, .31)+rnorm(1, 0, .5) , seq(5.9, .23, -.18)+rnorm(1, 0,
.5) ,
> abs(rnorm(50, 0.18, .1)) )
>
> plot(Y~c(1:length(Y))) # it is water level through time
>
> I am trying to find a way to divide these data into 3 sections , 1) the
> rising limbs, 2) falling limbs, and 3) not #1 or #2.
> I'll spare you the list of things I've tried, just know that the data is
> generally too noisy to use something as simple as which(diff(Y) > b),
where
> b is some threshold.
> Please let me know if you have an idea of how to tackle this.
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