R help - Aug 2003 - Using spline parameters to generate data

# I need to generate some data. I'm modeling some time series that follow
a
# negative exponential decay (mostly). I have 20 samples that can easily
be fit with cubic splines.
# What I want to do is generate many thousands of similar samples using
the parameters from the splines

# For instance one data sample looks not unlike this:
foo.curve <- 1 * exp(-0.01 * 1:500) + 0.5 
ts.plot(foo.curve, lwd = 2)

# Another sample looks not unlike this:
foo.curve2 <- 0.9 * exp(-0.02 * 1:500) + 0.5 
ts.plot(foo.curve2, lwd = 2)



# They can be fit with splines easily like so:

ts.plot(foo.curve, lwd = 2)
spline.model <- smooth.spline(foo.curve)

fits4foo <- predict(spline.model)$y
spline4foo <- spline(1:500, fits4foo)
lines(spline4foo$x, spline4foo$y, col = "red", lwd = 2, lty =
"dashed")

# I originally generated the data I needed by using a nls model and 
# adding some noise to the mean of the coefficents from those fits.
# However, I've been told to try and do this using splines for arcane
reasons.

# So, if you had 20 splines like spline.model above and wanted to generate
some similar data what would you do?
# Thanks in advance.









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Have you considered adding noise to "predict(spline.model)$y"?  If
this
won't solve your problem, then I think I don't understand what you want 
to do.

spencer graves

chumpmonkey3 at hushmail.com wrote:
> # I need to generate some data. I'm modeling some time series that
follow
> a
> # negative exponential decay (mostly). I have 20 samples that can easily
> be fit with cubic splines.
> # What I want to do is generate many thousands of similar samples using
> the parameters from the splines
> 
> # For instance one data sample looks not unlike this:
> foo.curve <- 1 * exp(-0.01 * 1:500) + 0.5 
> ts.plot(foo.curve, lwd = 2)
> 
> # Another sample looks not unlike this:
> foo.curve2 <- 0.9 * exp(-0.02 * 1:500) + 0.5 
> ts.plot(foo.curve2, lwd = 2)
> 
> 
> 
> # They can be fit with splines easily like so:
> 
> ts.plot(foo.curve, lwd = 2)
> spline.model <- smooth.spline(foo.curve)
> 
> fits4foo <- predict(spline.model)$y
> spline4foo <- spline(1:500, fits4foo)
> lines(spline4foo$x, spline4foo$y, col = "red", lwd = 2, lty =
"dashed")
> 
> # I originally generated the data I needed by using a nls model and 
> # adding some noise to the mean of the coefficents from those fits.
> # However, I've been told to try and do this using splines for arcane
> reasons.
> 
> # So, if you had 20 splines like spline.model above and wanted to generate
> some similar data what would you do?
> # Thanks in advance.
> 
> 
> 
> 
> 
> 
> 
> 
> 
> Promote security and make money with the Hushmail Affiliate Program:
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://www.stat.math.ethz.ch/mailman/listinfo/r-help

# Sorry for the confussion.
# The way I had originally generated the data was
# sort of like this (cut and paste the code below a few times):

foo.curve <- runif(1,0.8,1.2) * exp(runif(1,-0.015,-0.005) * 1:500) +
runif(1,0.25,0.75) 
ts.plot(foo.curve, lwd = 2)

# where the min and max values in runif() were mined from the data using
nls.
# Are there similar ways to manipulate the smooth spline parameters to
give that kind of data?
# I still need a smooth line but would like the nugget, sill and range
to change 
# (to borrow terms from semivariance)

On Thu, 14 Aug 2003 17:46:28 -0700 Spencer Graves <spencer.graves at
PDF.COM>
wrote:
>Have you considered adding noise to "predict(spline.model)$y"?  If
>this 
>won't solve your problem, then I think I don't understand what you
>want 
>to do.
>
>spencer graves
>
>chumpmonkey3 at hushmail.com wrote:
>> # I need to generate some data. I'm modeling some time series
>that follow
>> a
>> # negative exponential decay (mostly). I have 20 samples that
>can easily
>> be fit with cubic splines.
>> # What I want to do is generate many thousands of similar samples
>using
>> the parameters from the splines
>> 
>> # For instance one data sample looks not unlike this:
>> foo.curve <- 1 * exp(-0.01 * 1:500) + 0.5 
>> ts.plot(foo.curve, lwd = 2)
>> 
>> # Another sample looks not unlike this:
>> foo.curve2 <- 0.9 * exp(-0.02 * 1:500) + 0.5 
>> ts.plot(foo.curve2, lwd = 2)
>> 
>> 
>> 
>> # They can be fit with splines easily like so:
>> 
>> ts.plot(foo.curve, lwd = 2)
>> spline.model <- smooth.spline(foo.curve)
>> 
>> fits4foo <- predict(spline.model)$y
>> spline4foo <- spline(1:500, fits4foo)
>> lines(spline4foo$x, spline4foo$y, col = "red", lwd = 2, lty
>"dashed")
>> 
>> # I originally generated the data I needed by using a nls model
>and 
>> # adding some noise to the mean of the coefficents from those
>fits.
>> # However, I've been told to try and do this using splines for
>arcane
>> reasons.
>> 
>> # So, if you had 20 splines like spline.model above and wanted
>to generate
>> some similar data what would you do?
>> # Thanks in advance.
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> 
>> Promote security and make money with the Hushmail Affiliate Program:
>> 
>> ______________________________________________
>> R-help at stat.math.ethz.ch mailing list
>> https://www.stat.math.ethz.ch/mailman/listinfo/r-help
>
>
>
>








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