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I am working with the following data (see picture)

I am trying to predict future Y axis values from the X values. As you can tell from the picture and as I discovered, the prediction is going to be horrible based on the data alone.

I am now trying to go row by row through the data and classify each point as significant or nonsignificant and see if I can find anything interesting in the classification. I want my algorithm something like this:

  1. Take absolute value of every element in both Y axis and X axis data columns

  2. Iterate through Y axis column and find the top 5% largest values

  3. Iterate through again marking those top 5% or maybe 1% (Will fiddle with this) with a flag

  4. Plot only the top 5% and see if the linear model (or some model) has a better R^2

  5. Perform some sort of classification analysis against the two flags, significant and non significant (I really need help and suggestions with this step)

    enter image description here

The two things I need are a pointer on are

  • How to step element by element through data, making modifications along the way
  • What kind of classification analysis exists that might be helpful, goal being to predict which classification future sampled elements will fall into.
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1 Answer 1

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You don't step element by element through data in R. Well, you can, but you need to be a very patient person.

The R way would be to calculate the quantile you want and subset the data.

Example:

set.seed(42)
DF <- data.frame(x=runif(1e3),y=rlnorm(1e3))
plot(y~x,data=DF)

#95 % quantile
q95 <- quantile(DF$y,0.95)

#plot data subset
plot(y~x,data=DF[DF$y>q95,])

#fit linear model to data subset
fit <- lm(y~x,data=DF[DF$y>q95,])
abline(fit)

Can't help you with the statistical issue ...

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  • $\begingroup$ Hey this helped a lot. I really needed some help with the basic subset commands. $\endgroup$ Jun 11, 2013 at 19:56

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