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I am trying to find the point where the slope of my data changes significantly. I want to determine a single point (in this case a value for days) in order to use it as an argument in a model I try to run.

study_data <- data.frame(days = c(30,50,100,133,166,200,233,266,300,333,366,400,450,500,750) ,
                 participants = c(100,85,60,45,36,30,27,23,21,19,19,17,16,16,6))

plot(study_data$days, study_data$participants)

using the cpop package gave me multiple points of change.

result <- cpop(study_data$participants, study_data$days)
summary(result)
plot(result)

cpop package output

using the segmented package I get the following

nodes_model <- lm(participants ~ days, data = study_data)
segmented_nodes_model <- segmented(nodes_model, seg.Z = ~days)
nodes_breaks <- segmented_nodes_model$psi
          Initial     Est.   St.Err
psi1.days      NA 175.7564 6.215544

Both outputs don't really line up. I am sorry, but I am confused.

segmented output

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  • $\begingroup$ What if it changes significantly at more than one place? $\endgroup$
    – jbowman
    Apr 24 at 2:47
  • $\begingroup$ That's a good question. I am not really sure, how I could use that information. I got the segmented package to work (see code in the question) could I use the est. value? $\endgroup$
    – User108
    Apr 24 at 3:32
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    $\begingroup$ What if it is changing everywhere, as it appears? Seeking a statistical test result to justify some sort of conclusion about particular point on the slope seems unlikely to yield a good outcome. What are the data and what would you really like to know about it? $\endgroup$ Apr 24 at 4:52
  • $\begingroup$ The data doesn't look like there are any breakpoints up to 500 days. It rather seems like you have a non-linear relationship. $\endgroup$
    – Roland
    Apr 25 at 5:19

1 Answer 1

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It appears that your version cpop does not respect the inhomogeneous grid in days. It should, and for me the latest version does:

enter image description here

Try updating the cpop package.

Also, the cpop method has a tuning parameter beta. This is beta = 2 * log(length(y)) by default, but the authors suggest to vary this and look at the resulting segmentations:

If we know, or have a good estimate of, the residual variances, and the noise is (close to) independent over time then an appropriate choice for the penalty is β = 2 log n, and this is the default for CPOP. However in many applications these assumptions will not hold and it is advised to look at segmentations for different value of β – this is possible using CPOP with the CROPS algorithm cpop.crops. Larger values of β will lead to functions with fewer changes.

source, see bottom of page 4.

Have you tried using the cpop.crops method?

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