error in shape() function in evir library - r

Question

I have a dataframe where a column is a mix of positive and negative numbers and the first entry is NA. I'm trying to run the shape function as
shape(data$col, models = 30, start = 30, end = 400, ci=.90,reverse = TRUE,auto.scale = TRUE)
where the data in 'col' is [NA, -0.2663194135, -3.7665034719, -0.2072122334, 1.5721742718, -9.142419, -8.954330, -5.167314, 11.805930, 9.533830, 7.065835]
but I get an error that says
Error in optim(theta, negloglik, hessian = TRUE, ..., tmp = excess) :
non-finite value supplied by optim
Can someone help me figure out what it means? I've googled it but haven't found anything concrete

Answer 1

It's not clear what you are trying to do here. Calling shape allows you to see how altering the threshold or nextremes parameters in the gpd function will alter the xi parameter of the resulting generalised Pareto distribution model.
There are a few reasons why the example you supplied doesn't work. Let's first of all show an example of what does work. The exponential distribution is a special case of a GPD with mu = 0 and xi = 0, so a sample drawn from the exponential distribution should do the trick:
library(evir) # For the shape() function
set.seed(69) # Makes this example reproducible
x <- rexp(300) # Random sample of 300 elements drawn from exponential distribution
shape(x)
Fine.
However, your sample contains an NA. What happens if we make a single value NA in our sample?
x[1] <- NA
shape(x)
#> Error in optim(theta, negloglik, hessian = TRUE, ..., tmp = excess) :
#> non-finite value supplied by optim
So, no NAs allowed.
Unfortunately, you will find that you still get the same error if you remove your NA value. There are two reasons for this. Firstly, you have 9 non-NA samples. What happens if we try a length-9 exponential sample?
shape(rexp(9))
#> Error in optim(theta, negloglik, hessian = TRUE, ..., tmp = excess) :
#> non-finite finite-difference value [1]
We will find that the model will fail to fit with fewer than about 16 data points.
But that's not the only problem. What if we try to get a plot for data that can't be drawn from a generalized Pareto distribution?
# Maybe a uniform distribution?
shape(runif(300, 1, 10))
#> Error in optim(theta, negloglik, hessian = TRUE, ..., tmp = excess) :
#> non-finite finite-difference value [1]
#> In addition: Warning message:
#> In sqrt(diag(varcov)) : NaNs produced
#>
So in effect, you need a bigger sample with no NAs, and it needs to conform approximately to a GPD, otherwise the gpd function will throw an error.
I might be able to help if you let us know the bigger picture of what you are trying to do.