Return to Question

I have data for the population of a number of different fish, sampled over a period of about 5 years, but in a very irregular pattern. Sometimes there are months between samples, sometimes there are several samples in one month. There are also many 0 counts

How to deal with such data?

I can graph it easily enough in R, but the graphs are not particularly illuminating, because they are very bumpy.

In terms of modeling - with species modeled as a function of various things - maybe a mixed model (aka multilevel model).

Any references or ideas welcome

Some details in response to comments

There are about 15 species.

I am trying to both get an idea of any trends or seasonality in each fish, and look at how the species are related to each other (my client originally wanted a simple table of correlations)

The goal is descriptive and analytic, not predictive

I have data for the population of a number of different fish, sampled over a period of about 5 years, but in a very irregular pattern. Sometimes there are months between samples, sometimes there are several samples in one month. There are also many 0 counts

How to deal with such data?

I can graph it easily enough in R, but the graphs are not particularly illuminating, because they are very bumpy.

In terms of modeling - with species modeled as a function of various things - maybe a mixed model (aka multilevel model).

Any references or ideas welcome

Some details in response to comments

There are about 15 species.

I am trying to both get an idea of any trends or seasonality in each fish, and look at how the species are related to each other (my client originally wanted a simple table of correlations)

The goal is descriptive and analytic, not predictive

Further edits: I did find this paper by K. Rehfield et al., which suggests using Gaussian kernels to estimate the ACF for highly irregular time series

http://www.nonlin-processes-geophys.net/18/389/2011/npg-18-389-2011.pdf

I have data for the population of a number of different fish, sampled over a period of about 5 years, but in a very irregular pattern. Sometimes there are months between samples, sometimes there are several samples in one month. There are also many 0 counts

How to deal with such data?

I can graph it easily enough in R, but the graphs are not particularly illuminating, because they are very bumpy.

In terms of modeling - with species modeled as a function of various things - maybe a mixed model (aka multilevel model).

Any references or ideas welcome

Peter

I have data for the population of a number of different fish, sampled over a period of about 5 years, but in a very irregular pattern. Sometimes there are months between samples, sometimes there are several samples in one month. There are also many 0 counts

How to deal with such data?

I can graph it easily enough in R, but the graphs are not particularly illuminating, because they are very bumpy.

In terms of modeling - with species modeled as a function of various things - maybe a mixed model (aka multilevel model).

Any references or ideas welcome

Some details in response to comments

There are about 15 species.

I am trying to both get an idea of any trends or seasonality in each fish, and look at how the species are related to each other (my client originally wanted a simple table of correlations)

The goal is descriptive and analytic, not predictive