Modeling Occurrence of First Migration in Breeding Amphibians: Suggestions?

In summary, the conversation discusses a statistical model for determining the probability of an individual experiencing a specific event on a certain day. The suggested approach is a multinomial logistic regression with a cumulative tracking of independent variables. The study focuses on factors that influence the timing of first migration to breeding ponds for a species of amphibian. The use of similar models in marketing literature is also mentioned as a potential resource.
  • #1
wvguy8258
50
0
Hi,

Does anyone have advice on a good statistical model for the following? I have a dependent variable that is the day that something occurs for an individual. It can occur on any of X number of days for any of Y individuals. It occurs once for each individual, so once it happens the probability of it occurring again is zero. There are a set of independent variables for each day and individual. I would like to model this as a probability that an individual has the occurrence on a specific day, so this would have to take into account what has happened before. I do not believe that logistic regression will work, because of the fact that once the occurrence happens, that individual is out of the data set. Perhaps a multinomial logistic regression with the reponse being day of occurrence and a set of cumulative independent variables to keep track of what happened previous to each day. I do not think a survival/time-to-event analysis is appropriate because time in this case doesn't matter. If conditions for an occurrence are right on day 1, the fact that it is only 1 day into the study doesn't matter.

By the way, the study is looking at factors that contribute to the timing of first migration to breeding ponds during a year's breeding season of a breeding population of a species of amphibian. All animals in the study (those observed to have migrated to breed) are known to have bred that year. It is common knowledge that warm temperatures in early spring with rain cause this migration, but no good analysis has been done so far using several data sets from different years and geographic locations. Thanks. -seth
 
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  • #2
What you described ("a multinomial logistic regression with the reponse being day of occurrence and a set of cumulative independent variables to keep track of what happened previous to each day") sounds right.

One place you might look at is the marketing ("sales") literature. I am not very familiar with all aspects of it, but I know that they've used models like this that track cumulative sales growth. There may be other variants that track an individual's disposition to make a purchase, and the timing of it.
 

Related to Modeling Occurrence of First Migration in Breeding Amphibians: Suggestions?

1. What are model suggestions?

Model suggestions are recommendations for the type of model that can be used in a scientific study or experiment. These models can be mathematical, physical, or conceptual and are used to represent real-world phenomena.

2. How do I choose the right model for my study?

Choosing the right model for your study depends on various factors such as the type of data, research question, and available resources. It is important to thoroughly research and understand the different types of models before making a decision.

3. Can I use more than one model in my study?

Yes, it is common to use multiple models in a study to gain a better understanding of the phenomenon being studied. However, it is important to ensure that the models are compatible and can be integrated effectively.

4. Are there any guidelines for building a model?

There are general guidelines that can be followed when building a model, such as clearly defining the variables, assumptions, and limitations of the model. However, the specific guidelines may vary depending on the type of model and the field of study.

5. Can models be used to make predictions?

Yes, models can be used to make predictions about future events or outcomes. However, the accuracy of these predictions depends on the quality of the model and the data used to build it.

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