We’ve all been there – staring at a dataset with frustrating gaps, wondering how to fill them in without compromising our analysis. That’s exactly what happened to a Reddit user who needed to impute missing oxygen flux values in a time series dataset. They had a good starting point with a linear model using PAR (light) and oxygen concentration as predictors, but there was a problem. During the night, PAR was zero, and oxygen concentration wasn’t entirely accurate, leading to overestimation of flux values.
The user’s question was: can time be used as a predictor in this scenario? It’s a great question, especially since the flux values showed a clear sinusoidal pattern over a 24-hour period.
The Problem with PAR and Oxygen Concentration
PAR is a great predictor for flux during the day, but at night, it’s zero, and oxygen concentration becomes the dominant factor. However, oxygen concentration measurements can be inaccurate due to strong water flow, leading to overestimation of flux values.
Can Time Be a Predictor?
The user wondered if time could be used as a predictor to capture the daily cycle of flux values. It’s not an uncommon approach, especially when dealing with time series data. In this case, using time as a predictor could help account for the variation in flux values that’s not explained by PAR and oxygen concentration.
Other Options for Imputation
While using time as a predictor is a viable option, there are other approaches to consider. For example, the user could explore non-linear models or machine learning algorithms that can handle missing values and non-linear relationships. Additionally, they could consider using other predictors that might be relevant to the oxygen flux values, such as temperature or other environmental factors.
Takeaway
Dealing with missing data is always a challenge, but it’s also an opportunity to think creatively about our analysis. By considering alternative predictors like time, we can develop more robust models that better capture the underlying patterns in our data.
Further reading: Handling Missing Data in R