Visualizing Variance in Multilevel Models Using the Riverplot Package

Spurred on by Alex Shackman, I have been working to figure out a good way to visualize different sources of variation in momentary mood. The most common way of visually depicting variance decompositions from the sort of multilevel models we used to analyze our data is a stacked bar plot. So that seemed like a good place to start. Figure 1. Stacked Barplot of Model Variance Decomposition Now, choosing a color scheme that screams “HI I’M A COLOR!

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Gaussian Process Imputation/Forecast Models

(Updated: 2020-12-31) Problem Statement Create a forecast model using only the information available in a single, univariate time series. The Past is Prologue Sometimes the only data we have to predict a particular phenomenon are previous measurements of the target variable we hope to forecast. Using the past to predict the future means that we assume prior trends will continue into the forecast window. Absent other information and all else being equal, making a past is prologue assumption is not a terrible decision in many cases.

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