R

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!

Power Analysis for an Unconditional Growth Model Using a Selected Sample

By Matthew Barstead, Ph.D. in Modeling

June 7, 2018

(Updated: 2020-12-31) Problem Statement Determine whether, given what we know about the target phenomenon, a sample of 120 subjects, measured at three time points is sufficient to detect linear change in an unconditional growth model. A trained phlebotomist once described her experience trying to pierce a patient’s vein with her needle as akin to “Trying to use a toothpick to stick a wet noodle covered by a piece of wax paper.

Gaussian Process Imputation/Forecast Models

By Matthew Barstead, Ph.D. in 77

May 21, 2018

(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.

Visualizing Variance in Multilevel Models Using the Riverplot Package

Power Analysis for an Unconditional Growth Model Using a Selected Sample

Gaussian Process Imputation/Forecast Models

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