CHARMSTONE RSTUDIO ANALYSIS TRIAL

The data that make up a time series are the individual points which are considered to be random draws from an underlying stochastic process. For example, you might think of the daily closing prices of your favorite stock. To fix a mental model of this idea, first consider an ordinary time series. The data in FDA are smooth curves (or surfaces) in time or space. I will also point out some of the available resources that a newcommer to FDA should find helpful.įDA is a branch of statistics that deals with data that can be conceptualized as a function of an underlying, continuous variable. I’ll show some “first steps” code, but most of the post will be about providing background and motivation for looking into FDA. This post is meant to be a “gentle” introduction to doing FDA with R for someone who is totally new to the subject. In a scenario like this, you might find Functional Data Analysis (FDA) to be a viable alternative to the usual multi-level, mixed model approach. The curve looks like a time series with obvious correlations among the points, but there are not enough measurements to model the data with the usual time series methods. Or it could represent any series of longitudinal data where the measurements are taken at irregular intervals.

CHARMSTONE RSTUDIO ANALYSIS TRIAL

This plot might depict 80 measurements for a participant in a clinical trial where each data point represents the change in the level of some protein level. Suppose you have data that looks something like this.

Charmstone rstudio analysis