Why are there missing data in Bayesian data analysis?
Sometimes missing data arise from design, but more often data are missing for reasons that are beyond researchers’ control. I will first provide some conceptual discussion on the types of missing data, and then talk about the Bayesian approach for handling missing data by treating missing data as parameters with some prior information.
How to use your for Bayesian statistics 0.1?
This booklet tells you how to use the R statistical software to carry out some simple analyses using Bayesian statistics. This booklet assumes that the reader has some basic knowledge of Bayesian statistics, and the principal focus of the booklet is not to explain Bayesian statistics, but rather to explain how to carry out these analyses using R.
How can I create missing data in R?
In R, the package mice can be used to perform multiple imputation (to be discussed soon), as well as to create missing data. First, let’s generate some missing completely at random (MCAR) data by randomly removing up to 50% of the data:
What does MCAR mean in Bayesian data analysis?
MCAR means that the probability of a missing response (denoted as R) is unrelated to anything of interest in the research question. For example, for the left graph in Figure 2, Z maybe some haphazard events such as interviewers accidentally erase responses for some people, which we believe to be unrelated to participants’ ages or voting intentions.
Why are there missing data in my research?
Missing data are common in many research problems. Sometimes missing data arise from design, but more often data are missing for reasons that are beyond researchers’ control.
Can a regression line change with no missing data?
As you can see, the regression line barely changes with or without the missing data. In general, under MCAR, using only cases with no missing value still give valid inferences and unbiased estimations.