# Recent Posts

### [Pre-print] A Model Comparison Approach to Posterior Predictive Model Checks in Bayesian Confirmatory Factor Analysis

Abstract Posterior Predictive Model Checking (PPMC) is frequently used for model fit evaluation in Bayesian Confirmatory Factor Analysis (BCFA). In standard PPMC procedures, model misfit is quantified by the location of a ML-based estimate to the predictive distribution of a statistic for a model. When the ML-based point estimate is far away from the center of the density of the posterior predictive distribution, model fit is poor. One main critique of such standard PPMC procedures is the strong link to the ML-based point estimates of the observed data.

### Study Notes: gt package and format table

A introduction about gt package is here knitr::opts_chunkset(echo = TRUE, warning = FALSE, message = FALSE, fig.align = "default", eval = TRUE) library(gt) suppressMessages(library(tidyverse)) Basics of gt A basic gt table can be created as so data("iris") glimpse(iris) ## Rows: 150 ## Columns: 5 ## Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4… ## \$ Sepal.Width <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.

### Some Thoughts After Reading <Statistical Rethinking>

Last night, I read the 1st chapter of Statistical Rethinking: A Bayesian Course with Examples in R and Stan from Richard McElreath. I found this is nice book to share with my friends. The core idea of that chapter is the relationship between NULL hypothesis and statistical model. That is, should we trust the statistical models to reject the NULL hypothesis? It is a long history to use statistical models to figure out what is true?

### Use R as a bash language

R language could be easily used as a bash script using Rscript *.R. system() is a R base function which could run command line within R. Below is a simple example which allows to automate create a new blog post: (1) Ask users to type in filename, title and language (2) Create a new markdown file in specific directory (i.e. your local posts saved path) (3) Add some metadata in .

### Introduction to Latent Attribute Network Analysis

A brief introduction of using Network Model to visualize latent attributes’ hierarchy of Diagnostic Modeling.

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# Projects #### Latent Network Diagnostic Classfication Model

A Proposed Model Embeding Networking Modeling with DCM #### Posterior Predictive Model Checking for DCM

Model checking in Diagnostic Classification Models (DCM) is an underdeveloped area

# Teaching

I am a teaching instructor for the following courses at University of Kansas:

• EPSY 906: Latent Trait Measurement and Structural Equation Models