HaiBiostat's Posts
Measurement in Social Studies
How to establish the measurements on cognition, stressors and physiological function
The Data Analyst -- Interview Prep & Skills
Data Analyst; Interview Preperation; Skills;
Factor Analysis applied in Multivariate Regression: Machine Learning's Perspective
FA vs. PCA; Conducting an Exploratory Factor Analysis Example; Factor Score; FA applied in Regression;
Exploratory / Confirmatory Factor Analysis?
Correlation matrix; Exploratory Factor Analysis vs. Confirmatory Factor Analysis; Run An Example with Categorical Data in R (psych package);
Structural Equation Modeling: What Fun if We Cannot Run in Mplus
The introduction composed on Lecture Notes from authors: Michael Zyphur, Lesa Hoffman and Johnny Lin; Review of SEM; Read correlation/covariance matrix as input in Mplus & lavaan (R); Run SEM in Mplus & lavaan (R); Model fit indices
Basic Tools of Multivariate Matching
Observational study; Causal Inference; Matching; R
Series 8 - Ordinal Response & Probit Model
How to fit an Ordinal Regression (unconditional/conditional) in Bayes Compare 2 groups
Series 7 -- Fitting a Logistic Regression in Bayes
How to fit a Logistic Regression using Bayesian Methods The advantage of using Bayes to overcome the Separation in Logistic Regression
Series 6 -- ANOVA & ANCOVA
A bit review ANOVA & ANCOVA in the Frequentist's view "ANOVA & ANCOVA" in Bayesian Context Contrast Comparison
Series 5 of 10 -- Introduction to Selection of Variables using Bayesian Approach
Gently Introduce to Variable Selection using Bayesian Approach
Series 4 of 10 -- Fitting Linear Models - Multiple Regression
Review a simple linear regression using Bayesian Methods How to fit a linear multiple regression (Stan) using Bayesian Methods Cases of significant/insignificant, correlated and collinear predictors Shrinkage Bayesian model with or without shrinkage, ridge regression and lasso regression: An example of SAT scores
Series 3 of 10 -- How to Compare Two Groups with Robust Bayesian Estimation
4 years ago, the argument about the stop relying 100% on null hypothesis significance testing (NHST) which was the P-VALUE. A very appealing alternative to NHST is Bayesian statistics, which in itself contains many approaches to statistical inference. In this post, I provide an introductory and practical tutorial to Bayesian parameter estimation in the context of comparing two independent groups' data based on the adaption of UC's lecture and Kruschke's textbook (Chapter 16).
Series 2 of 10 -- MCMC for Estimation of Gaussian parameters
Run MCMC on binomial model Gaussian distribution, one sample Hierarchical model, two groups of Gaussian observations
From Unconditional to Multivariate Bayesian Model Workshop -- Series 1 of 10 -- Gaussian vs Robust Estimation
Fitting an unconditional model (without predictors) Robust estimates -- Gaussian vs. t-distribution
What could we do if Latent Profile Analysis (LPA) run not as expected? -- LPA of Chicago Neighborhoods
American Census Survey data - Chicago Neighborhood; Latent Profile Analysis: Gaussian Mixture modeling; Mclust package; Mplus; Exploratory Factor Analysis;
Selection bias
Understanding: definition, examples; Applying in DAG; General solution;
Matching in R
Observational study; Causal Inference; Matching; R;
Metabolic Syndrome Prevalence across time using NHANES Data
How to correctly approach NHANES data to make estimates that are representative of the population Define Metabolic Syndrome based on ATP III Look at the MetS prevalence over time
Set up to run Mplus inside Rmarkdown
Mplus as a knitr engine in Rmarkdown MplusAutomation: a brief guide
A Note on 5-day Workshop on Mplus ~ Day 1
A Crash Course on Social Psychology Research from Michael Zyphur: Regression - Pathway analysis - Model fit Combination of using Mplus and R Drawing a pathway graph/causal graph using `DiagrammeR` package
Fitting a Simple Linear Regression in Bayesian Context
How to fit a linear regression using Bayesian Methods Consider a Bayesian model fit as a remedial measures for influential case
Discriminant Analysis -- A Classification by Maximizing Class Separation
An Gentle Introduction of Discriminant Analysis & Its Applicant
Machine Learning & Predictive Analytics
An Overview of Machine Learning & Predictive Analytics
Beta Distribution: an Intuitive Explanation
Intuitively explain the Beta Distribution and its applications.
Gamma Distribution: an Intuitive Explanation
Intuitively explain the Gamma Distribution and its applications.