The final submission for BISC 450C Computational Biology-r

Computational Biology Final Project BISC 450c Lauren Koertge

Cleaned version

Provides R code and sample data for running a random intercept cross-lagged panel model

Bayesian Confirmatory Factor Analysis (BCFA) is a statistical method that incorporates prior distributions for model parameters, offering an alternative to traditional frequentist approaches like maximum likelihood estimation in Confirmatory Factor Analysis (CFA) (Hoofs et al., 2017). BCFA has been utilized in various fields such as psychology, medicine, and education to assess the latent structure of data and determine relationships between constructs (Gracia et al., 2017; Santirso et al., 2018; Liu et al., 2021). It imposes an a priori structure on the data, allowing researchers to confirm or refute specific models based on prior information (Falkenström et al., 2014). BCFA has been employed to examine factor structures, including correlated-factor models, bi-factor models, higher-order factor models, and single general-factor models (Liu et al., 2021). Additionally, BCFA has been used to test existing models across different populations, demonstrating its versatility and applicability in diverse settings (Heinzel, 2023). In the context of confirmatory factor analysis, BCFA provides a robust framework for model estimation, particularly in cases where traditional assumptions may lead to poor model fit (Pan et al., 2017). It allows for the incorporation of informative priors based on expert knowledge or flat uninformative priors, enhancing the analysis process (Bott et al., 2016). BCFA has also been compared with other estimation techniques like penalized maximum likelihood and Markov Chain Monte Carlo methods, showcasing its effectiveness in estimating models with small sample sizes (Lüdtke et al., 2020).

Creating a species richness map of birds in Australia.