Yam Code

New Paste Browse

https://www.selleckchem.com/products/lf3.html Graphical models have received an increasing amount of attention in network psychometrics as a promising probabilistic approach to study the conditional relations among variables using graph theory. Despite recent advances, existing methods on graphical models usually assume a homogeneous population and focus on binary or continuous variables. However, ordinal variables are very popular in many areas of psychological science, and the population often consists of several different groups based on the heterogeneity in ordinal data. Driven by these needs, we introduce the finite mixture of ordinal graphical models to effectively study the heterogeneous conditional dependence relationships of ordinal data. We develop a penalized likelihood approach for model estimation, and design a generalized expectation-maximization (EM) algorithm to solve the significant computational challenges. We examine the performance of the proposed method and algorithm in simulation studies. Moreover, we demonstrate the potential usefulness of the proposed method in psychological science through a real application concerning the interests and attitudes related to fan avidity for students in a large public university in the United States.To describe the clinical manifestations, immunological features, and risk factors in patients with sarcoidosis complicated with autoimmune diseases (ADs) as well as determine the frequency of autoantibodies and possible correlation between autoantibodies and laboratory data. Patients with pathologically confirmed sarcoidosis at Beijing Chaoyang Hospital (China) between January 2017 and October 2020 were included. Age- and sex-matched patients who visited the rheumatology outpatient clinic without systemic or ADs were included as controls. Demographic, clinical, serological, and radiological data of sarcoidosis patients were recorded and analyzed. To exclude ADs, autoantibodies, such as antinuclear antibody, extract

Paste Settings

Paste Title : [Optional]

Paste Folder : [Optional]

Syntax Highlighting : [Optional]

Paste Expiration : [Optional]

Paste Status : [Optional]

Password : [Optional]

Description: [Optional]

Tags: [Optional]

Encrypt Paste (?)

You are currently not logged in, this means you can not edit or delete anything you paste. Sign Up or Login

Paste Settings

Site Languages