Posts

I was talking to a colleague about modeling nominal outcomes in STAN, and wrote up this example. Just put it here in case it’s helpful for anyone (probably myself in the future).

In this post, I demonstrate why rescaling a coefficient (i.e., multiplied/divided by a constant) is different from “standardizing” a coefficient (i.e., multiplied/divided by the sample standard deviation, which is a random variable) in SEM.

E-Step M-Step Estimating a 2-PL Model with EM in Julia Find \(\bar r_{jk}\) and \(\bar n_k\) Solve estimating equations Iterations Stopping criteria Benchmarking Remark \[ \newcommand{\bv}[1]{\boldsymbol{\mathbf{#1}}} \]

Import Data Two-Parameter Logistic Model Estimation with mirt in R Marginal Maximum Likelihood (MML) Estimation Implement MML Estimation in Julia Import R data Compute marginal loglikelihood Optimization Benchmarking The semester is finally over, and for some reason, I wanted to consolidate my understanding of some psychometric models, perhaps because I’ll be teaching a graduate measurement class soon.

Load Packages An Example Multilevel Model Nakagawa-Johnson-Schielzeth \(R^2\) Right-Sterba \(R^2\) Confidence Intervals for \(R^2\) Parametric Bootstrap Bias-corrected estimate Confidence intervals Residual Bootstrap Confidence Intervals Bootstrap CI With Transformation Conclusion Load Packages library(lme4) ## Loading required package: Matrix library(MuMIn) # for computing multilevel R-squared library(r2mlm) # another package for R-squared ## Loading required package: nlme ## ## Attaching package: 'nlme' ## The following object is masked from 'package:lme4': ## ## lmList ## Registered S3 method overwritten by 'parameters': ## method from ## format.

Correlation and Standardized Regression In this chapter, you will learn about correlation and its role in regression. To do so, we will use the keith-gpa.csv data to examine whether time

如何管理自己的导师 https://mp.weixin.qq.com/s/XbF3CixJrsPe2o3Z3CsYcA Hugh Kearns & Maria Gardiner, 2011, The care and maintenance of your adviser. Nature. 469. 570. doi:10.1038/nj7331-570a 我大致

Load Packages Data Hypothetical Research Question Distinguishable Dyads Indistinguishable Dyads Model Comparison Using SEM Distinguishable Dyads Indistinguishable Dyads Every time I teach multilevel modeling (MLM) at USC, I have students interested in running the actor partner independence model (APIM) using dyadic model.

oTree简介 oTree是一个基于python的框架，能够让

信号检测论在R中计算 信号检测理论导读：测量与计算 https://www.douban.com/note/784162736/ 信号检测理