A introduction about gt package is here
knitr::opts_chunk$set(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.

A new R packge (gganimate ) provides some new features for animation in R. Its big advantage is it could make use of ggplot API and embeded into ggplot. Next, I will use a sample data to show the example. Then I will use some real educational data to explore a little bit what we can do in psychometric area.
A Simple Example I want to introduce this package.

More details please refer to the link below: (https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html#lin)
This post shows how to use glmnet package to fit lasso regression and how to visualize the output. The description of data is shown in here.
dt <- readRDS(url("https://s3.amazonaws.com/pbreheny-data-sets/whoari.rds")) attach(dt) fit <- glmnet(X, y) Visualize the coefficients plot(fit) Label the path plot(fit, label = TRUE) The summary table below shows from left to right the number of nonzero coefficients (DF), the percent (of null) deviance explained (%dev) and the value of \(\lambda\) (Lambda).

Descrepancy Measures This Blog is the notes for my recent project about reliability and model checking. Next I want to organize a little about one important concept in model checking - discrepancy measures.
Descrepancy Measures \(\chi^2\) measures for item-pairs (Chen & Thissen, 1997) \[ X^2_{jj'}=\sum_{k=0}^{1} \sum_{k'=0}^{1} \frac{(n_{kk'}-E(n_{kk'}))^2}{E(n_{kk'})} \]
\(G^2\) for item pairs
\[ G^2_{jj'}=-2\sum_{k=0}^{1} \sum_{k'=0}^{1} \ln \frac{E(n_{kk'})}{n_{kk'}} \]
model-based covariance (MBC; Reckase, 1997) \[ COV_{jj'} = \frac{\sum_{i=1}^{N}(X_{ij}-\overline{X_j})(X_{ij'}-\overline{X_{j'}}) }{N} \\ MBC_{jj'} = \frac{\sum_{i=1}^{N}(X_{ij}-E(X_{ij}))(X_{ij'}-E(X_{ij'}))}{N} \]

Hi there! This is Jihong. This is a webpage folked from JOSHUA M. ROSENBERG. It aims to provid a very clear example about how to conduct Latent Profile Analysis using MCLUST in r.
Import data and load packages library(tidyverse) library(mclust) library(hrbrthemes) # typographic-centric ggplot2 themes data("iris") df <- select(iris, -Species) # 4 variables explore_model_fit <- function(df, n_profiles_range = 1:9, model_names = c("EII", "VVI", "EEE", "VVV")) { x <- mclustBIC(df, G = n_profiles_range, modelNames = model_names) y <- x %>% as.

Introduction Calibration of Form A Look at the data Plot the density of true \(\theta\) of Group A CTT Table Clean data Classical Test Theory Final Calibration of Form A Model Specification Calibration of Form B Final Calibration of Form A Model Specification of B b-plot a-plot Linking This simulation study is to show how to do IRT Linking Process using mirt R Package.

What is Measurement Invariance (MI)? Why we should use Measurement Invariance? How to use Measurement Invariance Multiple Group CFA Invariance Example (data from Brown Charpter 7): Major Deression Criteria across Men and Women (n =345) Data Import Model Specification Model Options Runing Model Model Comparision STRUCTUAL INVARIANCE TESTS Factor Variance Invariance Model Factor Mean Invariance Model Model Comparision Recently, I was asked by my friend why should we use Measurement Invariance in real research.

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