Miscellaneous Functions 'T. Yanagida'
Between-Subject Analysis of Variance
Repeated Measures Analysis of Variance (Within-Subject ANOVA)
Replace User-Specified Values With Missing Values or Missing Values Wi...
Blimp Summary Measures, Convergence and Efficiency Diagnostics
Blimp Trace Plots and Posterior Distribution Plots
Print Blimp Output
Create, Run, and Print Blimp Models
Run Blimp Models
Blimp Input Updating
Centering Predictor Variables in Single-Level and Multilevel Data
Collinearity Diagnostics
Statistical Measures for Leverage, Distance, and Influence
Residual Diagnostics
Colored and Styled Terminal Output Text
Multiple Pattern Matching
Multiple Pattern Matching And Replacements
Omit Strings
Trim Whitespace from String
Confidence Interval for the Difference in Arithmetic Means
Confidence Interval for the Arithmetic Mean and Median
Within-Subject Confidence Interval for the Arithmetic Mean
Confidence Interval for the Difference in Proportions
Confidence Interval for Proportions
Confidence Interval for the Variance and Standard Deviation
Clear Console in RStudio
Cluster Scores
Coding Categorical Variables
Cohen's d
Correlation Matrix
Cross Tabulation
Descriptive Statistics
Extract Duplicated or Unique Rows
Merge Multiple Data Frames
Move Variable(s) in a Data Frame
Combine Data Frames by Rows, Filling in Missing Columns
Rename Columns in a Matrix or Variables in a Data Frame
Data Frame Sorting
Subsetting Data Frames
Dominance Analysis, Manually Inputting a Correlation Matrix
Dominance Analysis
Effect Sizes for Categorical Variables
Frequency Table
Confidence Intervals for the Indirect Effect
Coefficient Alpha and Item Statistics
Confirmatory Factor Analysis
Between-Group and Longitudinal Measurement Invariance Evaluation
Coefficient Omega, Hierarchical Omega, and Categorical Omega
Reverse Code Scale Item
Scale Scores
Create Lagged Variables
Load and Attach Multiple Packages
Mplus Summary Measures, Convergence and Efficiency Diagnostics
Mplus Model Specification for Latent Class Analysis
Plot Mplus GH5 File
Print Mplus Output
Create, Run, and Print Mplus Models
Run Mplus Models
Mplus Input Updating
Multilevel Confirmatory Factor Analysis
Within-Group and Between-Group Correlation Matrix
Multilevel Descriptive Statistics for Two-Level and Three-Level Data
Simultaneous and Level-Specific Multilevel Model Fit Information
Intraclass Correlation Coefficient, ICC(1) and ICC(2)
Confidence Interval for the Indirect Effect in a 1-1-1 Multilevel Medi...
Cross-Level Measurement Invariance Evaluation
Multilevel Composite Reliability
R-Squared Measures for Multilevel and Linear Mixed Effects Models by R...
R-Squared Measures for Multilevel and Linear Mixed Effects Models
Auxiliary Variables Analysis
Variance-Covariance Coverage
Descriptive Statistics for Missing Data in Single-Level, Two-Level and...
Missing Data Indicator Matrix
Missing Data Pattern
Proportion of Missing Data for Each Case
Missing Completely at Random (MCAR) Test
Print misty.object object
Read Stata DTA File
Read Mplus Data File and Variable Names
Read SPSS File
Read Excel File
Recode Variable
Restart R Session
Summary Result Table and Grouped Bar Charts for Latent Class Analysis ...
Unstandardized Coefficients with Heteroscedasticity-Consistent Standar...
Lindell, Brandt and Whitney (1999) r*wg(j) Within-Group Agreement Inde...
Save Copy of the Current Script in RStudio
Open new R Script, R Markdown script, or SQL Script in RStudio
Open, Close and Save R Script in RStudio
Set Working Directory to the Source File Location
Sample Size Determination for Testing Pearson's Correlation Coefficien...
Sample Size Determination for Testing Arithmetic Means
Sample Size Determination for Testing Proportions
Skewness and Kurtosis
Standardized Coefficients
Levene's Test for Homogeneity of Variance
t-Test
Welch's Test
z-Test
Write Stata DTA File
Write Mplus Data File
Write Results of a misty Object into an Excel file
Write SPSS File
Write Excel File
Miscellaneous functions for (1) data management (e.g., grand-mean and group-mean centering, coding variables and reverse coding items, scale and cluster scores, reading and writing Excel and SPSS files), (2) descriptive statistics (e.g., frequency table, cross tabulation, effect size measures), (3) missing data (e.g., descriptive statistics for missing data, missing data pattern, Little's test of Missing Completely at Random, and auxiliary variable analysis), (4) multilevel data (e.g., multilevel descriptive statistics, within-group and between-group correlation matrix, multilevel confirmatory factor analysis, level-specific fit indices, cross-level measurement equivalence evaluation, multilevel composite reliability, and multilevel R-squared measures), (5) item analysis (e.g., confirmatory factor analysis, coefficient alpha and omega, between-group and longitudinal measurement equivalence evaluation), (6) statistical analysis (e.g., confidence intervals, collinearity and residual diagnostics, dominance analysis, between- and within-subject analysis of variance, latent class analysis, t-test, z-test, sample size determination), and (7) functions to interact with 'Blimp' and 'Mplus'.