Index
A
- abline() function / Plotting a slope
- Adsorption
- adsorption dataset
- agrep function / String processing and pattern matching
- Akaike information criterion (AIC) value / The propagate package
- amelia command / The Amelia package
- Amelia package
- about / The Amelia package
- estimates, obtaining from multiply imputed datasets / Getting estimates from multiply imputed datasets
- Analysis of variance (Anova)
- about / Analysis of variance
- anova() function / Analysis of variance
- aov() function / Analysis of variance
- apply() function / The apply() function
- array() function / Multidimensional arrays
- atomic vectors
- about / Atomic vectors
- operations / Operations on vectors
- attributes / Attributes
- Augmented Dickey-Fuller (ADF) / Unit root tests
B
- basic plots
- creating / Basic plots and the ggplot2 package
- Bernoulli random variables / Bernoulli random variables
- binomial exact test / Proportion tests
- Binomial random variables / Binomial random variables
- biplot() function / Principal component analysis
- body measures dataset
- bootstrap approach / Hypothesis testing
- boxplot command / Nonparametric nonlinear methods
- break statement / The repeat{} and break statement
- Brownian motion / Simulating physical systems
- browser() function / General programming and debugging tools
- Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm / More optim() features
C
- CDC
- central limit theorem / Central limit theorem
- centroid method / The centroid method
- CERN's Root
- URL / Memory management in R
- character classes, R
- [aeiou] / Regular expressions
- [AEIOU] / Regular expressions
- [0-9] / Regular expressions
- [a-z] / Regular expressions
- [A-Z] / Regular expressions
- [a-zA-Z0-9] / Regular expressions
- [^0-9] / Regular expressions
- [[$alpha$]] / Regular expressions
- [[$punct$]] / Regular expressions
- [[$print$]] / Regular expressions
- [[$digit$]] / Regular expressions
- chartSeries() function / Unit root tests
- Chi-squared / Two sample hypothesis tests
- cholesky decomposition
- about / Cholesky decomposition
- class() function / Attributes
- classical test theory (CTT)
- about / Calculating Cronbach's alpha
- clustering
- about / Clustering
- comma-separated values (CSV) file
- complete case analysis / Listwise deletion or complete case analysis
- Confidence intervals (CI) / Confidence intervals
- cornode() function / The cornode() function
- correlation matrices
- covariance matrices
- CRAN distributions page
- CRAN page
- URL / Using the mc2d package
- CRAN R project Time Series Analysis website
- URL / Unit root tests
- Cronbach's alpha
- calculating / Calculating Cronbach's alpha
D
- data
- loading, in R / Loading data into R
- data frames / Data frames
- saving / Saving data frames
- dataset / The physical functioning dataset
- about / Datasets used in this chapter
- red wine / Datasets used in this chapter
- abalone / Datasets used in this chapter
- physical functioning / Datasets used in this chapter
- datasets
- about / Datasets
- political democracy / Political democracy
- physical functioning dataset / Physical functioning dataset
- Holzinger-Swineford 1939 dataset / Holzinger-Swineford 1939 dataset
- cleaning, in R / Cleaning datasets in R
- data structures, in R
- about / Data structures in R
- homogeneous / Data structures in R
- heterogeneous / Data structures in R
- atomic vectors / Atomic vectors
- lists / Lists
- attributes / Attributes
- Factors / Factors
- multidimensional arrays / Multidimensional arrays
- data frames / Data frames
- data variability
- about / Data variability
- Confidence intervals (CI) / Confidence intervals
- data visualization / Data visualization
- DCT
- in R / DCT in R
- debugging tools
- deletion
- using, as method / Pairwise deletion
- deletion methods
- about / Deletion methods
- listwise deletion / Listwise deletion or complete case analysis
- complete case analysis / Listwise deletion or complete case analysis
- pairwise deletion / Pairwise deletion
- density functions / Other density functions
- descriptive statistics
- about / Descriptive statistics
- data variability / Data variability
- diagonal matrix / Matrix notation
- dim() function / Attributes
- dimension reduction
- PCA for / PCA for dimension reduction
- distributions
- fitting, statistical tests used / Other statistical tests to fit distributions
- dnorm() function / Probability distributions
E
- EFA
- about / Exploratory factor analysis and reflective constructs
- Latent trait or common factor / Familiarizing yourself with the basic terms
- Path coefficient / Familiarizing yourself with the basic terms
- Communality / Familiarizing yourself with the basic terms
- Uniqueness / Familiarizing yourself with the basic terms
- Observed / Familiarizing yourself with the basic terms
- Implied / Familiarizing yourself with the basic terms
- Orthogonal factor structure / Familiarizing yourself with the basic terms
- Oblique factor structure / Familiarizing yourself with the basic terms
- in matrix model / Expressing factor analysis in a matrix model
- covariance algebra / Basic EFA and concepts of covariance algebra
- estimation / Concepts of EFA estimation
- centroid method / The centroid method
- multiple actors / Multiple actors
- direct factor extraction, by principal axis factoring / Direct factor extraction by principal axis factoring
- principal axis factoring, performing in R / Performing principal axis factoring in R
- extraction methods / Other factor extraction methods
- factor rotation / Factor rotation
- advanced EFA, with psych package / Advanced EFA with the psych package
- effective degrees of freedom (edf) / Generalized additive models
- eigenvalue decomposition
- Element-wise matrix operations
- about / Element-wise matrix operations
- matrix subtraction / Matrix subtraction
- matrix addition / Matrix addition
- matrix sweep / Matrix sweep
- error structure, canonical link functions
- about / Generalized linear models
- estimates
- obtaining, from multiply imputed datasets / Getting estimates from multiply imputed datasets
- evalmcmod() function / The evalmcmod() function
- example, in R / An example in R
- example, lavaan
- defining / Explaining an example in lavaan
- example, OpenMx
- defining / Explaining an example in OpenMx
- example, SEM specification / An example of SEM specification
- Exploratory factor analysis (EFA) / The basic ideas of SEM
- exponential random variables / Exponential random variables
- expression() function / The propagate package
F
- Factor correlation matrix / Matrices of interest
- Factor pattern matrix / Matrices of interest
- factor rotation
- about / Factor rotation
- in R / Factor rotation in R
- factor rotation, methods
- Quartimax rotation / Quartimax rotation
- Varimax rotation / Varimax rotation
- Oblique rotations / Oblique rotations
- Oblimin rotation / Oblimin rotation
- Promax rotation / Promax rotation
- Factors / Factors
- first mean value theorem / Monte Carlo integration
- Fisher's Exact test / Two sample hypothesis tests
- fitDistr() function / The propagate package
- fitMeasures command / The lavaan syntax
- fitting distributions
- about / Fitting distributions
- higher order moments, of distribution / Higher order moments of a distribution
- statistical tests, for fitting distributions / Other statistical tests to fit distributions
- floating point operations
- flow control
- about / Flow control
- for() loop / The for() loop
- if() statement / The if() statement
- while() loop / The while() loop
- repeat{} statement / The repeat{} and break statement
- break statement / The repeat{} and break statement
- for() loop
- about / The for() loop
- apply() function / The apply() function
- formative constructs
- PCA used / Formative constructs using PCA
- functions
- about / Functions
- used, for fitting distributions / Other statistical tests to fit distributions
G
- gam() function / Generalized additive models
- generalized additive models (GAMs)
- about / Generalized additive models
- generalized linear model (GLM)
- about / Generalized linear models
- general non-linear optimization
- about / General non-linear optimization
- getwd() function / Unit root tests
- ggbiplot package
- ggplot2 package
- glm() function / Generalized linear models
- Golden section search method / The golden section search method
- grep function / String processing and pattern matching
- grepl function / String processing and pattern matching
- gridsize argument / Kernel weighted local polynomial fitting
- gsub function / String processing and pattern matching
H
- Holzinger-Swineford 1939 dataset / Holzinger-Swineford 1939 dataset
- hypothesis testing
- about / Hypothesis testing
- proportion tests / Proportion tests
- two sample hypothesis tests / Two sample hypothesis tests
- unit root tests / Unit root tests
I
- identity matrix / Matrix notation
- if() statement / The if() statement
- image
- importing, to R / Importing an image into R
- image compression
- direct consine transform used / Image compression using direct cosine transform
- image, importing into R / Importing an image into R
- about / The compression technique
- matrices, putting together for / Putting the matrices together for image compression
- Implied correlation matrix / Matrices of interest
- importance sampling
- about / Importance sampling
- imputation
- approaches to / Approaches to imputation
- imputation functions, in MICE
- about / Imputation functions in mice
- integer-restricted optimization / Integer-restricted optimization
- intercept, as parameter 1
- about / The intercept as parameter 1
- model, updating / Updating a model
- item response theory (IRT)
- about / Calculating Cronbach's alpha
K
- Kaiser Guttman rule / Choosing the number of principal components to retain
- kernel regression / Kernel regression
- scientific application / A practical scientific application of kernel regression
- kernel weighted local polynomial fitting
- about / Kernel weighted local polynomial fitting
- optimal bandwidth selection / Optimal bandwidth selection
- scientific application, of kernel regression / A practical scientific application of kernel regression
- ksmooth function / Kernel regression
- Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test / Unit root tests
L
- Langmuir adsorption model
- lavaan
- used, for fitting SEM models / Fitting SEM models using lavaan
- OpenMx, comparing to / Comparing OpenMx to lavaan
- example, defining / Explaining an example in lavaan
- lavaan command / The lavaan syntax
- lavaan syntax / The lavaan syntax
- lda() function / Linear discriminant analysis
- length() function / Matrices
- Levels / Factors
- linear algebra
- used, for Rasch analysis / Rasch analysis using linear algebra and a paired comparisons matrix
- Linear discriminant analysis (LDA)
- about / Linear discriminant analysis
- linear framework
- extending / Extending the linear framework
- polynomial regression / Polynomial regression
- polynomial regression, performing in R / Performing a polynomial regression in R
- spline regression / Spline regression
- linear programming
- about / Linear programming
- integer-restricted optimization / Integer-restricted optimization
- unrestricted variables / Unrestricted variables
- linear regression
- about / Linear regression
- slope, plotting / Plotting a slope
- lists / Lists
- listwise deletion / Listwise deletion or complete case analysis
- locally weighted polynomial regression / Locally weighted polynomial regression and the loess function
- locpoly command / Kernel weighted local polynomial fitting
- loess command / Locally weighted polynomial regression and the loess function
- loess function / Locally weighted polynomial regression and the loess function
- lp() function / Unrestricted variables
M
- matrices / Matrices
- about / Matrices and linear algebra
- in R / Matrices in R
- rectangular / Matrix notation
- square / Matrix notation
- diagonal / Matrix notation
- triangular / Matrix notation
- symmetric / Matrix notation
- identity / Matrix notation
- vector / Matrix notation
- sparse matrix / Matrix notation
- A / The reticular action model (RAM)
- S / The reticular action model (RAM)
- F / The reticular action model (RAM)
- matrix
- about / Matrices and linear algebra
- subtraction / Matrix subtraction
- addition / Matrix addition
- sweep / Matrix sweep
- multiplication / Matrix multiplication
- Reduced correlation matrix / Matrices of interest
- Implied correlation matrix / Matrices of interest
- Residual correlation matrix / Matrices of interest
- Factor pattern matrix / Matrices of interest
- Factor correlation matrix / Matrices of interest
- Uniqueness matrix / Matrices of interest
- matrix-wise operations, basic
- about / Basic matrixwise operations
- transposition / Transposition
- matrix multiplication / Matrix multiplication
- matrix inversion / Matrix inversion
- determinants / Determinants
- matrix decomposition
- about / Matrix decomposition
- QR decomposition / QR decomposition
- eigenvalue decomposition / Eigenvalue decomposition
- lower upper decomposition / Lower upper decomposition
- cholesky decomposition / Cholesky decomposition
- singular value decomposition / Singular value decomposition
- matrix inversion
- about / Matrix inversion
- linear equations, systems solving / Solving systems of linear equations
- matrix multiplication
- about / Matrix multiplication
- square matrices, multiplying for social networks / Multiplying square matrices for social networks
- outer products / Outer products
- sparse matrices using / Using sparse matrices in matrix multiplication
- matrix operations
- about / Basic matrix operations
- element-wise matrix operations / Element-wise matrix operations
- matrix-wise operations / Basic matrixwise operations
- matrix representation, SEM
- about / Matrix representation of SEM
- reticular action model (REM) / The reticular action model (RAM)
- example, in R / An example in R
- matrix specification
- mc2d documentation
- mc2d functions
- about / Additional mc2d functions
- mcprobtree() function / The mcprobtree() function
- cornode() function / The cornode() function
- mcmodel() function / The mcmodel() function
- evalmcmod() function / The evalmcmod() function
- data visualization / Data visualization
- mc2d package
- using / Using the mc2d package
- one-dimensional Monte Carlo simulation / One-dimensional Monte Carlo simulation
- two-dimensional Monte Carlo simulation / Two-dimensional Monte Carlo simulation
- mc2d functions / Additional mc2d functions
- multivariate nodes / Multivariate nodes
- mcmodel() function / The mcmodel() function
- mcprobtree() function / The mcprobtree() function
- mean
- extracting / Extracting the mean
- mean() function / Data variability
- Mean Value theorem / Monte Carlo integration
- memory
- R objects, handling in / Handling R objects in memory
- memory management, in R
- about / Memory management in R
- R memory commands / Basic R memory commands
- R objects, handling in memory / Handling R objects in memory
- mice command / Imputation functions in mice
- MICE package
- about / The mice package
- imputation functions / Imputation functions in mice
- missing data
- about / Missing data
- computational aspects, in R / Computational aspects of missing data in R
- statistical considerations / Statistical considerations of missing data
- dealing with / Statistical considerations of missing data
- deletion methods / Deletion methods
- visualizing / Visualizing missing data
- multiple imputation / An overview of multiple imputation
- model
- updating / Updating a model
- fitting / Fitting the model
- model formulas, statistical modeling / Model formulas
- model matrices
- specifying / Specifying the model matrices
- model, fitting / Fitting the model
- Monte Carlo integration
- about / Monte Carlo integration
- multiple integration / Multiple integration
- density functions / Other density functions
- Monte Carlo simulations
- about / Monte Carlo simulations
- central limit theorem / Central limit theorem
- mc2d package, using / Using the mc2d package
- URL / Using the mc2d package
- multidimensional arrays
- about / Multidimensional arrays
- matrices / Matrices
- multiple error terms, statistical modeling / Error terms
- multiple imputation
- about / An overview of multiple imputation
- principle / Imputation basic principles
- multiple integration / Multiple integration
- multiply imputed datasets
- estimates, obtaining from / Getting estimates from multiply imputed datasets
- mean, extracting / Extracting the mean
- standard error of mean, extracting / Extracting the standard error of the mean
- multivariate nodes / Multivariate nodes
N
- names() function / Attributes
- National Health and Nutrition Examination Survey (NHANES) / The physical functioning dataset, Physical functioning dataset
- Nelder-Mead simplex method / The Nelder-Mead simplex method
- Newton-Raphson method / The Newton-Raphson method
- nls command / Theory-driven nonlinear regression
- nonconvergence
- problems / Performing a polynomial regression in R
- nonlinear quantile regression / Nonlinear quantile regression
- nonlinear relationships
- exploring, visually / Visually exploring nonlinear relationships
- nonparametric methods, with np package
- about / Nonparametric methods with the np package
- nonlinear quantile regression / Nonlinear quantile regression
- nonparametric model
- nonparametric nonlinear methods
- about / Nonparametric nonlinear methods
- kernel regression / Kernel regression
- kernel weighted local polynomial fitting / Kernel weighted local polynomial fitting
- locally weighted polynomial regression / Locally weighted polynomial regression and the loess function
- loess function / Locally weighted polynomial regression and the loess function
- npqreg command / Nonlinear quantile regression
- numerical data types
O
- Oblimin rotation / Oblimin rotation
- Oblique rotations / Oblique rotations
- one-dimensional Monte Carlo simulation / One-dimensional Monte Carlo simulation
- one-dimensional optimization
- about / One-dimensional optimization
- Golden section search method / The golden section search method
- optimize() function / The optimize() function
- Newton-Raphson method / The Newton-Raphson method
- Nelder-Mead simplex method / The Nelder-Mead simplex method
- optim() function / More optim() features
- OpenMx
- using / Using OpenMx and matrix specification of an SEM
- defining / Summarizing the OpenMx approach
- comparing, to lavaan / Comparing OpenMx to lavaan
- example, defining / Explaining an example in OpenMx
- optim() function / More optim() features
- optimal bandwidth selection / Optimal bandwidth selection
- optimization packages
- about / Other optimization packages
- URL / Other optimization packages
- optimize() function / The optimize() function
- overimpute command / The Amelia package
P
- pairwise deletion / Pairwise deletion
- parametric model
- parametric regression models
- advantages / Nonparametric and parametric models
- disadvantages / Nonparametric and parametric models
- paste() function / Probability distributions
- path diagram
- about / Path diagram
- observed variable / Path diagram
- latent variable / Path diagram
- causal path / Path diagram
- residual / Path diagram
- correlation / Path diagram
- pattern matching
- PCA
- about / Principal component analysis and total variance
- basics / Understanding the basics of PCA
- relating, to SVD / How does PCA relate to SVD?
- scaled versus unscaled PCA / Scaled versus unscaled PCA
- for dimension reduction / PCA for dimension reduction
- for summarizing wine properties / PCA to summarize wine properties
- number of principal components to retain, selecting / Choosing the number of principal components to retain
- used, for formative constructs / Formative constructs using PCA
- physical functioning dataset / Physical functioning dataset
- physical systems
- simulating / Simulating physical systems
- plot() function / Probability distributions, Unit root tests, Linear programming
- plot3d() function
- about / Principal component analysis
- Poisson random variables / Poisson random variables
- political democracy / Political democracy
- polynomial regression
- about / Polynomial regression
- performing, in R / Performing a polynomial regression in R
- prcomp
- and princomp / Understanding the basics of PCA
- prcomp() function / Principal component analysis
- predict() function / Linear discriminant analysis
- predict command / Locally weighted polynomial regression and the loess function
- principal axis factoring (PAF)
- principal component
- about / Understanding the basics of PCA
- to retain, selecting / Choosing the number of principal components to retain
- Principal component analysis (PCA)
- about / Principal component analysis
- princomp
- and prcomp / Understanding the basics of PCA
- print() function / Two-dimensional Monte Carlo simulation
- probability distributions
- about / Probability distributions
- programming tools
- Promax rotation / Promax rotation
- prop.test() function / Two sample hypothesis tests
- propagate() function / The propagate package
- propagate package / The propagate package
- proportion tests / Proportion tests
- pseudorandom numbers
- about / Pseudorandom numbers
- runif() function / The runif() function
- Bernoulli random variables / Bernoulli random variables
- Binomial random variables / Binomial random variables
- Poisson random variables / Poisson random variables
- exponential random variables / Exponential random variables
Q
- qnorm() / Probability distributions
- QR decomposition
- about / QR decomposition
- qt() function / Confidence intervals
- quadratic programming
- about / Quadratic programming
- Quantile-Quantile plot (Q-Q plot) / Fitting distributions
- quantization matrix
- Quartimax rotation / Quartimax rotation
R
- R
- data, loading into / Loading data into R
- polynomial regression, performing in / Performing a polynomial regression in R
- matrices / Matrices in R
- vectors / Vectors in R
- image, importing into / Importing an image into R
- DCT / DCT in R
- datasets, cleaning in / Cleaning datasets in R
- computational aspects, of missing data / Computational aspects of missing data in R
- Rasch analysis
- linear algebra used / Rasch analysis using linear algebra and a paired comparisons matrix
- rbern() function / Bernoulli random variables
- rectangular matrix / Matrix notation
- Reduced correlation matrix / Matrices of interest
- Regular Expressions
- regular expressions
- about / Regular expressions
- . / Regular expressions
- $ / Regular expressions
- ? / Regular expressions
- * / Regular expressions
- + / Regular expressions
- ^ / Regular expressions
- | / Regular expressions
- [ ] / Regular expressions
- { } / Regular expressions
- \\d / Regular expressions
- \\D / Regular expressions
- \\s / Regular expressions
- \\S / Regular expressions
- \\w / Regular expressions
- \\W / Regular expressions
- rejection sampling
- about / Rejection sampling
- rep() function / Atomic vectors
- repeat{} statement / The repeat{} and break statement
- require() function / Loading data into R
- Residual correlation matrix / Matrices of interest
- result argument / The cornode() function
- Reticular action model (RAM) / Matrix representation of SEM
- reticular action model (REM)
- about / The reticular action model (RAM)
- example, SEM specification / An example of SEM specification
- R memory commands / Basic R memory commands
- rnom() function / The propagate package
- rnorm() function / Two-dimensional Monte Carlo simulation
- R objects
- handling, in memory / Handling R objects in memory
- Root Mean Square Error of Approximation (RMSEA) / Advanced EFA with the psych package
- Root Mean Square Residual (RMSR) / Advanced EFA with the psych package
- round() function / Descriptive statistics
- rpois() / Probability distributions
- runif() function / The runif() function
S
- sample() function / Basic sample simulations in R
- sapply command / Extracting the mean
- scaled PCA
- versus unscaled PCA / Scaled versus unscaled PCA
- scatter.smooth command / Visually exploring nonlinear relationships
- Screen test / Choosing the number of principal components to retain
- SEM
- about / The basic ideas of SEM
- SEM model
- components / Components of an SEM model
- observed variable / Components of an SEM model
- latent variable / Components of an SEM model
- path / Components of an SEM model
- residual / Components of an SEM model
- covariance algebra / Components of an SEM model
- path diagram / Path diagram
- fitting / SEM model fitting and estimation methods
- estimation methods / SEM model fitting and estimation methods
- fit, assessing / Assessing SEM model fit
- OpenMx, using / Using OpenMx and matrix specification of an SEM
- matrix specification, using / Using OpenMx and matrix specification of an SEM
- OpenMx, defining / Summarizing the OpenMx approach
- example / Explaining an entire example
- model matrices, specifying / Specifying the model matrices
- fitting, lavaan used / Fitting SEM models using lavaan
- lavaan syntax / The lavaan syntax
- SEM model fit
- assessing / Assessing SEM model fit
- indices / Assessing SEM model fit
- seq() function / Atomic vectors
- setwd() function / Unit root tests
- simulations, R
- about / Basic sample simulations in R
- single imputation / Imputation basic principles
- singular value decomposition
- about / Singular value decomposition
- skewness() function / Higher order moments of a distribution
- slope, linear regression
- plotting / Plotting a slope
- solnp() function / General non-linear optimization
- sparse matrices
- using, in matrix multiplication / Using sparse matrices in matrix multiplication
- sparse matrix / Matrix notation
- Spectral Tests
- URL / Pseudorandom numbers
- spline regression / Spline regression
- sqrt() function / Data variability
- square matrix / Matrix notation
- standard deviation / Data variability
- standard error, of mean
- extracting / Extracting the standard error of the mean
- statistical considerations, missing data
- about / Statistical considerations of missing data
- missing completely at random (MCAR) / Statistical considerations of missing data
- missing at random (MAR) / Statistical considerations of missing data
- missing not at random (MNAR) / Statistical considerations of missing data
- statistical modeling
- about / An overview of statistical modeling
- model formulas / Model formulas
- variables, interaction between / Explanatory variables interactions
- multiple error terms / Error terms
- intercept, as parameter 1 / The intercept as parameter 1
- statistical tests
- used, for fitting distributions / Other statistical tests to fit distributions
- propagate package / The propagate package
- str() function / Atomic vectors
- string processing
- sub function / String processing and pattern matching
- sum() function / Data variability
- summary() function / Descriptive statistics
- summary command / Theory-driven nonlinear regression
- SVD
- PCA, relating to / How does PCA relate to SVD?
- symbols, statistical modeling
- - / Model formulas
- * / Model formulas
- / / Model formulas
- | / Model formulas
- $ / Model formulas
- I / Model formulas
- symmetric matrix / Matrix notation
- system.time command / Handling R objects in memory
T
- theory-driven nonlinear regression
- tilde (~) symbol
- about / Model formulas
- transformation matrix
- tri.mat function / Triangular matrices
- triangular matrices / Triangular matrices
- triangular matrix / Matrix notation
- ts() function / Unit root tests
- Tucker-Lewis Index (TLI) / Advanced EFA with the psych package
- two-dimensional Monte Carlo simulation / Two-dimensional Monte Carlo simulation
- two sample hypothesis tests / Two sample hypothesis tests
U
- UCI Machine Learning Repository / Datasets used in this chapter
- UC Irvine Machine Learning Repository dataset / Datasets used in this chapter
- Uniqueness matrix / Matrices of interest
- unit root tests / Unit root tests
- unrestricted variables / Unrestricted variables
- unscaled PCA
- versus scaled PCA / Scaled versus unscaled PCA
V
- variables
- interactions between / Explanatory variables interactions
- Varimax rotation / Varimax rotation
- vector matrix / Matrix notation
- vectors
- about / Matrices and linear algebra
- in R / Vectors in R
W
- while() loop / The while() loop
- wilcox.test() function / Hypothesis testing
- Wilcoxon signed-rank test / Hypothesis testing
- with.imputationList command / Extracting the standard error of the mean
- with command / Imputation functions in mice
Z
- Z-test / Proportion tests