diff --git a/.DS_Store b/.DS_Store index 26e505b..ab3db3c 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/inst/.DS_Store b/inst/.DS_Store index c0247ef..5fe5bad 100644 Binary files a/inst/.DS_Store and b/inst/.DS_Store differ diff --git a/vignettes/EFAshiny.Rmd b/vignettes/EFAshiny.Rmd index 8ffed26..a276a56 100644 --- a/vignettes/EFAshiny.Rmd +++ b/vignettes/EFAshiny.Rmd @@ -10,39 +10,42 @@ vignette: > --- ## A Tutorial to EFAshiny -[`EFAshiny`](https://psychilin.shinyapps.io/EFAshiny/) is an user-friendly application for exploratory factor analysis (EFA; Bartholomew, Knott, & Moustaki, 2011). The graphical user interface in shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2017) is designed to free users from scripting in R by wrapping together various packages for data management, factor analysis, and graphics. Easy-to-follow analysis flow and reasonable default settings avoiding common errors (Henson & Roberts, 2006) are provided. Results of analysis in tables and graphs are presented on-line and can be exported. +`EFAshiny` is an user-friendly application for exploratory factor analysis (EFA; Bartholomew, Knott, & Moustaki, 2011). The graphical user interface in shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2017) is designed to free users from scripting in R by wrapping together various packages for data management, factor analysis, and graphics. Easy-to-follow analysis flow and reasonable default settings avoiding common errors (Henson & Roberts, 2006) are provided. Results of analysis in tables and graphs are presented on-line and can be exported. Key features include: -- Easy-to-follow analysis flow -- Quick data summary -- Graphical and numerical retention methods -- Lots of extraction and rotation methods -- Confidence intervals of factor loadings -- Visualizations of factor loadings -- Default arguments suggested by current research +- An easy-to-use GUI to free users from scripting in R +- A step by step analysis flow to perform EFA +- Quick ways to summarize data by tables or graphs +- Several ways to explore factor retention numerically or graphically +- Several ways to explore factor extraction and rotation numerically or graphically +- A display of confidence intervals for factor loadings +- Several ways to link visualization of correlation matrix with factor structure +- Default options are chosen according to recommendations in the literature +- A demonstration using a real psychological scale dataset -The intended user of [`EFAshiny`](https://psychilin.shinyapps.io/EFAshiny/) is a behavioral science researcher who seeks an easy way to analyze and visualize item-level scales or datasets with a set of associated variables using EFA. +The `EFAshiny` application is primarily aimed at behavioral researchers who want to perform EFA on a set of associated variables (e.g., item-level scale dataset). Note that it can also be used to explore FA-based connectivity analyses (McLaughlin et al., 1992) in instrument data, such as event related potentials (ERPs) and functional near-infrared spectroscopy (fNIRS). Though the major focus of `EFAshiny` is to perform EFA, it is worth noting that confirmatory factor analysis (CFA) is an useful future direction for `shiny` APP. ## Getting Started -#### 1. Shiny APP version (recommended) -If you want to use the `EFAshiny`, installation is not required. The application is deployed on **shinyapps.io** server.
-Have fun with `EFAshiny` : https://psychilin.shinyapps.io/EFAshiny/ +#### 1. Github version (Full version) +To run `EFAshiny` on your R, [`devtools`](https://cran.r-project.org/web/packages/devtools/devtools.pdf) and [`shiny`](https://cran.r-project.org/web/packages/shiny/shiny.pdf) are required.
-#### 2. Github version -To run `EFAshiny` on your R locally, you have to intsall [`devtools`](https://cran.r-project.org/web/packages/devtools/devtools.pdf).
-Then, intsall `EFAshiny` from Github. ```{r, eval=FALSE} -#install.packages("devtools") -devtools::install_github("PsyChiLin/EFAshiny") +install.packages("devtools") +install.packages("shiny") ``` -To launch the app: +Install and launch `EFAshiny`: ```{r, eval=FALSE} +devtools::install_github("PsyChiLin/EFAshiny") EFAshiny::EFAshiny() ``` +#### 2. Shiny APP version (Standard version) +If you want to use the standard version of `EFAshiny`, installation is not required. The application is deployed on **shinyapps.io** server. This standard version has all the function except for the `Editor` tab (which is only useful for users who want to code online). Users can easily explore and analyze their data with this online APP without worrying about installation.
+Have fun with `EFAshiny` : https://psychilin.shinyapps.io/EFAshiny/ + ## Tutorial @@ -59,8 +62,6 @@ When you open [`EFAshiny`](https://psychilin.shinyapps.io/EFAshiny/), the interf In the `Introduction` tab, you can see the main features for `EFAshiny`, a demo figure, and some key references.
- - #### 3. Data Input The data sets that required the implementations of EFA are typically in a wide format, i.e., one observation per row.
They are composed of a set of responses in one or more psychometric tests in Likert scale.
@@ -74,23 +75,19 @@ In the `Data Input` tab, users can upload the data. If no data is uploaded, `EFAshiny` will use the [Rosenberg Self-Esteem Scale ](https://github.com/PsyChiLin/EFAshiny/blob/master/RSE/RSE.csv) dataset to perform the default demostrations. - - #### 4. Data Summary After uploading the data, the exploratory data analysis should be conducted.
In `Data Summary` tab, three types of explorations are provided. -- **Numeric Statistic**: The first to fourth order moments for each variable were automatically calculated and printed without worrying about inputting any arguments. -- **Distribution**: Histograms that demonstrated numbers of observations conditioned on the points of Likert scale (e.g. 1 to 4 points) reported the distribution of each variable. +- **Numeric Statistic**: The first to fourth order moments for each variable were automatically calculated and printed without worrying about inputting any arguments. Median and MAD are provided as well. +- **Histogram**: Histograms that demonstrated numbers of observations conditioned on the points of Likert scale (e.g. 1 to 4 points) reported the distribution of each variable. +- **Density Plot**: Density plots are provided. Users can visualize the distribution of each item accroding to the histograms and density plots. Note that the histograms and density plots are generated using `plotly` package. In other words, they can be played dynamically. Try it with some clicks ! - **Correlation Matrix**: A bird’s eye view of the pairwise correlation between variables will be illustrated. + **Type of correlation**: Tetrachoric correlations can be adopted to calculate the correlations between bivariates, and Polychoric correlations can be used on dichotomous ordinal variables. The default argument is set to *Pearson’s correlation coefficients*. +- **ggcorrplot**: In addition to the `Correlation Matrix` tab using `corrplot` package, we also provide a `ggcorrplot` version. Have fun with those plots and further get some intuitions. Note that the provided correlation matrix is the basis of EFA, which is a procedure that aim to investigate the underlying structure from the correlations between variables, so either calculating or visualizing the correlation matrix will be really important. - - - - #### 5. Factor Retention One of the central idea of the EFA is to represent a set of observed variables by a smaller number of factors. Thus, selecting how many factors to retain is a critical decision.
@@ -99,16 +96,15 @@ In `Factor Retention` tab, a set of indices to determine numbers of factor are p - **Scree Plot and Parallel Analysis**: Scree Plot (Cattell, 1966) and Parallel Analysis (Horn, 1965) are two popular methods to determine numbers of factor. + **Quantile of Parallel analysis**: Mean, 95th- and 99th-percentile eigenvalues of random data can be used as criteria. + **Number of simulated analyses to perform**: Users can perform more simulation to obstain reliable results. In general, the default 200 is correct enough. -- **Numeric Rules**: Velicer's minimum average partial (MAP, Velicer, 1976) test, RMSEA, BIC and SRMR are also provided as the objective numeric rules. +- **Numeric Rules**: Very simple structure complexitiy (VSS), Velicer's minimum average partial (MAP, Velicer, 1976) test, RMSEA, BIC and SRMR are also provided as the objective numeric rules. + **Max Number of Factor For Estimation**: Users should define their max number of factor to estimate. Should be more than hypothesized. - **Exploratory Graph Analysis (EGA)**: EGA is a new approach, which is based on the graphical lasso with the regularization parameter specified using EBIC, for retaining factors (Golino & Epskamp,2017). + **Number of simulated analyses to perform**: Users can perform more simulation to obstain reliable results. Note that too much simulated analyses will somehow slow down the EGA. +- **Summary**: We provide a easy summary for all these methods. Users can easy make a decision for the number of factors according to the summary. In addition, **Sample Size** is another option for users to validate the results for factor retentions by randomly adjusting different Sample Size.
Although users still have to determine the number of factors upon their own decisions, `EFAshiny` provides users several indices without worrying on methods implementations. - - #### 6. Extraction and Rotation The major step of EFA is to extract and rotate the factors structure, further estimating the factor loadings.
@@ -120,8 +116,6 @@ In `Extraction and Rotation` tab, several factor extraction and rotation methods By providing plenty of factor extraction methods, rotation methods, and useful interval estimations of factor loadings, `EFAshiny` is not only helpful for EFA newbies, but also flexible for EFA users with many experiences. - - #### 7. Diagram For EFA results, the fundamental visualizations is plotting the relationship between factors and indicators.
@@ -129,8 +123,6 @@ In `Diagram` tab, the path diagram representation is provided by using `psych` R It has the structure that all factors and indicators are represented as a bigger or smaller node, and all loadings with absolute values greater than some thresholds (e.g. 0.3) are represented as a line.
Through the graphical representations with flexible plotting options, users can easily understand the factor structure. - - #### 8. Factor Loadings In `Factor Loadings` tab, EFAshiny provides useful visualization of factor loadings to facilitate proper interpretations of extracted factors. @@ -142,8 +134,6 @@ Preacher, 2015), by presenting comparison figure. Users can observe whether the In addition to providing a table of loadings for EFA results, users can automatically get the whole picture of the EFA results through these visualizations. - - #### 9. Summarized Steps We summarize, in six concrete steps, our provided flow in `EFAshiny` for performing EFA. @@ -155,8 +145,21 @@ We summarize, in six concrete steps, our provided flow in `EFAshiny` for perform 5. Visualize the results. Three kinds of plots are shown by EFAshiny. Get a general idea of the results from these visualization. 6. Download and use the results, including figures and tables, in every step for any purpose. +To see the tutorial in vignettes: +```{r, eval=FALSE} +browseVignettes("EFAshiny") +``` + By following this analysis flow in `EFAshiny`, users without any knowledge of programming are able to perform EFA and obtain great understandings for their own studies. +#### 10. R Code for the Github version + +In addition to the GUI, we also provide an `Editor` tab with several code demonstrations in the Github version of `EFAshiny`. In this `Editor` mode (see figure below), we already present some quick examples allowing users to perform similar analyses in `EFAshiny` GUI. Users can also write their own R code here. With this feature users might have the possibility to use `EFAshiny` within a script pipeline. In general, this cool feature allow users to learn R, understand the code underlying analyses in `EFAshiny` or automate the analyses in the future. + +Note that this feature can also allow the use of `lavaan` R package to perform confirmatory factor analysis (CFA), which is also a widely used method but not the main focus of `EFAshiny`. Simply input `require(lavaan)` should work (see [lavaan package](http://lavaan.ugent.be/) for details). Another useful tool is the `showcase` version of `shiny` when running the APP ( definitely, you can directly see the code in `server.R` and `ui.R`). + +In summary, Users who want to further understand `EFAshiny` or learn `R` can (1) see the code in `Editor` tab of github version `EFAshiny` GUI (as shown in figure), (2) download the R markdown file similar to the code in editor mode [here](https://github.com/PsyChiLin/EFAshiny/blob/master/EFAshiny_RCodeDemo.Rmd), (3) see the same R markdown file in [this public link](http://rpubs.com/Chi-Lin/EFAshiny_R_Code_Demo), (4) use `showcase` function in `shiny`, and (5) directly see the code in `server.R` and `ui.R`. + ## Data The dataset for demonstration is the 10-items Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965) via [an online platform for psychological research](http://personality-testing.info/_rawdata/). The RSE was recorded in 1 to 4 Likert scale, where higher scores indicated higher agreements for the items (1=strongly disagree, 2=disagree, 3=agree, and 4=strongly agree). Previous studies suggested that the RSE could be treat as a one factor un-dimensional scale, which simply assessed a positive self-evaluation construct, or a two factor bi-dimensional scale, where one factor is proposed to assess positive self-esteem (e.g. I feel that I have a number of good qualities) with another measuring negative self-esteem (e.g. At times I think I am no good at all). `EFAshiny` already implements a 256 participants RSE data as a built-in dataset, but [RSE.csv](https://github.com/PsyChiLin/EFAshiny/blob/master/RSE/RSE.csv) with [codebook](https://github.com/PsyChiLin/EFAshiny/blob/master/RSE/codebook.txt) can also be directly downloaded. @@ -169,10 +172,13 @@ The dataset for demonstration is the 10-items Rosenberg Self-Esteem Scale (RSE; - [`reshape2`](https://cran.r-project.org/web/packages/reshape2/reshape2.pdf) (Wickham, 2014) - [`EGA`](https://github.com/hfgolino/EGA) (Golino & Epskamp, 2017) - [`ggplot2`](https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf) (Wickham, 2016) +- [`ggcorrplot`](https://cran.r-project.org/web/packages/ggcorrplot/ggcorrplot.pdf) (Kassambara, 2016) - [`gridExtra`](https://cran.r-project.org/web/packages/gridExtra/gridExtra.pdf) (Auguie, 2017) - [`igraph`](https://cran.r-project.org/web/packages/igraph/igraph.pdf) (Csardi & Nepusz, 2006) - [`moments`](https://cran.r-project.org/web/packages/moments/moments.pdf) (Komsta & Novomestky, 2013) +- [`plotly`](https://plot.ly/r/) (Sievert, et al., 2017) - [`psych`](https://cran.r-project.org/web/packages/psych/psych.pdf) (Revelle, 2017) +- [`psycho`](https://cran.r-project.org/web/packages/psycho/index.html) (Makowski, 2018) - [`qgraph`](https://cran.r-project.org/web/packages/qgraph/qgraph.pdf) (Epskamp, et al., 2012) - [`shiny`](https://cran.r-project.org/web/packages/shiny/shiny.pdf) (Chang, Cheng, Allaire, Xie, & McPherson, 2017) - [`shinytheme`](https://cran.r-project.org/web/packages/shinythemes/shinythemes.pdf) (Chang, 2016) @@ -191,8 +197,13 @@ The dataset for demonstration is the 10-items Rosenberg Self-Esteem Scale (RSE; - Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological measurement, 66(3), 393-416. - Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179-185. - Komsta, L., & Novomestky, F. (2013). moments: moments, cumulants, skewness, kurtosis and related tests. R package version 0.13. +- Kassambara, A. (2016). ggcorrplot: Visualization of a Correlation Matrix using'ggplot2'. R package version 0.1.1. +- Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36. +- Makowski, (2018). The psycho Package: an Efficient and Publishing-Oriented Workflow for Psychological Science. Journal of Open Source Software, 3(22), 470. +- McLaughlin, T., Steinberg, B., Christensen, B., Law, I., Parving, A., & Friberg, L. (1992). Potential language and attentional networks revealed through factor analysis of rCBF data measured with SPECT. Journal of Cerebral Blood Flow & Metabolism, 12(4), 535-545. - Revelle, W. (2017) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, R package version 1.7.8. - Rosenberg, M. (1965). Rosenberg self-esteem scale (RSE). Acceptance and commitment therapy. Measures package, 61, 52. +- Sievert, C., Parmer, C., Hocking, T., Chamberlain, S., Ram, K., Corvellec, M., & Despouy, P. (2016). plotly: Create Interactive Web Graphics via ‘plotly. js’. R package version, 4.7.1. - Taiyun Wei and Viliam Simko (2017). R package "corrplot": Visualization of a Correlation Matrix. R package version 0.84. - Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321-327. - Wickham, H. (2016). reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package. R package version 1.4.2. @@ -200,6 +211,7 @@ The dataset for demonstration is the 10-items Rosenberg Self-Esteem Scale (RSE; - Zhang, G., & Preacher, K. J. (2015). Factor rotation and standard errors in exploratory factor analysis. Journal of Educational and Behavioral Statistics, 40(6), 579-603. - Zhang, G. (2014). Estimating standard errors in exploratory factor analysis. Multivariate Behavioral Research, 49, 339-353. + ## Authors [**Chi-Lin Yu**](https://github.com/PsyChiLin) : Department of Psychology, National Taiwan University, Taiwan
[**Ching-Fan Sheu**](http://140.116.183.121/~sheu/) : Institute of Education, National Cheng Kung University, Taiwan
diff --git a/vignettes/EFAshiny.html b/vignettes/EFAshiny.html index 88a1497..4bc755e 100644 --- a/vignettes/EFAshiny.html +++ b/vignettes/EFAshiny.html @@ -12,7 +12,7 @@ - + A Tutorial to EFAshiny @@ -280,38 +280,41 @@

A Tutorial to EFAshiny

Chi-Lin Yu

-

2018-01-22

+

2018-02-10

A Tutorial to EFAshiny

-

EFAshiny is an user-friendly application for exploratory factor analysis (EFA; Bartholomew, Knott, & Moustaki, 2011). The graphical user interface in shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2017) is designed to free users from scripting in R by wrapping together various packages for data management, factor analysis, and graphics. Easy-to-follow analysis flow and reasonable default settings avoiding common errors (Henson & Roberts, 2006) are provided. Results of analysis in tables and graphs are presented on-line and can be exported.

+

EFAshiny is an user-friendly application for exploratory factor analysis (EFA; Bartholomew, Knott, & Moustaki, 2011). The graphical user interface in shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2017) is designed to free users from scripting in R by wrapping together various packages for data management, factor analysis, and graphics. Easy-to-follow analysis flow and reasonable default settings avoiding common errors (Henson & Roberts, 2006) are provided. Results of analysis in tables and graphs are presented on-line and can be exported.

Key features include:

-

The intended user of EFAshiny is a behavioral science researcher who seeks an easy way to analyze and visualize item-level scales or datasets with a set of associated variables using EFA.

+

The EFAshiny application is primarily aimed at behavioral researchers who want to perform EFA on a set of associated variables (e.g., item-level scale dataset). Note that it can also be used to explore FA-based connectivity analyses (McLaughlin et al., 1992) in instrument data, such as event related potentials (ERPs) and functional near-infrared spectroscopy (fNIRS). Though the major focus of EFAshiny is to perform EFA, it is worth noting that confirmatory factor analysis (CFA) is an useful future direction for shiny APP.

Getting Started

-

Data

@@ -433,10 +440,13 @@

Dependencies

  • reshape2 (Wickham, 2014)
  • EGA (Golino & Epskamp, 2017)
  • ggplot2 (Wickham, 2016)
    • +
  • ggcorrplot (Kassambara, 2016)
  • gridExtra (Auguie, 2017)
  • igraph (Csardi & Nepusz, 2006)
  • moments (Komsta & Novomestky, 2013)
    • +
  • plotly (Sievert, et al., 2017)
  • psych (Revelle, 2017)
    • +
  • psycho (Makowski, 2018)
  • qgraph (Epskamp, et al., 2012)
  • shiny (Chang, Cheng, Allaire, Xie, & McPherson, 2017)
  • shinytheme (Chang, 2016)
    • @@ -457,8 +467,13 @@

    References

  • Henson, R. K., & Roberts, J. K. (2006). Use of exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological measurement, 66(3), 393-416.
  • Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179-185.
  • Komsta, L., & Novomestky, F. (2013). moments: moments, cumulants, skewness, kurtosis and related tests. R package version 0.13.
    • +
  • Kassambara, A. (2016). ggcorrplot: Visualization of a Correlation Matrix using’ggplot2’. R package version 0.1.1.
    • +
  • Yves Rosseel (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1-36.
    • +
  • Makowski, (2018). The psycho Package: an Efficient and Publishing-Oriented Workflow for Psychological Science. Journal of Open Source Software, 3(22), 470.
    • +
  • McLaughlin, T., Steinberg, B., Christensen, B., Law, I., Parving, A., & Friberg, L. (1992). Potential language and attentional networks revealed through factor analysis of rCBF data measured with SPECT. Journal of Cerebral Blood Flow & Metabolism, 12(4), 535-545.
  • Revelle, W. (2017) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, R package version 1.7.8.
  • Rosenberg, M. (1965). Rosenberg self-esteem scale (RSE). Acceptance and commitment therapy. Measures package, 61, 52.
    • +
  • Sievert, C., Parmer, C., Hocking, T., Chamberlain, S., Ram, K., Corvellec, M., & Despouy, P. (2016). plotly: Create Interactive Web Graphics via ‘plotly. js’. R package version, 4.7.1.
  • Taiyun Wei and Viliam Simko (2017). R package “corrplot”: Visualization of a Correlation Matrix. R package version 0.84.
  • Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321-327.
  • Wickham, H. (2016). reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package. R package version 1.4.2.