shiny_MVN: Multivariate Normality Checks
In leahpom/MATH5793POMERANTZ: Math 5793 R functions

Description Usage Value User Input Examples

The user uploads a multivariate data set with at least two continuous variables, and it must be a .csv. The app displays the first six entries of the data frame in a table so the user can verify they have selected the right dataset. The user then enters the column number that they are interested in for a Normality Proportion Test. The app will then output two plots with an associated statement saying whether the data passed or failed the check.The first plot shows one standard deviation away from the mean, the second plot shows two standard deviations from the mean. The user also selects the column of data they would like to see a QQ-Plot for. The associated p-value from the Shapiro-Wilk test will also be displayed. The next output is a table displaying the r_q values, where r_q is the correlation coefficient for each column q. There is then a graph showing the ellipse containing approximately 1-α of the data, where α is based on user input. We next have a table showing the generalized distance of each observation for the selected column and the critical value and whether the distance is greater than the critical value. The user can select which rows of the table they want to see. There is then a chi-square plot for the selected column, followed by a table showing the standardized z-values for each column. Again, the user can select which rows of the table they would like to see. To evaluate the bivariate normality, the user then selects two columns of data and the app returns a plot showing the scatterplot for the two variables with marginal dotplots. We then have an updated chi-square plot, which shows the colors of the points based on whether or not the generalized distance is in the upper fifth percentile of the chi-square distribution. We then have a plot for the Box-Cox tranformation. The user selects a column of data that they would like to be transformed and the lambda for the transformation. There is then a plot showing the l(λ) vs λ. This can help the user select which lambda is best for the input. There are then two more plots. The first is a clickable QQ-plot of the Box-Cox transformed data. The user can click on a point on the plot and then another plot will appear, showing the QQ-plot for the Box-Cox transformed data, calculated with the clicked point dropped.

1	shiny_MVN()

A table showing the first six entries of the uploaded data set

A dotplot showing the selected column of data, colored by whether or not the observations are within one standard deviation of the mean

A dotplot showing the selected column of data, colored by whether or not the observations are within two standard deviations of the mean

A scatterplot that is the QQ-plot for the selected column of data

A table that contains the correlation coefficient for each column

A scatterplot showing the ellipse containing approximately 1-α of the data, where α is based on user input.

A table showing the generalized distances and critical value for the selected column of data, where the displayed rows are from user input

A chi-square plot for the selected column of data

A table showing the standardized z-values for each column, where the displayed rows are from user input

An updated chi-square plot for the selected column of data where points greater than the upper fifth quantile are colored red

A plot showing the scatterplot and marginal dotplots for the user-selected columns of data

A plot showing the l(λ) vs. λ to help the user select a good λ value

A QQ-plot of the Box-Cox transformed data, which is clickable

A QQ-plot for the Box-Cox transformed data, calculated having dropped the clicked point from the previous graph

The user has the following input options:

The user inputs a .csv file
The user enters the column number of a variable for a Normal Proportion Check
The user enters the column number of the variable for QQ-Plot and Shapiro-Wilk test
The user enters the column number for the first variable for the bivariate normality check
The user enters the column number for the second variable for the bivariate normality check
The user enters the α value for the bivariate normality check
The user enters the consecutive rows of the Generalized Distances table, separated by a comma
The user enters the consecutive rows of the Standardized Values table, separated by a comma
The user enters the lambda for Box-Cox Transformation
The user enters the column number of the variable for the Box-Cox Evaluation