shiny_anova {ABACUS}

Shiny App to Demonstrate Analysis of Variance

Description

An interactive Shiny app to demonstrate Analysis of Variance.

Usage

shiny_anova()

Details

The interactive Shiny app demonstrates the principles of Analysis of Variance. The true parameter values are provided by the user. The user changes sample characteristics, distribution function and simulation features and explores the influence of these changes on the hypothesis testing using principles of analysis of variance.

The left panel includes the user inputs for Simulation Features, Population Parameters, Sample Characteristics, and Distribution Function. To use the app at first instance, just click the Update button. To alter the input values, edit the text box or move the point on the slider and explore the changes in different tabs (see below).

To obtain identical outcomes in a separate run of the app, set a common seed value at the bottom of the left panel and click Update. All subsequent updates will produce identical results provided other inputs are identical. The seed value is ignored when the option check the box to update instantly is selected.

Value

The outcomes are presented in several tabs.

Population

contains the density plots of three populations and rug plots of the sample units randomly drawn from these populations. It also shows the population parameter values chosen by the user.

Sample

contains the dot plots and box plots of three samples drawn randomly from the three populations and rug plots of the sample units. It also includes the estimates of mean and standard deviation of three samples.

SS & MS

contains the bar plots showing the between and within sum of squares (SS) and mean squares (MS) as well as the proportion of between and within SS over total SS.

Test Statistic

contains the plots showing the mean difference between groups. and corresponding 95% confidence intervals (CI). The tab also contains the distribution of the test statistic F, the observed value of the test statistic and probabilities under the given value of the Type 1 error

Summary

includes the summary of the sampled data and outcomes from the one-way analysis of variance. Different sections are: (1) Hypothesis, highlighting the null and alternative hypothesis; (2) Sample, tabulating the full sampled data; (3) Summary Statistics, summarising the summary information of three samples; (4) Model Outputs, the outputs from fitting the analysis of variance model. The section also present the multiple comparision of means using Tukey's Honest Significant Differences test. This section represents standard R outputs based on fitting an lm function.

Note

https://shiny.abdn.ac.uk/Stats/apps/

Author(s)

Mintu Nath

See Also

Function in base R for normal distribution, F distribution and fitting linear model including dnorm, pnorm, qnorm, rnorm, df, pf, qf, rf, lm, aov, anova.lm, summary.lm summary.aov, model.tables

Examples

if(interactive()){
    library(ggplot2)
    library(shiny)
    library(ABACUS)
    # Run shiny app
    shiny_anova()
}