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Shiny Application Presentation

Richard Ian Carpenter

Shiny App Introduction

This Shiny App was developed as a result of a series of discussions that took place in the forums of the Statistical Inference and Machine Learning courses. The unresoved issue was focused on selecting model parameters and finding the ?best? model.

  • The app uses the mtcars dataset and allows the user to define both the dependent and independent variables in a linear model (lm()) regression.
  • The regressions results appear in the same manner as they would if the summary() function were used along with the regression.

Shiny App Introduction, continued

  • A plot using the selected variables is also generated, plotting the dependent variable along the y-axis, the independent variable(s) along the x-axis, and the regression line ?fitted? among the scatter plot of points.
  • An example of the output is contained in the remaining slides.
  • Additional comments and some unused code are commented out in the ui.R and server.R files.
  • My Shiny app can be found here:
  • My GitHub repository can be found here:

Example Regression Table Output

Here is an example of a regression using the mtcars dataset:

lm(formula = qsec ~ hp, data = mtcars)

    Min      1Q  Median      3Q     Max 
-2.1766 -0.6975  0.0348  0.6520  4.0972 

             Estimate Std. Error t value Pr(>|t|)    
(Intercept) 20.556354   0.542424  37.897  < 2e-16 ***
hp          -0.018458   0.003359  -5.495 5.77e-06 ***
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.282 on 30 degrees of freedom
Multiple R-squared:  0.5016,    Adjusted R-squared:  0.485 
F-statistic: 30.19 on 1 and 30 DF,  p-value: 5.766e-06

Example Regression Plot Output

Here is the example regression plot output, created using ggplot2:

plot of chunk unnamed-chunk-2DATA_ADSENSE

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