rvsr_plot_shiny

Graph to determine whether we should add a new predictor to the
model already containing other predictors. The residuals from the model is
regressed on the new predictor and if the plot shows non random pattern,
you should consider adding the new predictor to the model.

Tools designed to make it easier for users, particularly beginner/intermediate R users
to build ordinary least squares regression models. Includes comprehensive regression output,
heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence,
model fit assessment and variable selection procedures.

Aravind Hebbali

olsrr

Tools for Building OLS Regression Models

rvsr_plot_shiny function

<dl><dt>model</dt>
<dd>An object of class <code>lm</code>.</dd>
<dt>data</dt>
<dd>A <code>data.frame</code> or <code>tibble</code>.</dd>
<dt>variable</dt>
<dd>Character; new predictor to be added to the <code>model</code>.</dd>
<dt>print_plot</dt>
<dd>logical; if <code>TRUE</code>, prints the plot else returns a plot object.</dd></dl>

Arguments

Residual vs regressors plot for shiny app — rvsr_plot_shiny

<dl>

<dt>model</dt>
<dd>An object of class <code>lm</code>.</dd>


<dt>data</dt>
<dd>A <code>data.frame</code> or <code>tibble</code>.</dd>


<dt>variable</dt>
<dd>Character; new predictor to be added to the <code>model</code>.</dd>


<dt>print_plot</dt>
<dd>logical; if <code>TRUE</code>, prints the plot else returns a plot object.</dd>

</dl>

rvsr_plot_shiny: Residual vs regressors plot for shiny app

Description

Usage

Arguments

Examples