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.

Tools for Building OLS Regression Models

package-ubuntu-20.04-r-4.0.5#cran#olsrr-0.5.3

<p>Regress a predictor in the model on all the other predictors.
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>data</code></td>
<td>
<p>A <code>data.frame</code>.</p>
</td>
</tr>
<tr valign="top">
<td><code>i</code></td>
<td>
<p>A numeric vector (indicates the predictor in the model).</p>
</td>
</tr>
</table>

model <- lm(mpg ~ disp + hp + wt, data = mtcars)
data <- ols_prep_avplot_data(model)
ols_prep_regress_x(data, 1)

package-ubuntu-20.04-r-4.0.5#cran#olsrr-0.5.3#docs#ols_prep_regress_x

`data`	A `data.frame`.
`i`	A numeric vector (indicates the predictor in the model).

ols_prep_regress_x

Description

Usage

Arguments

Examples

olsrr

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