fit_models

Fit several models with different response variables

Frequently one needs a convenient way to build and tune
several models in one go.The goal is to provide a number of machine learning convenience
functions. It provides the ability to build, tune and obtain predictions of
several models in one function. The models are built using functions from
'caret' with easier to read syntax.
Kuhn(2014) <doi:10.48550/arXiv.1405.6974>.

Nelson Gonzabato

manymodelr

Build and Tune Several Models

fit_models function

<dl><dt>df</dt>
<dd>A data.frame object</dd>
<dt>yname</dt>
<dd>The outcome variable</dd>
<dt>xname</dt>
<dd>The predictor variable(s)</dd>
<dt>modeltype</dt>
<dd>A character specifying the model type e.g lm for 
linear model</dd>
<dt>drop_non_numeric</dt>
<dd>Should non numeric columns be dropped? 
Defaults to FALSE</dd>
<dt>...</dt>
<dd>Other arguments to specific model types.</dd></dl>

Arguments

Fit several models with different response variables — fit_models

<dl>

<dt>df</dt>
<dd>A data.frame object</dd>


<dt>yname</dt>
<dd>The outcome variable</dd>


<dt>xname</dt>
<dd>The predictor variable(s)</dd>


<dt>modeltype</dt>
<dd>A character specifying the model type e.g lm for 
linear model</dd>


<dt>drop_non_numeric</dt>
<dd>Should non numeric columns be dropped? 
Defaults to FALSE</dd>


<dt>...</dt>
<dd>Other arguments to specific model types.</dd>

</dl>

fit_models: Fit several models with different response variables

Description

Usage

Value

Arguments

Examples