rlr_prediction

a penalized regression model(cv.glmnet).

model

test.data

the name of the variable to be predicted.

variable.pred

numerical. Logarithm of lambda in case you don't want to use the optimal lambda.

log.lambda

generates the prediction of the penalized regression model.

Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.

Oldemar Rodriguez

regressoR

Regression Data Analysis System

Oldemar Rodriguez R.

Andres Navarro D.

Diego Jimenez A.

Ariel Arroyo S.

rlr_prediction function

<dl><dt>model</dt>
<dd>a penalized regression model(cv.glmnet).</dd>
<dt>test.data</dt>
<dd>dataframe.</dd>
<dt>variable.pred</dt>
<dd>the name of the variable to be predicted.</dd>
<dt>log.lambda</dt>
<dd>numerical. Logarithm of lambda in case you don't want to use the optimal lambda.</dd></dl>

Arguments

rlr_prediction — rlr_prediction

<dl>

<dt>model</dt>
<dd>a penalized regression model(cv.glmnet).</dd>


<dt>test.data</dt>
<dd>dataframe.</dd>


<dt>variable.pred</dt>
<dd>the name of the variable to be predicted.</dd>


<dt>log.lambda</dt>
<dd>numerical. Logarithm of lambda in case you don't want to use the optimal lambda.</dd>

</dl>

rlr_prediction: rlr_prediction

Description

Usage

Arguments