bootPredict

Generate model predictions against a specified set of explanatory levels with bootstrapped confidence intervals.

Make summary tables for descriptive statistics and select explanatory variables
automatically in various regression models. Support linear models, generalized linear
models and cox-proportional hazard models. Generate publication-ready tables summarizing
result of regression analysis and plots. The tables and plots can be exported in "HTML",
"pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents.

KeonWoong Moon

autoReg

Automatic Linear and Logistic Regression and Survival Analysis

Keon-Woong Moon

bootPredict function

<dl><dt>fit</dt>
<dd>An object of class lm or glm</dd>
<dt>newdata</dt>
<dd>A data.frame</dd>
<dt>R</dt>
<dd>Number of simulations. Note default R=100 is very low.</dd>
<dt>type</dt>
<dd>he type of prediction required, see predict.glm. The default for glm models is on the scale of the response variable.
Thus for a binomial model the default predictions are predicted probabilities.</dd>
<dt>...</dt>
<dd>Further arguments to be passed to boot::boot</dd></dl>

Arguments

Bootstrap simulation for model prediction — bootPredict

<dl>

<dt>fit</dt>
<dd>An object of class lm or glm</dd>


<dt>newdata</dt>
<dd>A data.frame</dd>


<dt>R</dt>
<dd>Number of simulations. Note default R=100 is very low.</dd>


<dt>type</dt>
<dd>he type of prediction required, see predict.glm. The default for glm models is on the scale of the response variable.
Thus for a binomial model the default predictions are predicted probabilities.</dd>


<dt>...</dt>
<dd>Further arguments to be passed to boot::boot</dd>

</dl>

bootPredict: Bootstrap simulation for model prediction

Description

Usage

Value

Arguments

Examples