sys_discretize

Convert categorical variables into discrete variables

There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of  feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs.

Xiting Wang

sysAgNPs

Systematic Quantification of AgNPs to Unleash their Potential
for Applicability

Longfei Mao

Jiamin Hu

sys_discretize function

<dl><dt>dataset</dt>
<dd>A dataframe of dataset. Datasets used to establish evaluation criteria.</dd>
<dt>vars_to_discretize</dt>
<dd>Variables or columns to be discretized. Default is NULL.</dd></dl>

Arguments

Convert categorical variables into discrete variables — sys_discretize

<dl>

<dt>dataset</dt>
<dd>A dataframe of dataset. Datasets used to establish evaluation criteria.</dd>


<dt>vars_to_discretize</dt>
<dd>Variables or columns to be discretized. Default is NULL.</dd>

</dl>

sys_discretize: Convert categorical variables into discrete variables

Description

Usage

Value

Arguments

Examples