sys_CE

Calculate the average value of the probability of surprising level of the presence and absence of a particular category within the specific category to measure the average uncertainty of feature categories.

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_CE function

<dl><dt>data</dt>
<dd>A dataframe that contains experimental data.</dd>
<dt>dataset</dt>
<dd>The dataset used to to calculate the ratio of the number of reporting a certain feature in the AgNPs dataset to the total number of samples.</dd></dl>

Arguments

Calculate the Combination Entropy — sys_CE

<dl>

<dt>data</dt>
<dd>A dataframe that contains experimental data.</dd>


<dt>dataset</dt>
<dd>The dataset used to to calculate the ratio of the number of reporting a certain feature in the AgNPs dataset to the total number of samples.</dd>

</dl>

sys_CE: Calculate the Combination Entropy

Description

Usage

Value

Arguments

Examples