Sentence examples for log degree from inspiring English sources

We then fit the following model using the generalized linear model with the logit link function in the R programming language: (11) Duplication ∼ log(Degree Centrality) + Duplicated Neighbors + log(Degree Centrality) : Duplicated Neighbors The response was coded as 0 or 1, corresponding to absence or presence of a duplicate paralog from the given WGD, respectively.

To better visualize the impact of network structure and vaccination, we created a network graph that shows the participants' log degree centrality as the size of the node, and the number of infectious events between those nodes as the width and color of the line between them.

Top: Estimated Pearson's R correlation coefficients (95% Confidence Intervals in brackets) between individual Happiness (Subjective Well-Being) vs. individual Popularity (log degree) for All subjects: 0.109 ([0.077, 0.140]), Happy group: 0.126 ([0.081, 0.171]), and unhappy group: -0.047 ([-0.08, -0.013]).

Correlations between protein degree and abundance were evaluated by determining Pearson and Spearman rank correlation coefficients for log(degree) vs. log2 abundance).

To illustrate the general trends in the correlations, or lack of, we generated plots of log

To quantify the structure of our network, we employed four centrality measures: betweenness, degree, time degree, and log time degree.

In contrast, a linear regression between log the degree and log the number of nodes with the degree resulted with R = 0.919 (Fig. 2).

Time degree is normalized and therefore always less than or equal to one, causing log time degree to be always negative, with more negative numbers indicating a lower centrality.

Because time degree aggregates over contact times, and because contact times are often characterized by exponential or power law distributions Contact Data Section [ 6, 10, 15], we also introduce log time degree (LTD) centrality as a measure of contact density.

We visualized the degree distribution of the network that was constructed from the pathway commons data (Additional file 8A), and we found that there were extra high-degree nodes, which disturb the power-law of the log-log degree distribution.

This classification was significantly different from that observed by chance (model χ = 230.3, 2 log likelihood 810.7, degrees of freedom 8, P < 0.001).