我正试图获得有针对性的加权网络的中心度量.我一直在使用igraph和tnet包R.但是,我发现使用这两个软件包获得的结果存在一些差异,我对这些差异的原因有点困惑.见下文.
require(igraph)
require(tnet)
set.seed(1234)
m <- expand.grid(from = 1:4, to = 1:4)
m <- m[m$from != m$to, ]
m$weight <- sample(1:7, 12, replace = T)
igraph_g <- graph.data.frame(m)
tnet_g <- as.tnet(m)
closeness(igraph_g, mode = "in")
2 3 4 1
0.05882353 0.12500000 0.07692308 0.09090909
closeness(igraph_g, mode = "out")
2 3 4 1
0.12500000 0.06250000 0.06666667 0.10000000
closeness(igraph_g, mode = "total")
2 3 4 1
0.12500000 0.14285714 0.07692308 0.16666667
closeness_w(tnet_g, directed = T, alpha = 1) …我对如何在NIH审查补助金感兴趣.补助金审查程序的运作方式是国会为各种机构(如国家癌症研究所或NCI)分配资金,并向这些机构提交个人补助金.这些机构围绕各种资助重点(例如,癌症,传染病等)进行组织.
但是,在审查补助金时,通常会(但并非总是)将补助金发送给个别研究部门,这些部门更多地围绕科学学科进行组织.因此,如果研究人员向NHLBI提交资助以研究白血病,那么"肿瘤进展"研究部分可以发现自己正在审查来自国家癌症研究所和国家心脏,肺和血液研究所(NHLBI)的资助.
我在R中有一个数据框,看起来像这样:
grant_id <- 1:100
funding_agency <- sample(rep(c("NIAID", "NIGMS", "NHLBI", "NCI", "NINDS"), 20))
study_section <- sample(rep(c("Tumor Cell Biology", "Tumor Progression",
"Vector Biology", "Molecular Genetics",
"Medical Imaging", "Macromolecular Structure",
"Infectious Diseases", "Drug Discovery",
"Cognitive Neuroscience", "Aging and Geriatrics"),
10)
)
total_cost <- rnorm(100, mean = 30000, sd = 10000)
d <- data.frame(grant_id, funding_agency, study_section, total_cost)
some(d)
grant_id funding_agency study_section total_cost
15 15 NINDS Vector Biology 25242.19
19 19 NCI Infectious Diseases 29075.21
50 50 NCI Drug Discovery 25176.35
62 62 …