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network plots  (STATA Corporation)


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    Structured Review

    STATA Corporation network plots
    Network Plots, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 41091 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/stata-network+plot/STATA+15%2E0/pmc12867776-91-0-8
    Average 99 stars, based on 41091 article reviews
    network plots - by Bioz Stars, 2026-10
    99/100 stars

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    Related Articles

    Transformation Assay:

    Article Title: Diagnostic yield of nine user-friendly bioinformatics tools for predicting Mycobacterium tuberculosis drug resistance: A systematic review and network meta-analysis
    Article Snippet: 22 9 5 16 5 5 22 9 5 16 5 2 22 11 5 16 5 3 22 12 5 16 5 8 22 11 5 16 5 1 25 3 0 3 7 6 25 3 0 3 7 2 25 3 0 3 7 1 27 5 33 5 33 1 28 4 55 5 65 2 29 61 41 61 49 9 29 61 41 61 49 2 29 53 41 53 57 3 29 64 40 65 45 8 29 64 40 65 45 1 30 119 137 137 160 1 33 16 8 16 8 1 #=========================DATA ====================================== library(rstan) library(StanHeaders) library(rstudioapi) library(ggplot2) library(plyr) library(loo) options(mc.cores = parallel::detectCores()) rstan_options(auto_write = TRUE) data <- read.csv("data.csv",sep=",", header=T) data model = " data{ int N; int Nt; int Ns; int TP[N]; int Dis[N]; int TN[N]; int NDis[N]; int Study[N]; int Test[N]; } transformed data{ vector[2] zero; zero[1] <- 0; zero[2] <- 0; } parameters{ matrix[2,Nt] logitmu; matrix[2,Nt] Multi; matrix[Ns,2] nu; matrix[Ns,2] delta[Nt]; vector[2] sigmaw; vector[2] sigmab; real etarho; } transformed parameters{ matrix[2,Nt] MU; matrix[2,Nt] RR; matrix[2,Nt] OR; vector[Nt] DOR; study t1 t2 r1 r2 n1 n2 1 Mykrobe TBProfiler 5 9 10 10 Stata-network plot-'networkplot' The results are shown in Table 3, Table 4 and Supplementary Table 6. vector[Nt] S; matrix[Nt,Nt] A; matrix[Nt,Nt] B; matrix[Nt,Nt] C; matrix[Ns,2] p_i[Nt]; vector[2] sigmawsq; vector[2] sigmabsq; vector[2] sigmasq; real rho; vector[2] rhow; matrix[2, 2] Sigma; for (i in 1:Ns){ for (j in 1:2){ for (k in 1:Nt) p_i[k][i,j] <- inv_logit(logitmu[j,k] +nu[i,j] + delta[k][i,j]); }} for (j in 1:2){ for (k in 1:Nt){ MU[j,k] <- mean(col(p_i[k],j)); }} for (j in 1:2){ for (k in 1:Nt){ RR[j,k] <- MU[j,k]/MU[j,Nt]; OR[j,k] <- (MU[j,k]/(1 - MU[j,k]))/(MU[j,1]/(1 - MU[j,1])); }} for (l in 1:Nt){ DOR[l] <- (MU[1,l]*MU[2,l])/((1 -MU[1,l])*(1 - MU[2,l])); for(m in 1:Nt){ A[l,m] <- if_else((MU[1,l] > MU[1,m]) && (MU[2,l] > MU[2,m]),1,0); B[l,m] <- if_else((MU[1,l] < MU[1,m]) && (MU[2,l] < MU[2,m]),1,0); C[l,m] <- if_else((MU[1,l] == MU[1,m]) && (MU[2,l] == MU[2,m]),1,0); } S[l] <- (2*sum(row(A,l)) + sum(row(C,l)))/(2*sum(row(B,l)) + sum(row(C,l))); } rho <- tanh(etarho); sigmawsq <- (sigmaw).

    Plasmid Preparation:

    Article Title: Diagnostic yield of nine user-friendly bioinformatics tools for predicting Mycobacterium tuberculosis drug resistance: A systematic review and network meta-analysis
    Article Snippet: 22 9 5 16 5 5 22 9 5 16 5 2 22 11 5 16 5 3 22 12 5 16 5 8 22 11 5 16 5 1 25 3 0 3 7 6 25 3 0 3 7 2 25 3 0 3 7 1 27 5 33 5 33 1 28 4 55 5 65 2 29 61 41 61 49 9 29 61 41 61 49 2 29 53 41 53 57 3 29 64 40 65 45 8 29 64 40 65 45 1 30 119 137 137 160 1 33 16 8 16 8 1 #=========================DATA ====================================== library(rstan) library(StanHeaders) library(rstudioapi) library(ggplot2) library(plyr) library(loo) options(mc.cores = parallel::detectCores()) rstan_options(auto_write = TRUE) data <- read.csv("data.csv",sep=",", header=T) data model = " data{ int N; int Nt; int Ns; int TP[N]; int Dis[N]; int TN[N]; int NDis[N]; int Study[N]; int Test[N]; } transformed data{ vector[2] zero; zero[1] <- 0; zero[2] <- 0; } parameters{ matrix[2,Nt] logitmu; matrix[2,Nt] Multi; matrix[Ns,2] nu; matrix[Ns,2] delta[Nt]; vector[2] sigmaw; vector[2] sigmab; real etarho; } transformed parameters{ matrix[2,Nt] MU; matrix[2,Nt] RR; matrix[2,Nt] OR; vector[Nt] DOR; study t1 t2 r1 r2 n1 n2 1 Mykrobe TBProfiler 5 9 10 10 Stata-network plot-'networkplot' The results are shown in Table 3, Table 4 and Supplementary Table 6. vector[Nt] S; matrix[Nt,Nt] A; matrix[Nt,Nt] B; matrix[Nt,Nt] C; matrix[Ns,2] p_i[Nt]; vector[2] sigmawsq; vector[2] sigmabsq; vector[2] sigmasq; real rho; vector[2] rhow; matrix[2, 2] Sigma; for (i in 1:Ns){ for (j in 1:2){ for (k in 1:Nt) p_i[k][i,j] <- inv_logit(logitmu[j,k] +nu[i,j] + delta[k][i,j]); }} for (j in 1:2){ for (k in 1:Nt){ MU[j,k] <- mean(col(p_i[k],j)); }} for (j in 1:2){ for (k in 1:Nt){ RR[j,k] <- MU[j,k]/MU[j,Nt]; OR[j,k] <- (MU[j,k]/(1 - MU[j,k]))/(MU[j,1]/(1 - MU[j,1])); }} for (l in 1:Nt){ DOR[l] <- (MU[1,l]*MU[2,l])/((1 -MU[1,l])*(1 - MU[2,l])); for(m in 1:Nt){ A[l,m] <- if_else((MU[1,l] > MU[1,m]) && (MU[2,l] > MU[2,m]),1,0); B[l,m] <- if_else((MU[1,l] < MU[1,m]) && (MU[2,l] < MU[2,m]),1,0); C[l,m] <- if_else((MU[1,l] == MU[1,m]) && (MU[2,l] == MU[2,m]),1,0); } S[l] <- (2*sum(row(A,l)) + sum(row(C,l)))/(2*sum(row(B,l)) + sum(row(C,l))); } rho <- tanh(etarho); sigmawsq <- (sigmaw).



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