MCMCplot: MCMC plot

In GCPBayes: Bayesian Meta-Analysis of Pleiotropic Effects Using Group Structure

MCMC plot

Description

Trace plot, density plot and ACF plot for the output of CS/DS/HS. The plot is able to draw at most ten SNPs.

Usage

MCMCplot(
  Result = Result,
  k = k,
  nchains = nchains,
  whichsnps = whichsnps,
  betatype = "l",
  acftype = "correlation",
  dencol = "white",
  denlty = 1,
  denbg = "white"
)

Arguments

Result	All the generated results by CS/DS/HS function.
k	The number of study for drawing plots, k=1,2,...,K.
nchains	Number of Markov chains run in Result.
whichsnps	The name of SNPs.
betatype	The type of plot desired. The following values are possible: "p" for points, "l" for lines, "b" for both points and lines, "c" for empty points joined by lines, "o" for overplotted points and lines, "s" and "S" for stair steps and "h" for histogram-like vertical lines. Finally, "n" does not produce any points or lines.
acftype	String giving the type of ACF to be computed. Allowed values are "correlation" (the default), "covariance" or "partial". Will be partially matched.
dencol	The color for filling the density plot.
denlty	The line type to be used in the density plot.
denbg	The color to be used for the background of the density plot.

Details

Trace plot, density plot and ACF plot for the output of CS/DS/HS for checking convergence of MCMC chains.

Author(s)

Taban Baghfalaki.

References

Baghfalaki, T., Sugier, P. E., Truong, T., Pettitt, A. N., Mengersen, K., & Liquet, B. (2021). Bayesian meta analysis models for cross cancer genomic investigation of pleiotropic effects using group structure. Statistics in Medicine, 40(6), 1498-1518.

Examples

#############################Gene DNAJC1 ###############################################
data(DNAJC1)
Breast <- DNAJC1$Breast
Thyroid <- DNAJC1$Thyroid
genename <- "DNAJC1"
snpnames <- Breast$snp
Betah <- list(Breast$beta, Thyroid$beta)
Sigmah <- list(diag(Breast$se^2), diag(Thyroid$se^2))
K <- 2
m <- 14


RES1 <- DS(Betah, Sigmah,
           kappa0 = 0.5, sigma20 = 1,
           m = m, K = K, niter = 2000, burnin = 1000, nthin = 2, nchains = 1,
           a1 = 0.1, a2 = 0.1, d1 = 0.1, d2 = 0.1, snpnames, genename
)


MCMCplot(Result = RES1, k = 2, nchains = 1, whichsnps = sample(snpnames, 7),
                     betatype = "l",
                     acftype = "correlation",
                     dencol = "white", denlty = 1, denbg = "white")
###################Simulated summary level data with K=5 ###############################
## Not run: 
data(Simulated_summary)
genename <- Simulated_summary$genename
snpnames <- Simulated_summary$snpnames
Betah <- Simulated_summary$simBeta
Sigmah <- Simulated_summary$simSIGMA
K <- 5
m <- 10

RES1 <- DS(Betah, Sigmah,
 kappa0 = c(0.2, 0.5), sigma20 = c(1, 2),
 m = m, K = K, niter = 2000, burnin = 1000, nthin = 2, nchains = 2,
 a1 = 0.1, a2 = 0.1, d1 = 0.1, d2 = 0.1, snpnames, genename)

MCMCplot(Result = RES1, k = 3, nchains = 2, whichsnps = sample(snpnames, 3),
         betatype = "l",
         acftype = "partial",
         dencol = "blue", denlty = 1, denbg = "black")

RES1 <- DS(Betah, Sigmah,
 kappa0 = c(0.2, 0.5, 0.6), sigma20 = c(1, 2, 1.5),
 m = m, K = K, niter = 2000, burnin = 1000, nthin = 2, nchains = 3,
 a1 = 0.1, a2 = 0.1, d1 = 0.1, d2 = 0.1, snpnames, genename)

MCMCplot(Result = RES1, k = 3, nchains = 3, whichsnps = sample(snpnames, 5),
         betatype = "l",
         acftype = "partial",
         dencol = "white", denlty = 1, denbg = "white")
#############################Gene DNAJC1 ###############################################
pvalue <- matrix(0, K, m)
for (k in 1:K) {
  pvalue[k, ] <- 2 * pnorm(-abs(Betah[[k]] / sqrt(diag(Sigmah[[k]]))))
}

zinit <- rep(0, K)
for (j in 1:K) {
  index <- 1:m
  PVALUE <- p.adjust(pvalue[j, ])
 SIGNALS <- index[PVALUE < 0.05]
 modelf1 <- rep(0, m)
 modelf1[SIGNALS] <- 1
  if (max(modelf1) == 1) (zinit[j] <- 1)
}

RES <- CS(Betah, Sigmah,
  kappa0 = 0.5, tau20 = 1, zeta0 = zinit,
 m = m, K = K, niter = 2000, burnin = 1000, nthin = 2, nchains = 1, a1 = 0.1, a2 = 0.1,
c1 = 0.1, c2 = 0.1, sigma2 = 10^-3, snpnames = snpnames, genename = genename)

MCMCplot(Result = RES1, k = 1, nchains = 1, whichsnps = sample(snpnames, 7),
         betatype = "l",
         acftype = "correlation",
         dencol = "white", denlty = 1, denbg = "white")
###################################Gene PARP2 ##########################################
library(BhGLM)
data(PARP2)
Breast <- PARP2$Breast
Thyroid <- PARP2$Thyroid
genename <- "PARP2"
snpnames <- c("rs3093872", "rs3093921", "rs1713411", "rs3093926", "rs3093930", "rs878156")


Fit1 <- BhGLM::bglm(y1~ ., family=binomial(link="logit"),data=Breast)
Betah1 <-  Fit1$coefficients[-1]
Sigmah1 <- cov(coef(arm::sim(Fit1)))[-1,-1]

Fit2 <- BhGLM::bglm(y2~ ., family=binomial(link="logit"),data=Thyroid)
Betah2 <-  Fit2$coefficients[-1]
Sigmah2 <- cov(coef(arm::sim(Fit2)))[-1,-1]

Betah <- list(Betah1,Betah2)
Sigmah <- list(Sigmah1,Sigmah2)
K <- 2
m <- 6


RES1 <- DS(Betah, Sigmah, kappa0=c(0.2,0.5), sigma20=c(1,2),
          m=m, K=K, niter=1000, burnin=500, nthin=1, nchains=2,
          a1=0.1, a2=0.1, d1=0.1, d2=0.1, snpnames, genename)

MCMCplot(Result=RES1, k=1, nchains=2, whichsnps=snpnames,
         betatype = "l",
         acftype = "correlation",
         dencol = "red", denlty = 1, denbg = "white")


RES1 <- DS(Betah, Sigmah, kappa0=c(0.2,0.5), sigma20=c(1,2),
          m=m, K=K, niter=2000, burnin=1000, nthin=2, nchains=2,
          a1=0.1, a2=0.1, d1=0.1, d2=0.1, snpnames, genename)

MCMCplot(Result=RES1, k=1, nchains=2, whichsnps=snpnames,
         betatype = "l",
         acftype = "correlation",
         dencol = "white", denlty = 1, denbg = "white")

## End(Not run)

GCPBayes documentation built on May 29, 2024, 3:30 a.m.