scdeco.pg {scDECO}

ZENCO Poisson Gamma dynamic correlation fitting function

Description

ZENCO Poisson Gamma dynamic correlation fitting function

Usage

scdeco.pg(
  dat,
  b0,
  b1,
  adapt_iter = 100,
  update_iter = 100,
  coda_iter = 1000,
  coda_thin = 5,
  coda_burnin = 100
)

Arguments

dat

matrix containing expression values as first two columns and covariate as third column

b0

intercept of zinf parameter

b1

slope of zinf parameter

adapt_iter

number of adaptation iterations in the jags.model function

update_iter

update iterations in the update function

coda_iter

number of iterations for the coda.sample function

coda_thin

how much to thin the resulting MCMC output

coda_burnin

how many iterations to burn before beginning coda sample collection

Value

MCMC samples that have been adapted, burned, and thinned

Examples

phi1_use <- 4
phi2_use <- 4
phi3_use <- 1/7
mu1_use <- 15
mu2_use <- 15
mu3_use <- 7
b0_use <- -3
b1_use <- 0.1
tau0_use <- -2
tau1_use <- 0.4

simdat <- scdeco.sim.pg(N=1000, b0=b0_use, b1=b1_use,
                        phi1=phi1_use, phi2=phi2_use, phi3=phi3_use,
                        mu1=mu1_use, mu2=mu2_use, mu3=mu3_use,
                        tau0=tau0_use, tau1=tau1_use)

zenco_out <- scdeco.pg(dat=simdat,
                       b0=b0_use, b1=b1_use,
                       adapt_iter=1, # 500,
                       update_iter=1, # 500,
                       coda_iter=5, # 5000,
                       coda_thin=1, # 10,
                       coda_burnin=0) # 1000

boundsmat <- cbind(zenco_out$quantiles[,1],
                   c(1/phi1_use, 1/phi2_use, 1/phi3_use,
                   mu1_use, mu2_use, mu3_use,
                   tau0_use, tau1_use),
                   zenco_out$quantiles[,c(3,5)])

colnames(boundsmat) <- c("lower", "true", "est", "upper")

boundsmat