# R/plot.blm.R In bsamGP: Bayesian Spectral Analysis Models using Gaussian Process Priors

```"plot.blm" <- function(x, ...) {
smcmc <- x\$mcmc\$smcmc
param <- x\$mcmc.draws\$beta
wname <- x\$wname
if (x\$model != "gblr") {
sigma2 <- x\$mcmc.draws\$sigma2
param <- cbind(param, sigma2)
wname <- c(wname, "sigma2")
}
p <- ncol(param)
if (p == 2) {
par(mfcol = c(2, 2))
for (i in 1:p) {
plot(1:smcmc, param[, i], xlab = "Iteration", ylab = "", main = wname[i], type = "l")
plot(density(param[, i]), main = "")
}
} else if (p == 3) {
par(mfcol = c(2, 3))
for (i in 1:p) {
plot(1:smcmc, param[, i], xlab = "Iteration", ylab = "", main = wname[i], type = "l")
plot(density(param[, i]), main = "")
}
} else if (p == 4) {
par(mfcol = c(2, 2))
for (i in 1:p) {
plot(1:smcmc, param[, i], xlab = "Iteration", ylab = "", main = wname[i], type = "l")
plot(density(param[, i]), main = "")
par(...)
}
} else {
par(mfcol = c(2, 3))
for (i in 1:p) {
plot(1:smcmc, param[, i], xlab = "Iteration", ylab = "", main = wname[i], type = "l")
plot(density(param[, i]), main = "")
par(...)
}
}
}
```

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bsamGP documentation built on March 26, 2020, 6:31 p.m.