## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment  = "#>",
  fig.width = 6, fig.height = 4
)

## ----setup--------------------------------------------------------------------
library(rsDCM)

## -----------------------------------------------------------------------------
data(toy_dcm)
str(toy_dcm, max.level = 1)
c(regions = toy_dcm$n, scans = toy_dcm$v)

## ----fig.alt = "Simulated BOLD response of two regions to a boxcar input"-----
n   <- 2L
pri <- dcm_fmri_priors(A = matrix(1, n, n),
                       B = array(0, c(n, n, 1)),
                       C = matrix(c(1, 0), n, 1),
                       D = array(0, c(n, n, 0)),
                       options = list())

U <- list(u = matrix(c(rep(1, 16), rep(0, 16)), ncol = 1), dt = 1)
M <- list(f = "dcm_fx_fmri", g = "dcm_gx_fmri", x = pri$x,
          m = ncol(U$u), n = length(pri$x), l = nrow(pri$x), ns = 32)

P <- pri$pE
P$C[1, 1] <- 1     # input drives region 1
P$A[2, 1] <- 0.4   # region 1 -> region 2

y <- dcm_int(P, M, U)

matplot(y, type = "l", lty = 1, xlab = "scan", ylab = "BOLD",
        main = "Simulated response to a boxcar input")
legend("topright", c("region 1", "region 2"), lty = 1, col = 1:2, bty = "n")

## ----eval = FALSE-------------------------------------------------------------
# fit <- dcm_estimate(toy_dcm)

## ----eval = FALSE-------------------------------------------------------------
# round(fit$Ep$A, 3)   # estimated connectivity
# fit$F                # log-evidence

## ----eval = FALSE-------------------------------------------------------------
# ev <- dcm_evidence(fit)
# ev$aic_overall
# ev$bic_overall

## -----------------------------------------------------------------------------
rsdcm_options()$GLOBAL_DX