Nothing
## ---- echo = FALSE------------------------------------------------------------
###Start with a clean space
# rm(list = ls())
###Take care of some stuff that I don't want the user to see...
# path.package <- "/Users/Sam/Desktop/spCP/"
# suppressMessages(devtools::load_all(path.package)) #loads scripts
# suppressMessages(devtools::document(path.package)) #creates documentation
###Make sure to remove devtools from Suggests line in DESCRIPTION before submission
## -----------------------------------------------------------------------------
library(womblR)
library(spCP)
## -----------------------------------------------------------------------------
head(VFSeries)
## ---- fig.align="center", fig.width = 5.5, fig.height = 5.5-------------------
PlotVfTimeSeries(Y = VFSeries$DLS,
Location = VFSeries$Location,
Time = VFSeries$Time,
main = "Visual field sensitivity time series \n at each location",
xlab = "Days from baseline visit",
ylab = "Differential light sensitivity (dB)",
line.col = 1, line.type = 1, line.reg = FALSE)
## -----------------------------------------------------------------------------
blind_spot <- c(26, 35) # define blind spot
VFSeries <- VFSeries[order(VFSeries$Location), ] # sort by location
VFSeries <- VFSeries[order(VFSeries$Visit), ] # sort by visit
VFSeries <- VFSeries[!VFSeries$Location %in% blind_spot, ] # remove blind spot locations
Y <- VFSeries$DLS # define observed outcome data
## -----------------------------------------------------------------------------
Time <- unique(VFSeries$Time) / 365 # years since baseline visit
print(Time)
## -----------------------------------------------------------------------------
W <- HFAII_Queen[-blind_spot, -blind_spot] # visual field adjacency matrix
M <- dim(W)[1] # number of locations
## -----------------------------------------------------------------------------
DM <- GarwayHeath[-blind_spot] # Garway-Heath angles
## ---- fig.align="center", fig.width = 5.5, fig.height = 5.5-------------------
PlotAdjacency(W = W, DM = DM, zlim = c(0, 180), Visit = NA,
main = "Garway-Heath dissimilarity metric\n across the visual field")
## -----------------------------------------------------------------------------
pdist <- function(x, y) pmin(abs(x - y), (360 - pmax(x, y) + pmin(x, y))) #Dissimilarity metric distance function (i.e., circumference)
DM_Matrix <- matrix(nrow = M, ncol = M)
for (i in 1:M) {
for (j in 1:M) {
DM_Matrix[i, j] <- pdist(DM[i], DM[j])
}
}
BAlpha <- -log(0.5) / min(DM_Matrix[DM_Matrix > 0])
AAlpha <- 0
## -----------------------------------------------------------------------------
Hypers <- list(Alpha = list(AAlpha = AAlpha, BAlpha = BAlpha),
Sigma = list(Xi = 6, Psi = diag(5)),
Delta = list(Kappa2 = 1000))
## -----------------------------------------------------------------------------
Starting <- list(Sigma = 0.01 * diag(5),
Alpha = mean(c(AAlpha, BAlpha)),
Delta = c(0, 0, 0, 0, 0))
## -----------------------------------------------------------------------------
Tuning <- list(Lambda0Vec = rep(1, M),
Lambda1Vec = rep(1, M),
EtaVec = rep(1, M),
Alpha = 1)
## -----------------------------------------------------------------------------
MCMC <- list(NBurn = 1000, NSims = 1000, NThin = 2, NPilot = 5)
## ---- include = FALSE---------------------------------------------------------
reg.spCP <- spCP(Y = Y, DM = DM, W = W, Time = Time, Starting = Starting, Hypers = Hypers, Tuning = Tuning, MCMC = MCMC)
## ---- eval = FALSE------------------------------------------------------------
# reg.spCP <- spCP(Y = Y, DM = DM, W = W, Time = Time,
# Starting = Starting, Hypers = Hypers, Tuning = Tuning, MCMC = MCMC,
# Family = "tobit",
# Weights = "continuous",
# Distance = "circumference",
# Rho = 0.99,
# ScaleY = 10,
# ScaleDM = 100,
# Seed = 54)
#
# ## Burn-in progress: |*************************************************|
# ## Sampler progress: 0%.. 10%.. 20%.. 30%.. 40%.. 50%.. 60%.. 70%.. 80%.. 90%.. 100%..
## -----------------------------------------------------------------------------
names(reg.spCP)
## -----------------------------------------------------------------------------
library(coda)
## -----------------------------------------------------------------------------
Alpha <- as.mcmc(reg.spCP$alpha)
## ---- fig.width = 5.2, fig.height = 5.2, echo = FALSE-------------------------
par(mfrow = c(1, 1))
traceplot(Alpha, ylab = expression(alpha), main = expression(paste("Posterior" ~ alpha)))
## ---- echo = FALSE------------------------------------------------------------
geweke.diag(Alpha)$z
## ---- echo = TRUE, fig.width = 5.2, fig.height = 5.2--------------------------
VFSeries$TimeYears <- VFSeries$Time / 365
PlotCP(reg.spCP, VFSeries, dls = "DLS", time = "TimeYears", location = "Location", cp.line = TRUE, cp.ci = TRUE)
## -----------------------------------------------------------------------------
Diags <- spCP::diagnostics(reg.spCP, diags = c("dic", "dinf", "waic"), keepDeviance = TRUE)
## ---- fig.align = 'center', fig.width = 4, fig.height = 3.3-------------------
Deviance <- as.mcmc(Diags$deviance)
traceplot(Deviance, ylab = "Deviance", main = "Posterior Deviance")
## ---- eval = FALSE------------------------------------------------------------
# print(Diags)
## ---- echo = FALSE------------------------------------------------------------
unlist(Diags$dic)
unlist(Diags$dinf)
unlist(Diags$waic)
## -----------------------------------------------------------------------------
Nu <- length(Time) # calculate number of visits
NewTimes <- Time[Nu] + c(50, 100) / 365
## -----------------------------------------------------------------------------
Predictions <- predict(reg.spCP, NewTimes)
## -----------------------------------------------------------------------------
names(Predictions)
## ---- fig.align = 'center', fig.width = 4.5, fig.height = 4.5-----------------
CPProbs <- apply(reg.spCP$eta, 2, function(x) mean(x < Time[Nu]))
PlotSensitivity(Y = CPProbs,
main = "Probability of an observed \n change point",
legend.lab = expression(paste("Pr[", eta, "(s)] < ", t)), legend.round = 2,
bins = 250, border = FALSE)
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