`spCP`

`spCP`

This is a brief description of how to use the `spCP`

package within the context of glaucoma progression. In order to fully
understand the flexibiltiy of `spCP`

we also load the
`womblR`

package, which has some supportive functions. We
begin by loading the packages.

```
library(womblR)
library(spCP)
```

```
##
## Attaching package: 'spCP'
```

```
## The following object is masked from 'package:womblR':
##
## diagnostics
```

In the `spCP`

package there is a longitudinal series of
visual fields that we will use to exemplify the statistical models
contained in the package. The data object is called
`VFSeries`

and has four variables, `Visit`

,
`DLS`

, `Time`

and `Location`

. The data
object loads automatically; here’s what the data looks like,

`head(VFSeries)`

```
## Visit DLS Time Location
## 1 1 25 0 1
## 2 2 23 126 1
## 3 3 23 238 1
## 4 4 23 406 1
## 5 5 24 504 1
## 6 6 21 588 1
```

The variable `Visit`

represents the visual field test
visit number, `DLS`

the observed outcome variable,
differential light sensitvity, `Time`

the time of the visual
field test (in days from baseline visit) and `Location`

the
spatial location on the visual field that the observation occured. To
help illuminate visual field data we can use the
`PlotVFTimeSeries`

function from the `womblR`

package. `PlotVFTimeSeries`

is a function that plots the
observered visual field data over time at each location on the visual
field.

```
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)
```

The figure above demonstrates the visual field from a Humphrey Field Analyzer-II testing machine, which generates 54 spatial locations (only 52 informative locations, note the 2 blanks spots corresponding to the blind spot). At each visual field test a patient is assessed for vision loss.

`spCP`

We can now begin to think about preparing objects for use in the the
spatially varying change point model function (`spCP`

).
According to the manual, the observed data `Y`

must be first
ordered spatially and then temporally. Furthermore, we will remove all
locations that correspond to the natural blind spot (which in the
Humphrey Field Analyzer-II correspond to locations 26 and 35).

```
<- c(26, 35) # define blind spot
blind_spot <- VFSeries[order(VFSeries$Location), ] # sort by location
VFSeries <- VFSeries[order(VFSeries$Visit), ] # sort by visit
VFSeries <- VFSeries[!VFSeries$Location %in% blind_spot, ] # remove blind spot locations
VFSeries <- VFSeries$DLS # define observed outcome data Y
```

Now that we have assigned the observed outcomed `Y`

we
move onto the temporal variable `Time`

. For visual field data
we define this to be the time from the baseline visit. We obtain the
unique days from the baseline visit and scale them to be on the year
scale.

```
<- unique(VFSeries$Time) / 365 # years since baseline visit
Time print(Time)
```

```
## [1] 0.0000000 0.3452055 0.6520548 1.1123288 1.3808219 1.6109589 2.0712329
## [8] 2.3780822 2.5698630
```

Our example patient has nine visual field visits and the last visit occured 2.57 years after the baseline visit.

We now specify the adjacency matrix, `W`

, and
dissimilarity metric, `DM`

. There are three adjacency
matrices for the Humphrey Field Analyzer-II visual field that are
supplied by the `spCP`

package, `HFAII_Queen`

,
`HFAII_QueenHF`

, and `HFAII_Rook`

.
`HFAII_Queen`

and `HFAII_QueenHF`

both define
adjacencies as edges and corners (i.e., the movements of a queen in
chess), while `HFAII_Rook`

only defines an adjacency as a
neighbor that shares an edge (i.e., a rook in chess). The
`HFAII_QueenHF`

adjacency matrix does not allow neighbors to
share information between the northern and southern hemispheres of the
visual field. In this analysis we use the standard queen specification.
The adjacency objects are preloaded and contain the blind spot, so we
define our adjacency matrix as follows.

```
<- HFAII_Queen[-blind_spot, -blind_spot] # visual field adjacency matrix
W <- dim(W)[1] # number of locations M
```

Now we turn our attention to assigning a dissimilarity metric. The
dissimilarity metric we use in this data application are the
Garway-Heath angles that describe the underlying location that the
retinal nerve fibers enter the optic disc. These angles (measured in
degrees) are included with `spCP`

in the object
`GarwayHeath`

. We create the dissimilarity metric object
`DM`

.

`<- GarwayHeath[-blind_spot] # Garway-Heath angles DM `

The `womblR`

package provides a plotting function
`PlotAdjacency`

that can be used to display a dissimilarity
metric over the spatial structure of the visual field. We demonstrate it
using the Garway-Heath angles.

```
PlotAdjacency(W = W, DM = DM, zlim = c(0, 180), Visit = NA,
main = "Garway-Heath dissimilarity metric\n across the visual field")
```

Now that we have specified the data objects `Y`

,
`DM`

, `W`

and `Time`

, we will customize
the objects that characterize Bayesian Markov chain Monte Carlo (MCMC)
methods, in particular hyperparameters, starting values, metroplis
tuning values and MCMC inputs.

We begin be specifying the hyperparameters for the model. The parameter \(\alpha\) is uniformly distributed with lower bound, \(0\), and upper bound, \(b_{\alpha}\). The upper bound for \(\alpha\) cannot be specified arbitrarily since it is important to account for the magnitude of time elapsed. We specify the following upper bound for \(\alpha\) to dictate a weakly informative prior distribution as specified in Berchuck et al.

```
<- function(x, y) pmin(abs(x - y), (360 - pmax(x, y) + pmin(x, y))) #Dissimilarity metric distance function (i.e., circumference)
pdist <- matrix(nrow = M, ncol = M)
DM_Matrix for (i in 1:M) {
for (j in 1:M) {
<- pdist(DM[i], DM[j])
DM_Matrix[i, j]
}
}<- -log(0.5) / min(DM_Matrix[DM_Matrix > 0])
BAlpha <- 0 AAlpha
```

Then, we can create a hyperparameters `list`

object,
`Hypers`

, that can be used for `spCP`

.

```
<- list(Alpha = list(AAlpha = AAlpha, BAlpha = BAlpha),
Hypers Sigma = list(Xi = 6, Psi = diag(5)),
Delta = list(Kappa2 = 1000))
```

Here, \(\delta\) has a multivariate
normal distribution with mean zero and common variance, \(\kappa^2\), and \(\Sigma\) has an inverse-Wishart
distribution with degrees of freedom \(\xi\) and scale matrix, \(\Psi\) (See the help manual for
`spCP`

for further details).

Specify a `list`

object, `Starting`

, that
contains the starting values for the hyperparameters.

```
<- list(Sigma = 0.01 * diag(5),
Starting Alpha = mean(c(AAlpha, BAlpha)),
Delta = c(0, 0, 0, 0, 0))
```

Provide tuning parameters for the metropolis steps in the MCMC sampler.

```
<- list(Lambda0Vec = rep(1, M),
Tuning Lambda1Vec = rep(1, M),
EtaVec = rep(1, M),
Alpha = 1)
```

We set `Tuning`

to the default setting of all ones and let
the pilot adaptation in the burn-in phase tune the acceptance rates to
the appropriate range. Finally, we set the MCMC inputs using the
`MCMC`

list object.

`<- list(NBurn = 1000, NSims = 1000, NThin = 2, NPilot = 5) MCMC `

We specify that our model will run for a burn-in period of 1,000 scans, followed by 1,000 scans after burn-in. In the burn-in period there will be 5 iterations of pilot adaptation evenly spaced out over the period. Finally, the final number of samples to be used for inference will be thinned down to 500 based on the thinning number of 2. We suggest running the sampler 250,000 iterations after burn-in, but in the vignette we are limited by compilation time.

We have now specified all model objects and are prepared to implement
the `spCP`

regression object. To demonstrate the
`STBDwDM`

object we will use all of its options, even those
that are being used in their default settings.

```
<- spCP(Y = Y, DM = DM, W = W, Time = Time,
reg.spCP 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%..
```

The first line of arguments are the data objects, `Y`

,
`DM`

, `W`

, and `Time`

. These objects
must be specified for `spCP`

to run. The second line of
objects are the MCMC characteristics objects we defined previously.
These objects do not need to be defined for `spCP`

to
function, but are provided for the user to custimize the model to their
choosing. If they are not provided, defaults are given. Next, we specify
that `Family`

be equal to `tobit`

since we know
that visual field data is censored. Our distance metric on the visual
field is based on the circumference of the optic disc, so we define
`Distance`

to be `circumference`

. Finally, we
define the following scalar variables, `Rho`

,
`ScaleY`

, `ScaleDM`

, and `Seed`

, which
are defined in the manual for `spCP`

.

The following are the returned objects from `spCP`

.

`names(reg.spCP)`

```
## [1] "alpha" "delta" "sigma" "beta0" "beta1"
## [6] "lambda0" "lambda1" "eta" "theta" "metropolis"
## [11] "datobj" "dataug" "runtime"
```

The object `reg.spCP`

contains raw MCMC samples for
parameters \(\beta_0(\mathbf{s})\),
\(\beta_1(\mathbf{s})\), \(\lambda_0(\mathbf{s})\), \(\lambda_1(\mathbf{s})\), \(\eta(\mathbf{s})\), \(\theta(\mathbf{s})\), \(\delta\), \(\Sigma\) and \(\alpha\), metropolis acceptance rates and
final tuning parameters (`metropolis`

) and model runtime
(`runtime`

). The objects `datobj`

and
`dataug`

can be ignored as they are for later use in
secondary functions.

Before analyzing the raw MCMC samples from our model we want to
verify that there are no convergence issues. We begin by loading the
`coda`

package.

`library(coda)`

Then we convert the raw `spCP`

MCMC objects to
`coda`

package `mcmc`

objects. We look at \(\alpha\) only for learning purposes.

`<- as.mcmc(reg.spCP$alpha) Alpha `

We begin by checking traceplots of the parameter.

From the figure, it is clear that the traceplots exhibit some poor behavior. However, these traceplots are nicely behaved considering the number of iterations the MCMC sampler ran. The traceplots demonstrate that the parameters have converged to their stationary distribution, but still need more samples to rid themselves of autocorrelation. Finally, we present the corresponding test statistics from the Geweke diagnostic test.

```
## Alpha
## -2.248338
```

Since none of these test statistics are terribly large in the absolute value there is not strong evidence that our model did not converge.

Once we have verified that we do not have any convergence issues, we
can begin to think about analyzing the raw MCMC samples. A nice summary
for `spCP`

is to plot the posterior mean process at each
location along with the posterior mean change point. This is possible
using the `PlotCP`

in the `spCP`

package.

```
$TimeYears <- VFSeries$Time / 365
VFSeriesPlotCP(reg.spCP, VFSeries, dls = "DLS", time = "TimeYears", location = "Location", cp.line = TRUE, cp.ci = TRUE)
```

The `diagnostics`

function in the `spCP`

package can be used to calculate various diagnostic metrics. The
function takes in the `spCP`

regression object.

`<- spCP::diagnostics(reg.spCP, diags = c("dic", "dinf", "waic"), keepDeviance = TRUE) Diags `

```
## Calculating Log-Lik: 0%.. 25%.. 50%.. 75%.. 100%.. Done!
## Calculating PPD: 0%.. 25%.. 50%.. 75%.. 100%.. Done!
```

The `diagnostics`

function calculates diagnostics that
depend on both the log-likelihood and posterior predictive distribtuion.
So, if any of these diagnostics are specified, one or both of these must
be sampled from. The `keepDeviance`

and `keepPPD`

indicate whether or not these distributions should be saved for the
user. We indicate that we would like the output to be saved for the
log-likelihood (i.e., deviance). We explore the output by looking at the
traceplot of the deviance.

```
<- as.mcmc(Diags$deviance)
Deviance traceplot(Deviance, ylab = "Deviance", main = "Posterior Deviance")
```

This distribution has possible convergence issues, however this is not concerning given the number of MCMC iterations run.

`print(Diags)`

```
## dic pd
## -43.02116 84.06801
```

```
## p g dinf
## 10180.88 247611.10 257791.98
```

```
## waic p_waic lppd p_waic_1
## -8.55283 77.61843 81.89485 36.70052
```

The `spCP`

package provides the `predict.spCP`

function for sampling from the posterior predictive distribution at
future time points of the observed data. This is different from the
posterior predictive distribution obtained from the
`diagnostics`

function, because that distribution is for the
observed time points and is automatically obtained given the posterior
samples from `spCP`

. We begin by specifying the future time
points we want to predict as 50 and 100 days past the most recent
visit.

```
<- length(Time) # calculate number of visits
Nu <- Time[Nu] + c(50, 100) / 365 NewTimes
```

Then, we use `predict.spCP`

to calculate the future
posterior predictive distribution.

`<- predict(reg.spCP, NewTimes) Predictions `

`## Future Predictions: 0%.. 25%.. 50%.. 75%.. 100%.. Done!`

We can see that `predict.spCP`

returns a `list`

containing a matrix of predictions corresponding to each future time
point. The name of each matrix is the numeric time point for each future
visit.

`names(Predictions)`

`## [1] "2.70684931506849" "2.84383561643836"`

You can plot a heat map representation of the posterior distribution
of the change points using the function `PlotSensitivity`

from `womblR`

.

```
<- apply(reg.spCP$eta, 2, function(x) mean(x < Time[Nu]))
CPProbs 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)
```

This figure shows the posterior probabiltiy that a change point has
occured in the follow-up period over the visual field. The
`PlotSensitivity`

function can be used for plotting any
observations on the visual field surface.