Again consider the Seeds example, compile the model for a single chain and load the initial value file and then selected the Uninitialized node option, a window will open containing

The random effects b[1] .. b[21] are unintialized.

For the Seeds example this option opens a window containing

This option will show hidden updaters in the model created by data augmentation algorithms.

For example if the Blockers example using a t distribution for the random effects is compiled this option will open a window containing

The variable names prefixed with aux. are for auxilliary variables introduced when the t distribution were re-written as a scale mixture of normals. delta.new is a stochastic node put in the model for making predictions, it is forward sampled and therefore has negative depth. It has an auxilliary variable [020FC910H] as a parent which is also forward sampled hence the depth of delta.new is -2.

For example if the model described by this Doodle is checked the Model option will open a windows containing the BUGS language code below

Opens a new window containing Latex code describing the currently checked model. The model can be specified in the

For example, if the following model is checked then the latex option will produce:

model {

for (i in 1:K) {

for (j in 1:n) {

Y[i, j] ~ dnorm(eta[i, j], tau.C)

eta[i, j] <- phi[i, 1] / (1 + phi[i, 2] * exp(phi[i, 3] * x[j]))

}

phi[i, 1] <- exp(theta[i, 1])

phi[i, 2] <- exp(theta[i, 2]) - 1

phi[i, 3] <- -exp(theta[i, 3])

for (k in 1:3) {

theta[i, k] ~ dnorm(mu[k], tau[k])

}

}

tau.C ~ dgamma(1.0E-3, 1.0E-3)

sigma.C <- 1 / sqrt(tau.C)

varC <- 1 / tau.C

for (k in 1:3) {

mu[k] ~ dnorm(0, 1.0E-4)

tau[k] ~ dgamma(1.0E-3, 1.0E-3)

sigma[k] <- 1 / sqrt(tau[k])

}

}

Processing the latex code gives

This option displays all the variables in the model that are data as one Splus list object. Only nodes that are defined as data in data files are included: that is nodes that are logical functions of other data nodes are omitted. For the Seeds model the following window is produced, note that x12 although data in the model is omitted.

This option opens a window for each chain in the MCMC simulation showing the current values of the stochastic variable. For the Seeds example after 1000 updates the following output is produced

This dialog box allows information about particular nodes in the model to be obtained. The model must be compiled before this dialog can be used.

As an example of using this dialog compile the Seeds model and then type the node name alpha12 into the node field.

Clicking the value button opens a window displaying

The model has been compiled with one chain and the current value of the alpha12 variable is 0.0

Clicking on the sampler button opens a window displaying

The variable alpha12 has an associated updater using the UpdaterGLM.UpdaterLogit algorithm. This is a block updater, the other members of the block are shown in brackets, size is the number of components in the block and depth is the topological depth of the alpha12 variable in the graphical model. More detailed information about the updater can be gained by clicking on the blue diamond (see the blue diamond section in the developer manual). If the variable in the node field does not have an associated updater then no window will be opened.

Clicking on the type button opens a window displaying

The alpha12 variable in the model is represented by a node of type dnorm. More detailed information about the node can be gained by clicking on the blue diamond (see the blue diamond section in the developer manual).

This option shows how the simulation would be split between multiple core for a given number of MCMC chains and a given number of cores. The number of chains and cores is specified in the model specification dialog and the model must be compiled. For the Seeds example with a single chain and two cores the following output is produced

This option shows how the calculation of deviance is split between cores when multiple cores are used in the simulation. Deviance consists of a sum of terms for each data node in the graphical model. The terms which are calculated on each core are listed. For the Seeds example the following output is produced

Note if the graphical model is large with lots of data this option can produce a huge amount of out put.

This option shows how the nodes are distributed across cores, and provides links that help with debugging.

This option provides information about the worker program created to run MCMC simulation for a particular graphical model on multiple cores. For the Inhaler example running two chains on four cores produces the following output

The modules are arranged in a directed acyclic graph where Clients gives the number of children each module has. Version is the current version of the module, Maintainer is the person who maintains the model (some times this is unknown), Compiled was the date when the module was compiled and Loaded was the time at which the module was loaded. Note that the software can load new modules as it runs as required.

This option displays how much memory the software is using. It is displayed at the bottom left of the screen.