R: PMC RAM Estimate

PMC.RAM {LaplacesDemon}

R Documentation

PMC RAM Estimate

Description

This function estimates the random-access memory (RAM) required to update a given model and data with PMC.

Warning: Unwise use of this function may crash a computer, so please read the details below.

Usage

PMC.RAM(Model, Data, Iterations, Thinning, M, N)

Arguments

`Model`	This is a model specification function. For more information, see `PMC`.
`Data`	This is a list of Data. For more information, see `PMC`.
`Iterations`	This is the number of iterations for which `PMC` would update. For more information, see `PMC`.
`Thinning`	This is the amount of thinning applied to the samples in `PMC`.For more information, see `PMC`.
`M`	This is the number of mixture components in `PMC`.
`N`	This is the number of samples in `PMC`.

Details

The PMC.RAM function uses the object.size function to estimate the size in MB of RAM required to update in PMC for a given model and data, and for a number of iterations and specified thinning. When RAM is exceeded, the computer will crash. This function can be useful when trying to estimate how many samples and iterations to update a model without crashing the computer. However, when estimating the required RAM, PMC.RAM actually creates several large objects, such as post (see below). If too many iterations are given as an argument to PMC.RAM, for example, then it will crash the computer while trying to estimate the required RAM.

The best way to use this function is as follows. First, prepare the model specification and list of data. Second, observe how much RAM the computer is using at the moment, as well as the maximum available RAM. The majority of the difference of these two is the amount of RAM the computer may dedicate to updating the model. Next, use this function with a small number of iterations. Note the estimated RAM. Increase the number of iterations, and again note the RAM. Continue to increase the number of iterations until, say, arbitrarily within 90% of the above-mentioned difference in RAM.

The computer operating system uses RAM, as does any other software running at the moment. R is currently using RAM, and other functions in the LaplacesDemon package, and any other package that is currently activated, are using RAM. There are numerous small objects that are not included in the returned list, that use RAM. For example, perplexity is a small vector, etc.

A potentially large objects that is not included is a matrix used for estimating LML.

Value

PMC.RAM returns a list with several components. Each component is an estimate in MB for an object. The list has the following components:

`alpha`	This is the estimated size in MB of RAM required for the matrix of mixture probabilities by iteration.
`Covar`	This is the estimated size in MB of RAM required for the covariance matrix or matrices.
`Data`	This is the estimated size in MB of RAM required for the list of data.
`Deviance`	This is the estimated size in MB of RAM required for the deviance vector before thinning.
`Initial.Values`	This is the estimated size in MB of RAM required for the matrix or vector of initial values.
`LH`	This is the estimated size in MB of RAM required for the `N \times T \times M` array `LH`, where `N` is the number of samples, `T` is the number of iterations, and `M` is the number of mixture components. The `LH` array is not returned by `PMC`.
`LP`	This is the estimated size in MB of RAM required for the `N \times T \times M` array `LP`, where `N` is the number of samples, `T` is the number of iterations, and `M` is the number of mixture components. The `LP` array is not returned by `PMC`.
`Model`	This is the estimated size in MB of RAM required for the model specification function.
`Monitor`	This is the estimated size in MB of RAM required for the `N \times J` matrix `Monitor`, where `N` is the number of unthinned samples and J is the number of monitored variables. Although it is thinned later in the algorithm, the full matrix is created.
`Posterior1`	This is the estimated size in MB of RAM required for the `N \times J \times T \times M` array `Posterior1`, where `N` is the number of samples, `J` is the number of parameters, `T` is the number of iterations, and `M` is the number of mixture components.
`Posterior2`	This is the estimated size in MB of RAM required for the `N \times J` matrix `Posterior2`, where `N` is the number of samples and `J` is the number of initial values or parameters. Although this is thinned later, at one point it is un-thinned.
`Summary`	This is the estimated size in MB of RAM required for the summary table.
`W`	This is the estimated size in MB of RAM required for the matrix of importance weights.
`Total`	This is the estimated size in MB of RAM required in total to update with `PMC` for a given model and data, and for a number of iterations, specified thinning, mixture components, and number of samples.

Author(s)

Statisticat, LLC. software@bayesian-inference.com