Benchmarks of the program using simulated data.

Description

The data file GPvam.benchmark that is included with the package contains runtime and peak memory requirements for different persistence settings, using simulated data sets with different values for number of years, number of teachers per year, and number of students per teacher. These have been multiplied to show the total number of teachers in the data set, as well as the total number of students. With student.side="R", the persistence="GP" model is most sensitive to increases in the size of the data set. With student.side="G", the memory requirements increase exponentially with the number of students and teachers, and that model should not be considered scalable to extremely large data sets.

All of these benchmarks were performed with Hessian=TRUE. Calculation of the Hessian accounts for anywhere from 20% to 75% of those run times. Unless the standard errors of the variance components are needed, leaving Hessian=FALSE will lead to a faster run time with smaller memory requirements.

Usage

data(vam_data)

Examples

data(GPvam.benchmark)
print(GPvam.benchmark[1,])

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(GPvam)
Loading required package: Matrix
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GPvam/GPvam.benchmark.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GPvam.benchmark
> ### Title: Benchmarks of the program using simulated data.
> ### Aliases: GPvam.benchmark
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(GPvam.benchmark)
> print(GPvam.benchmark[1,])
  Persistence Years Students.per.Teacher Teachers.per.Year Total.Students
1          VP     5                   50               150          37500
  Total.Teachers Number.of.Random.Effects.in.Model PEAK.MEMORY..MB.
1            750                               750            532.8
  RUNTIME..min.
1      48.50417
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>