How can I get data?
That depends on whether you have a suitable .R file. You can either obtain one from the metadata repository that serves as a common place to share benchmarking setups (BDLP Repository). If you do not have such a file, you need to write one yourself - the package does not provide an implementation that allows immediate data generation by just calling a function and passing a few arguments. A .R file (which shall be called setup file in the following) is absolutely necessary.
If you have a setup file - such a file containing a VERY simple benchmarking setup that consists of two simple metric 2d datasets is included in the package and can be sourced with
library(bdlp)
source(system.file("dangl2014.R", package = "bdlp"))The file only contains one function which has to be of the same name as the file name - always authorYEAR. If several benchmarking setups are available for the same author in one year, additional indication of this fact by authorYEARa/b/c/etc. are permissible. This function can return two things; either a summary about the setup:
dangl2014(info=T)## $summary
## n k shape
## 1 50 2 spherical
## 2 40 2 spherical
##
## $reference
## [1] "Dangl R. (2014) A small simulation study. Journal of Simple Datasets 10(2), 1-10"or a metadata object of one of the two available datasets. This is done by merely providing the the set nr and results in the following object:
library(MASS)
meta <- dangl2014(setnr=1)
meta## An object of class "metadata.metric"
## Slot "standardization":
## [1] "NONE"
##
## Slot "clusters":
## $c1
## $c1$n
## [1] 25
##
## $c1$mu
## [1] 4 5
##
## $c1$Sigma
## [,1] [,2]
## [1,] 1 0
## [2,] 0 1
##
##
## $c2
## $c2$n
## [1] 25
##
## $c2$mu
## [1] -1 -2
##
## $c2$Sigma
## [,1] [,2]
## [1,] 1 0
## [2,] 0 1
##
##
##
## Slot "genfunc":
## function (n = 1, mu, Sigma, tol = 1e-06, empirical = FALSE, EISPACK = FALSE)
## {
## p <- length(mu)
## if (!all(dim(Sigma) == c(p, p)))
## stop("incompatible arguments")
## if (EISPACK)
## stop("'EISPACK' is no longer supported by R", domain = NA)
## eS <- eigen(Sigma, symmetric = TRUE)
## ev <- eS$values
## if (!all(ev >= -tol * abs(ev[1L])))
## stop("'Sigma' is not positive definite")
## X <- matrix(rnorm(p * n), n)
## if (empirical) {
## X <- scale(X, TRUE, FALSE)
## X <- X %*% svd(X, nu = 0)$v
## X <- scale(X, FALSE, TRUE)
## }
## X <- drop(mu) + eS$vectors %*% diag(sqrt(pmax(ev, 0)), p) %*%
## t(X)
## nm <- names(mu)
## if (is.null(nm) && !is.null(dn <- dimnames(Sigma)))
## nm <- dn[[1L]]
## dimnames(X) <- list(nm, NULL)
## if (n == 1)
## drop(X)
## else t(X)
## }
## <bytecode: 0x000000001c6af678>
## <environment: namespace:MASS>
##
## Slot "seedinfo":
## [[1]]
## [1] 100
##
## [[2]]
## [1] "3.3.3"
##
## [[3]]
## [1] "Mersenne-Twister" "Inversion"The object consists of several slots. Most importantly, the slot genfunc contains the random number generating function (in this case the default mvrnorm). The second important slot is clusters, which contains the parameters that are needed by genfunc to generate data. It is important to notice that the parameters in clusters are identical to the arguments needed by genfunc. Two other slots are quite crucial for processing the metadata object: metaseedinfo and seedinfo. The former sets the random number generator parameters for the purpose of calculating the metadata object. For example, if the cluster centers were to be chosen randomly and not in a fixed way like in the case at hand, the metaseedinfo parameters ensure reproducibility of a particular metadata object. The latter, seedinfo, is information that is sent along with the metadata object to the the function that generates the actual random numbers of the dataset. It can be practical to have these two sets of random number generator parameters - if not needed, they default to the same set of arguments. Therefore, if a metadata object has been created, one can generate the actual numbers. This can be done for one single data set from this particular metadata scenario by
library(MASS)
data <- generateData(meta)
head(data)## V1 V2
## 1 3.022824 6.146590
## 2 4.127758 6.030671
## 3 2.393572 5.210740
## 4 3.453221 4.794338
## 5 4.225362 6.570782
## 6 4.239723 4.162089If a different random number seed is needed this can be done by
meta <- dangl2014(1, seedinfo = list(120, "3.1.3", c("Mersenne-Twister", "Inversion")))
data <- generateData(meta)
head(data)## V1 V2
## 1 3.647198 4.677243
## 2 4.292669 4.307122
## 3 4.419130 4.704274
## 4 4.344384 3.938583
## 5 3.806678 6.125037
## 6 3.869850 4.494429This data can then be plotted with the commonly used plot functions. Yet still, it is also possible to catch a glimpse of the structure of the data by plotting a metadata object directly. For this purpose, an instance of the dataset is generated automatically, which saves a few steps in-between.
meta <- dangl2014(setnr=1)
plotMetadata(meta)
However, generally one may require for benchmarking purposes a large number of datasets drawn from one particular metadata scenario. This works as follows:
generateDatabase(name = system.file("dangl2014.R", package = "bdlp"), setnr = 1, draws = 50)Which creates an SQLite database in the current working directory that contains 50 datasets drawn from matadata object 1 of the setup name. Of course, metaseedinfo and seedinfo is also available here. The random number seed starts at a certain base value (default 100) and increments by 1 for each draw. Therefore draw number 1 uses seed 101, etc. If a different increment step is desired, one can set the argument increment to this value.