generateTestData_2D: Generate simulated 2D dataset for testing
In clusternomics: Integrative Clustering for Heterogeneous Biomedical Datasets

Description Usage Arguments Value Examples

Generate simple 2D dataset with two contexts, where the data are generated from Gaussian distributions. The generated output contains two datasets, where each dataset contains 4 global clusters, originating from two local clusters in each context.

1	generateTestData_2D(groupCounts, means, variances = NULL)

`groupCounts`	Number of data samples in each global cluster. It is assumed to be a vector of four elements: `c(c11, c21, c12, c22)` where `cij` is the number of samples coming from cluster i in context 1 and cluster j in context 2.
`means`	Means of the simulated clusters. It is assumed to be a vector of two elements: `c(m1, m2)` where `m1` is the mean of the first cluster in both contexts, and `m2` is the mean of the second cluster in both contexts. Because the data are two-dimensional, the mean is assumed to be the same in both dimensions.
`variances`	Optionally, it is possible to specify different variance for each of the clusters. The variance is assumed to be a vector of two elements: `c(v1, v2)` where `v1` is the variance of the first cluster in both contexts, and `v2` is the variance of the second cluster in both contexts. Because the data are two-dimensional, the variance is diagonal and the same in both dimensions.

Returns the simulated datasets together with true assignmets.

`data`	List of datasets for each context. This can be used as an input for the `contextCluster` function.
`groups`	True cluster assignments that were used to generate the data.

groupCounts <- c(50, 10, 40, 60)
means <- c(-1.5,1.5)
testData <- generateTestData_1D(groupCounts, means)
# Use the dataset as an input for the contextCluster function for testing
datasets <- testData$data