Assess Significance of SiMLR Call — simlr.perm • ANTsR

This function performs permutation tests to assess the significance of a SiMLR analysis. For more detail on input parameters, see the original function.

simlr.perm(
  voxmats,
  smoothingMatrices,
  iterations = 10,
  sparsenessQuantiles,
  positivities,
  initialUMatrix,
  mixAlg = c("svd", "ica", "avg", "rrpca-l", "rrpca-s", "pca", "stochastic"),
  orthogonalize = FALSE,
  repeatedMeasures = NA,
  lineSearchRange = c(-1e+10, 1e+10),
  lineSearchTolerance = 1e-08,
  randomSeed,
  constraint = c("none", "Grassmann", "Stiefel"),
  energyType = c("cca", "regression", "normalized", "ucca", "lowRank",
    "lowRankRegression"),
  vmats,
  connectors = NULL,
  optimizationStyle = c("lineSearch", "mixed", "greedy"),
  scale = c("centerAndScale", "sqrtnp", "np", "center", "norm", "none", "impute",
    "eigenvalue", "robust"),
  expBeta = 0,
  jointInitialization = TRUE,
  sparsenessAlg = NA,
  verbose = FALSE,
  nperms = 50,
  FUN = "mean"
)

Arguments

voxmats: A list of voxel matrices.
smoothingMatrices: A list of smoothing matrices.
iterations: Number of iterations. Default is 10.
sparsenessQuantiles: A vector of sparseness quantiles.
positivities: A vector of positivity constraints.
initialUMatrix: Initial U matrix for the algorithm.
mixAlg: The mixing algorithm to use. Default is 'svd'.
orthogonalize: Logical indicating whether to orthogonalize. Default is FALSE.
repeatedMeasures: Repeated measures data. Default is NA.
lineSearchRange: Range for line search. Default is c(-1e+10, 1e+10).
lineSearchTolerance: Tolerance for line search. Default is 1e-08.
randomSeed: Seed for random number generation.
constraint: The constraint type. Default is 'none'.
energyType: The energy type. Default is 'cca'.
vmats: List of V matrices - optional initialization matrices
connectors: List of connectors. Default is NULL.
optimizationStyle: The optimization style. Default is 'lineSearch'.
scale: Scaling method. Default is 'centerAndScale'.
expBeta: Exponential beta value. Default is 0.
jointInitialization: Logical indicating joint initialization. Default is TRUE.
sparsenessAlg: Sparseness algorithm. Default is NA.
verbose: Logical indicating whether to print verbose output. Default is FALSE. values > 1 lead to more verbosity
nperms: Number of permutations for significance testing. Default is 50.
FUN: function for summarizing variance explained

Value

A data frame containing p-values for each permutation.