GenerateToyData {CJIVE}

Generate 'Toy' Data

Description

Generates two Simulated Datasets that follow JIVE Model using binary subject scores

Usage

GenerateToyData(
  n,
  p1,
  p2,
  JntVarEx1,
  JntVarEx2,
  IndVarEx1,
  IndVarEx2,
  jnt_rank = 1,
  equal.eig = FALSE,
  ind_rank1 = 2,
  ind_rank2 = 2,
  SVD.plots = TRUE,
  Error = TRUE,
  print.cor = TRUE
)

Arguments

n

integer for sample size, i.e. number of subjects

p1

integer for number of features/variables in first data set

p2

integer for number of features/variables in second data set

JntVarEx1

numeric between (0,1) which describes proportion of variance in the first data set which is attributable to the joint signal

JntVarEx2

numeric between (0,1) which describes proportion of variance in the second data set which is attributable to the joint signal

IndVarEx1

numeric between (0,1) which describes proportion of variance in the first data set which is attributable to the individual signal

IndVarEx2

numeric between (0,1) which describes proportion of variance in the second data set which is attributable to the individual signal

jnt_rank

integer for rank of the joint signal, i.e., number of joint components

equal.eig

logical (TRUE/FALSE) for whether components should contribute equal variance to signal matrices - default is FALSE

ind_rank1

integer for rank of the individual signal in first data set, i.e., number of joint components

ind_rank2

integer for rank of the individual signal in second data set, i.e., number of joint components

SVD.plots

logical (TRUE/FALSE) for whether plots of singular values from signal should be produced - used to confirm number of components

Error

logical (TRUE/FALSE) final data sets should be noise contaminated - default is FALSE; use TRUE to obtain pure signal datasets

print.cor

logical (TRUE/FALSE) for whether to print matrix of correlations between subject scores)

Value

A 'list' object which contains 1) list of signal matrices which additively comprise the simulated data sets, i.e. joint, individual, and error matrices for each data set; 2) list of simulated data sets (each equal to the sum of the matrices in part 1); 3) list of joint subject scores and individual subject scores for each data set, and 4) lsit of joint and individual loadings for each data set

Examples

ToyDat = GenerateToyData(n = 200, p1 = 2000, p2 = 1000, JntVarEx1 = 0.05, JntVarEx2 = 0.05,
                           IndVarEx1 = 0.25, IndVarEx2 = 0.25, jnt_rank = 1, equal.eig = FALSE,
                           ind_rank1 = 2, ind_rank2 = 3, SVD.plots = TRUE, Error = TRUE,
                           print.cor = TRUE)