Description Usage Arguments Value
Computes a simple modelbased bootstrap confidence interval for success of joint diagonalization procedure. The modelbased bootstrap approach assumes normally distributed error terms; the parameters of the noise distribution are estimated with maximum likelihood.
1 2  bootstrapBackShift(Ahat, X, ExpInd, nrep, alpha = 0.05, covariance = TRUE,
baseInd = 1, tolerance = 0.001, verbose = FALSE)

Ahat 
Estimated connectivity matrix returned by 
X 
A (nxp)dimensional matrix (or data frame) with n observations of p variables. 
ExpInd 
Indicator of the experiment or the intervention type an observation belongs to. A numeric vector of length n. Has to contain at least three different unique values. 
nrep 
Number of bootstrap samples. 
alpha 
Significance level for confidence interval. 
covariance 
A boolean variable. If 
baseInd 
Index for baseline environment against which the intervention variances are measured. Defaults to 1. 
tolerance 
Precision parameter for 
verbose 
If 
A list with the following elements:
bootsSumOffDiags
Vector of length nrep
with sum of offdiagonal elements after joint diagnolization procedure in each of the bootstrap samples.
sumOffDiagsBackShift
Sum of offdiagonal elements after joint diagnolization procedure in original estimation.
jointDiagSuccess
TRUE
if sumOffDiagsBackShift
lies
within bootstrap confidence interval.
lower
Lower bound of bootstrap confidence interval.
upper
Upper bound of bootstrap confidence interval.
lowerBasic
alpha/2
quantile of empirical bootstrap distribution.
upperBasic
1  alpha/2
quantile of empirical bootstrap distribution.
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