bootstrapBackShift {backShift} | R Documentation |
Computes a simple model-based bootstrap confidence interval for success of joint diagonalization procedure. The model-based bootstrap approach assumes normally distributed error terms; the parameters of the noise distribution are estimated with maximum likelihood.
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 off-diagonal elements after joint diagnolization procedure in each of the bootstrap samples.
sumOffDiagsBackShift
Sum of off-diagonal 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.