TestCor (version 0.0.2.2)

LCTboot: Bootstrap procedure LCT-B proposed by Cai & Liu (2016) for correlation testing.

Description

Bootstrap procedure LCT-B proposed by Cai & Liu (2016) for correlation testing.

Usage

LCTboot(
  data,
  alpha = 0.05,
  stat_test = "2nd.order",
  Nboot = 100,
  vect = FALSE,
  arr.ind = FALSE
)

Arguments

data

matrix of observations

alpha

level of multiple testing

stat_test

'empirical'

\(\sqrt{n}*abs(corr)\)

'fisher'

\(\sqrt{n-3}*1/2*\log( (1+corr)/(1-corr) )\)

'student'

\(\sqrt{n-2}*abs(corr)/\sqrt(1-corr^2)\)

'2nd.order'

\(\sqrt{n}*mean(Y)/sd(Y)\) with \(Y=(X_i-mean(X_i))(X_j-mean(X_j))\)

Nboot

number of iterations for bootstrap quantile evaluation

vect

if TRUE returns a vector of TRUE/FALSE values, corresponding to vectorize(cor(data)); if FALSE, returns an array containing TRUE/FALSE values for each entry of the correlation matrix

arr.ind

if TRUE, returns the indexes of the significant correlations, with respect to level alpha

Value

Returns

• an array containing indexes \(\lbrace(i,j),\,i<j\rbrace\) for which correlation between variables \(i\) and \(j\) is significant, if arr.ind=TRUE.

References

Cai, T. T., & Liu, W. (2016). Large-scale multiple testing of correlations. Journal of the American Statistical Association, 111(513), 229-240.

See Also

ApplyFdrCor, LCTNorm

Examples

# NOT RUN {
 
n <- 100
p <- 10
corr_theo <- diag(1,p)
corr_theo[1,3] <- 0.5
corr_theo[3,1] <- 0.5
data <- MASS::mvrnorm(n,rep(0,p),corr_theo)
alpha <- 0.05
# significant correlations:
LCTboot(data,alpha,stat_test='empirical',Nboot=100,arr.ind=TRUE)
# }