LCTboot: Bootstrap procedure LCT-B proposed by Cai & Liu (2016) for...
In TestCor: FWER and FDR Controlling Procedures for Multiple Correlation Tests

Description Usage Arguments Value References See Also Examples

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

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

`data`	matrix of observations
`alpha`	level of multiple testing
`stat_test`	'empirical' √{n}abs(corr)* 'fisher' √{n-3}1/2\log( (1+corr)/(1-corr) ) 'student' √{n-2}abs(corr)/√(1-corr^2)* '2nd.order' √{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

Returns

logicals, equal to TRUE if the corresponding element of the statistic vector is rejected, as a vector or a matrix depending of the value of vect,
an array containing indexes \lbrace(i,j),\,i<j\rbrace for which correlation between variables i and j is significant, if arr.ind=TRUE.

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

ApplyFdrCor, LCTNorm

 
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)