BootRWCor: Bootstrap multiple testing method of Romano & Wolf (2005) for...

In TestCor: FWER and FDR Controlling Procedures for Multiple Correlation Tests

Description

Multiple testing method based on the evaluation of quantile by bootstrap in the initial dataset (Romano & Wolf (2005)).

Usage

BootRWCor(
  data,
  alpha = 0.05,
  stat_test = "empirical",
  Nboot = 1000,
  vect = FALSE,
  logical = FALSE,
  arr.ind = FALSE
)

Arguments

data	matrix of observations
alpha	level of multiple testing (used if logical=TRUE)
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 Monte-Carlo quantile evaluation
vect	if TRUE returns a vector of adjusted p-values, corresponding to vectorize(cor(data)); if FALSE, returns an array containing the adjusted p-values for each entry of the correlation matrix
logical	if TRUE, returns either a vector or a matrix where each element is equal to TRUE if the corresponding null hypothesis is rejected, and to FALSE if it is not rejected
arr.ind	if TRUE, returns the indexes of the significant correlations, with respect to level alpha

Value

Returns

• the adjusted p-values, 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.

References

Romano, J. P., & Wolf, M. (2005). Exact and approximate stepdown methods for multiple hypothesis testing. Journal of the American Statistical Association, 100(469), 94-108.

Roux, M. (2018). Graph inference by multiple testing with application to Neuroimaging, Ph.D., Université Grenoble Alpes, France, https://tel.archives-ouvertes.fr/tel-01971574v1.

See Also

ApplyFwerCor, BootRWCor_SD

Examples

 
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)
# adjusted p-values
res <- BootRWCor(data,stat_test='empirical',Nboot=1000)
round(res,2)
# significant correlations with level alpha:
alpha <- 0.05
whichCor(res<alpha)
# directly
BootRWCor(data,alpha,stat_test='empirical',Nboot=1000,arr.ind=TRUE)

Example output

      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
 [1,]    0    1    0  1.0    1 1.00    1    1 1.00  0.95
 [2,]    0    0    1  0.8    1 0.25    1    1 0.96  1.00
 [3,]    0    0    0  1.0    1 1.00    1    1 1.00  1.00
 [4,]    0    0    0  0.0    1 1.00    1    1 1.00  0.47
 [5,]    0    0    0  0.0    0 1.00    1    1 1.00  1.00
 [6,]    0    0    0  0.0    0 0.00    1    1 1.00  1.00
 [7,]    0    0    0  0.0    0 0.00    0    1 1.00  1.00
 [8,]    0    0    0  0.0    0 0.00    0    0 1.00  1.00
 [9,]    0    0    0  0.0    0 0.00    0    0 0.00  1.00
[10,]    0    0    0  0.0    0 0.00    0    0 0.00  0.00
row col 
  1   3 
row col 
  1   3 

TestCor documentation built on Oct. 23, 2020, 5:31 p.m.