qut (version 2.2)

processX: Process X matrix

Description

Rescales and transforms the X matrix according to the desired parameters, and sets all the options required by the test.

Usage

processX(X, family = gaussian, alpha, intercept = TRUE, group.sizes = rep(1, ncol(X)), 
A = ncol(X), LAD = FALSE, composite = TRUE, 
M = min(10000, max(1000, 1e+10/nrow(X)/ncol(X))))

Arguments

X

input matrix, of dimension n x p; each row is an observation vector.

family

response type (see above). Default is gaussian.

alpha

alpha for quantile rescaling; if alpha=0, then no rescaling.

intercept

should intercept(s) be fitted (default=TRUE) or set to zero (FALSE).

group.sizes

the vector of group sizes for affine group lasso. The number of elements is L and sum(group.sizes) should be equal to P. If L==P, then the lasso test is employed, otherwise group lasso. Default is no groups, so rep(1,ncol(X)).

A

if A is a matrix it tests A beta = c. If A is a vector, then it gives the indexes of the parameters to be tested. Used if family=gaussian. Default is to test beta=0, so A=ncol(X).

LAD

set TRUE if LAD lasso test. Default is FALSE

composite

set TRUE if composite test (O & +). Default is TRUE

M

number of Monte Carlo Simulations to estimate the distribution \(\Lambda\).

Value

an object containing all the variables corresponding to the rescaling and test options.

Examples

# NOT RUN {
	# Test H0:beta=0
		P=200
		N=20
		s=1
		A=P
		alpha=0.05
		X=matrix(rnorm(N*P),N,P)
		outrescale=processX(X,gaussian,alpha)
		M=100 #Leave the default or select higher value for better level.
	
	#when H0 is not rejected
		beta_scal=0
		beta=c(rep(beta_scal, s), rep(0, P-s)) 
		y=X%*%beta+rnorm(N)
		out=affinelassotest(y,X,gaussian,alpha,M=M,outrescale=outrescale)
		print(out$rejectH0)
	
	#when H0 is rejected
		beta_scal=10
		beta=c(rep(beta_scal, s), rep(0, P-s)) 
		y=X%*%beta+rnorm(N)
		out=affinelassotest(y,X,gaussian,alpha,M=M,outrescale=outrescale)
		print(out$rejectH0)
# }