Description Usage Arguments Value Author(s) Examples
Rescales and transforms the X matrix according to the desired parameters, and sets all the options required by the test.
1 2 3 |
X |
input matrix, of dimension n x p; each row is an observation vector. |
family |
response type (see above). Default is |
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 |
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= |
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 Λ. |
an object containing all the variables corresponding to the rescaling and test options.
Sylvain Sardy and Jairo Diaz
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # 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)
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