Description Usage Arguments Details Value References Examples
The function can test significance of (potentially large) groups of predictors in low and highdimensional generalized linear models. Outputs a pvalue.
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X 
Input matrix with 
y 
Response vector. 
fam 
Must be "gaussian", "binomial" or "poisson". 
G 
A vector with indices of variables,
whose significance we wish to ascertain, after controlling for variables in

B 
The number of bootstrap samples to approximate the distribution of
the test statistic. Note that the pvalue returned will always be
at least 
penalize 
If 
The function can test the significance of a set of variables in a generalized linear model,
whose indices are specified by G
.
penalize = TRUE
is needed for highdimensional settings where the number of variables
not in G
is larger than the number of observations. We then employ a penalized regression
to regress y
on to these variables implemented in cv.glmnet
from package glmnet
.
For the lowdimensional case, an unpenalized regression may be used.
The output is a single pvalue.
Janková, J., Shah, R. D., Bühlmann, P. and Samworth, R. (2019) Goodnessoffit testing in highdimensional generalized linear models https://arxiv.org/abs/1908.03606
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