Description Usage Arguments Details Value References Examples
The function can test significance of (potentially large) groups of predictors in low- and high-dimensional generalized linear models. Outputs a p-value.
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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 p-value 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 high-dimensional 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 low-dimensional case, an unpenalized regression may be used.
The output is a single p-value.
Janková, J., Shah, R. D., Bühlmann, P. and Samworth, R. (2019) Goodness-of-fit testing in high-dimensional generalized linear models https://arxiv.org/abs/1908.03606
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