Description Usage Arguments Details Value References See Also Examples
The data is randomly split in two halves w.r.t. the observations and
variable selection using Lasso is performed on one half. Whereas the second
half and the selected variables are later used for testing by the function
test_only_hierarchy
. This is repeated multiple times.
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x 
a matrix or list of matrices for multiple data sets. The matrix or matrices have to be of type numeric and are required to have column names / variable names. The rows and the columns represent the observations and the variables, respectively. 
y 
a vector, a matrix with one column, or list of the aforementioned
objects for multiple data sets. The vector, vectors, matrix, or matrices
have to be of type numeric. For 
clvar 
a matrix or list of matrices of control variables. 
B 
number of sample splits. 
proportion.select 
proportion of variables to be selected by Lasso in the multisample splitting step. 
standardize 
a logical value indicating whether the variables should be standardized. 
family 
a character string naming a family of the error distribution;
either 
parallel 
type of parallel computation to be used. See the 'Details' section. 
ncpus 
number of processes to be run in parallel. 
cl 
an optional parallel or snow cluster used if

check.input 
a logical value indicating whether the function should
check the input. This argument is used to call

A given data with nobs
is randomly split in two halves w.r.t.
the observations and nobs * proportion.select
variables are selected
using Lasso (implemented in glmnet
) on one half.
Control variables are not penalized if supplied
using the argument clvar
. This is repeated B
times for each
data set if multiple data sets are supplied. Those splits (i.e. second
halves of observations) and corresponding selected variables are used to
perform hierarchical testing by the function
test_only_hierarchy
.
The multisample split step can be run in parallel across the different
sample splits (B
corresponds to number of sample splits) by
specifying the arguments parallel
and ncpus
.
There is an optional argument cl
if parallel = "snow"
.
There are three possibilities to set the argument parallel
:
parallel = "no"
for serial evaluation (default),
parallel = "multicore"
for parallel evaluation
using forking, and parallel = "snow"
for parallel evaluation
using a parallel socket cluster. It is recommended to select
RNGkind("L'EcuyerCMRG")
and set a seed to ensure that
the parallel computing of the package hierinf
is reproducible.
This way each processor gets a different substream of the pseudo random
number generator stream which makes the results reproducible if the arguments
(as sort.parallel
and ncpus
) remain unchanged. See the vignette
or the reference for more details.
The returned value is an object of class "hierM"
, consisting
of a list with number of elements corresponding to the number of data sets.
Each element (corresponding to a data set
The first matrix
contains the indices of the second half of variables (which were not used
to select the variables). The second matrix
contains the column names / variable names of the selected variables.
Renaux, C. et al. (2018), Hierarchical inference for genomewide association studies: a view on methodology with software. (arXiv:1805.02988)
Meinshausen, N., Meier, L. and Buhlmann, P. (2009), Pvalues for highdimensional regression, Journal of the American Statistical Association 104, 16711681.
cluster_var
,
cluster_position
,
test_only_hierarchy
,
test_hierarchy
, and
compute_r2
.
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