Description Usage Arguments Details Value References See Also Examples
View source: R/testhierarchy.R
Hierarchical testing based on multisample splitting.
1 2 3 4 5  test_hierarchy(x, y, dendr, clvar = NULL, family = c("gaussian",
"binomial"), B = 50, proportion.select = 1/6, standardize = FALSE,
alpha = 0.05, global.test = TRUE, agg.method = c("Tippett",
"Stouffer"), verbose = FALSE, sort.parallel = TRUE,
parallel = c("no", "multicore", "snow"), ncpus = 1L, cl = NULL)

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 
dendr 
the output of one of the functions

clvar 
a matrix or list of matrices of control variables. 
family 
a character string naming a family of the error distribution;
either 
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. 
alpha 
the significant level at which the FWER is controlled. 
global.test 
a logical value indicating whether the global test should be performed. 
agg.method 
a character string naming an aggregation method which
aggregates the pvalues over the different data sets for a given cluster;
either 
verbose 
logical value indicating whether the progress of the computation should be printed in the console. 
sort.parallel 
a logical indicating whether the values are sorted with respect to the size of the block. This can reduce the run time for parallel computation. 
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

The hierarchial testing requires the output of one of the functions
cluster_var
or cluster_position
as an input (argument dendr
).
The function first performs multisample splitting step.
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 going top down through the hierarchical tree. Testing only continues if at least one child of a given cluster is significant.
The multisample splitting step can be run in parallel across the
different sample splits where the argument B
corresponds to number
of sample splits. If the argument block
was supplied for the building
of the hierarchical tree (i.e. in the function call of either
cluster_var
or cluster_position
),
i.e. the second level of the hierarchical tree was given, the hierarchical
testing step can be run in parallel across the different blocks 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.
Note that if Tippett's aggregation method is applied for multiple data sets, then very small pvalues are set to machine precision. This is due to rounding in floating point arithmetic.
The returned value is an object of class "hierT"
,
consisting of two elements, the result of the multisample splitting step
"res.multisplit"
and the result of the hierarchical testing
"res.hierarchy"
.
The result of the multisample splitting step is a list with number of elements corresponding to the number of data sets. Each element (corresponding to a data set) contains a list with two matrices. 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.
The result of the hierarchical testing is a data frame of significant clusters with the following columns:
block 

p.value 
The pvalue of the significant cluster. 
significant.cluster 
The column names of the members of the significant cluster. 
There is a print
method for this class; see
print.hierT
.
Renaux, C. et al. (2018), Hierarchical inference for genomewide association studies: a view on methodology with software. (arXiv:1805.02988)
cluster_var
,
cluster_position
, and
compute_r2
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  n < 200
p < 500
library(MASS)
set.seed(3)
x < mvrnorm(n, mu = rep(0, p), Sigma = diag(p))
colnames(x) < paste0("Var", 1:p)
beta < rep(0, p)
beta[c(5, 20, 46)] < 1
y < x %*% beta + rnorm(n)
dendr1 < cluster_var(x = x)
set.seed(68)
sign.clusters1 < test_hierarchy(x = x, y = y, dendr = dendr1,
family = "gaussian")
## With block
# The column names of the data frame block are optional.
block < data.frame("var.name" = paste0("Var", 1:p),
"block" = rep(c(1, 2), each = p/2),
stringsAsFactors = FALSE)
dendr2 < cluster_var(x = x, block = block)
set.seed(23)
sign.clusters2 < test_hierarchy(x = x, y = y, dendr = dendr2,
family = "gaussian")
# Access part of the object
sign.clusters2$res.hierarchy[, "block"]
sign.clusters2$res.hierarchy[, "p.value"]
# Column names or variable names of the significant cluster in the first row.
sign.clusters2$res.hierarchy[[1, "significant.cluster"]]

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.